diff --git a/notebooks/figures/fig_matmu_intro.svg b/notebooks/figures/fig_matmu_intro.svg index 99a4ccd..33e31dd 100644 --- a/notebooks/figures/fig_matmu_intro.svg +++ b/notebooks/figures/fig_matmu_intro.svg @@ -14,7 +14,7 @@ id="svg8" inkscape:version="0.92.5 (2060ec1f9f, 2020-04-08)" sodipodi:docname="fig_matmu_intro.svg" - inkscape:export-filename="/home/francesc/repos/XM_40017/notebooks/figures/fig_matmul_intro_algs.png" + inkscape:export-filename="/home/francesc/repos/XM_40017/notebooks/figures/fig_matmul_intro_algs_1.png" inkscape:export-xdpi="200" inkscape:export-ydpi="200"> + transform="translate(24.814217,532.52974)"> x + + + + + + + + + + + + + + + + + + + + + proc 1 + worker + + + + + A + B + C + x x x x x x x x x x x x x x x x + + + + x x x x x x x x x x x x x x x x + + + + x x x x x x x x x x x x x x x x + + + + = * + + x x x x x x x x x x x x x x x x + + + x x x x x x x x + + + x x x x x x x x diff --git a/notebooks/matrix_matrix.ipynb b/notebooks/matrix_matrix.ipynb index 3c2a812..57bc0f4 100644 --- a/notebooks/matrix_matrix.ipynb +++ b/notebooks/matrix_matrix.ipynb @@ -12,7 +12,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, + "id": "b4fef0c2", + "metadata": {}, + "outputs": [], + "source": [ + "] add BenchmarkTools" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "id": "2f8ba040", "metadata": {}, "outputs": [ @@ -22,12 +32,16 @@ "alg_2_deps_check (generic function with 1 method)" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "using Distributed\n", + "if procs() == workers()\n", + " addprocs(4)\n", + "end\n", "function answer_checker(answer,solution)\n", " if answer == solution\n", " \"🥳 Well done! \"\n", @@ -35,6 +49,7 @@ " \"It's not correct. Keep trying! 💪\"\n", " end |> println\n", "end\n", + "alg_1_deps_check(answer) = answer_checker(answer,\"b\")\n", "alg_2_deps_check(answer) = answer_checker(answer,\"d\")" ] }, @@ -57,7 +72,7 @@ "\n", "- Parallelize a simple algorithm\n", "- Study the performance of different parallelization strategies\n", - "- Implement them using Julia Distributed\n", + "- Implement them using Julia\n", "- Learn concepts such as communication overhead and parallel speedup. " ] }, @@ -68,7 +83,7 @@ "source": [ "## Problem Statement\n", "\n", - "Let us consider the (dense) matrix-matrix product `C=A*B`. Our goal is to compute the product in parallel using more than one process (distributed implementation)." + "Let us consider the (dense) matrix-matrix product `C=A*B`." ] }, { @@ -86,6 +101,20 @@ "\n" ] }, + { + "cell_type": "markdown", + "id": "4cb6e98f", + "metadata": {}, + "source": [ + "### Goals\n", + "\n", + "We want to\n", + "\n", + "- compute the product in parallel using more than one process (distributed implementation)\n", + "- study the performance of different parallelization alternatives\n", + "- implement the algorithms using Julia\n" + ] + }, { "cell_type": "markdown", "id": "495ef679", @@ -138,20 +167,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "af8dfb37", "metadata": {}, "outputs": [], "source": [ - "function matmul_seq!(C,A,B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", + "@everywhere function matmul_seq!(C,A,B)\n", + " m = size(C,1)\n", + " n = size(C,2)\n", + " l = size(A,2)\n", + " @assert size(A,1) == m\n", + " @assert size(B,2) == n\n", + " @assert size(B,1) == l\n", " z = zero(eltype(C))\n", - " for j in 1:l\n", + " for j in 1:n\n", " for i in 1:m\n", " Cij = z\n", - " for k in 1:n\n", + " for k in 1:l\n", " @inbounds Cij += A[i,k]*B[k,j]\n", " end\n", " C[i,j] = Cij\n", @@ -168,7 +200,26 @@ "source": [ "
\n", "Note: The matrix-matrix multiplication naively implemented with 3 nested loops as above is known to be very inefficient (memory bound). Libraries such as BLAS provide much more efficient implementations, which are the ones used in practice (e.g., by the `*` operator in Julia). We consider, our hand-written implementation as a simple way of expressing the algorithm we are interested in.\n", - "
" + "\n", + "\n", + "Run the following cell to compare the performance of our hand-written function with respect to the built in function `mul!`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "899235d1", + "metadata": {}, + "outputs": [], + "source": [ + "using LinearAlgebra\n", + "using BenchmarkTools\n", + "N = 1000\n", + "A = rand(N,N)\n", + "B = rand(N,N)\n", + "C = rand(N,N)\n", + "@btime matmul_seq!(C,A,B)\n", + "@btime mul!(C,A,B);" ] }, { @@ -181,17 +232,17 @@ "Look at the three nested loops in the sequential implementation:\n", "\n", "```julia\n", - "for j in 1:l\n", + "for j in 1:n\n", " for i in 1:m\n", " Cij = z\n", - " for k in 1:n\n", + " for k in 1:l\n", " @inbounds Cij += A[i,k]*B[k,j]\n", " end\n", " C[i,j] = Cij\n", " end\n", "end\n", "```\n", - "- Loops over `i` and `j` are trivially parallelizable (i.e. the entries in the result matrix C can be computed in parallel).\n", + "- Loops over `i` and `j` are trivially parallelizable.\n", "- The loop over `k` can be parallelized but it requires a reduction." ] }, @@ -206,7 +257,7 @@ "\n", "- Algorithm 1: each worker computes a single entry of C\n", "- Algorithm 2: each worker computes a single row of C\n", - "- Algorithm 3: each worker computes a block of rows of C" + "- Algorithm 3: each worker computes a block rows of C" ] }, { @@ -239,7 +290,9 @@ "source": [ "### Data dependencies\n", "\n", - "Moving data through the network is expensive. For this reasons, one of the key points in the implementation of a distributed algorithm is to determine which is the minimum data needed by a worker to perform its computations. For algorithm 1, we need to answer this question: which entries of A and B are strictly needed to compute entry C[i,j]?" + "Moving data through the network is expensive and reducing data movement is one of the key points in distributed algorithm. To this end, we determine which is the minimum data needed by a worker to perform its computations.\n", + "\n", + "In algorithm 1, each worker computes only an entry of the result matrix C." ] }, { @@ -259,25 +312,28 @@ }, { "cell_type": "markdown", - "id": "794602ae", + "id": "28c04679", "metadata": {}, "source": [ - "It is clear that in order to compute C[i,j] we need the row A[i,:] and the column B[:,j]. " + "
\n", + "Question: Which are the data dependencies of the computations done by the worker in charge of computing entry C[i,j] ? \n", + "
\n", + "\n", + " a) column A[:,i] and row B[j,:]\n", + " b) row A[i,:] and column B[:,j]\n", + " c) the whole matrices A and B\n", + " d) row A[i,:] and the whole matrix B" ] }, { - "attachments": { - "fig_matmul_intro_algs_1.png": { - "image/png": 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9HKBFixaTkUGIOPT9EJgCjARWYkQqxKAncHac+pTuqoAn67nvtpp/vPvuu22AV4BT4tGpBjhSaz0mMzPz4g8//PDcgQMHrk9xf8Sh60/Av+u5by6hSJLwC6DNwLUN6dGiRYt+q5RKh0FIuB9u3779VmByqjsiDlm3NWDfQchApC5/BO6p787vv/9+DjA8ft0RIq0N++CDDwpPO+20T6n/OX0wh89ApJwGXvsAZGdnzyH1g5BwxymlngLOS3VHxCGrsAH7nkNoIBLX3A2l1LB4Hi+O8lLdASFEamRkZAwEGqW6H0IkSVZmZub3U92Jw8nixYuPAy5KdT9MSqkfL168+PRU90OI2sT7TmzExNLX17/Oe5ve87S1a9SOYUcPQ6nYindV62oe/d+j7Kra5Wk/r/N5DGw30G+Xnu+//35OXOIli7kZTR+j9QMc5sZ8rIm0p4ISzAFhBvczjq/r38k6OGQCE3GT9cItxOGVehyvH3bSXyVQgMMunz3iI96/v2J+g+ZnRut6HBxA17ebIrUCgUBbrf3/fCoYpMsjj5C5y/s23frjH7PjtNNifq2mX35J++ef97TpzEzW3HAD1U2bxny8aGVu20aXGTNQxs+5YcgQ9nXvHvPxWr35Ji3fecfTVtm2LWvz8iDG7+tYNP3qK9o/95ynrSG/vw7z59NkqTf1anffvmz+9a8b1M+6tHrjDVq++66nrb6/v3q+R9vF9CKiQbTWZ0fa9uGHObzySmtPW/PmVYwcuZaMjNhOK1rDrFld+O67LE/7GWds5+yzt0XYR59Nw/OAAX0dYF5gfQ5qWj2O1Rw3SiXL2DAT1Mf16l50r6uAIsLyekJeBPV/9TjeMbj50OGCwDhQm+vTwyhfN86/P30ebqhUuO3AaFBV9elhLOI9EGkWacOAtgN4cMmDrNmzxtPeIqsFl/a8NKYXmfnlTJ749glP21EtjuL2vub74YDGjRu3ABo+EFG8gmYiEH5WHEoxKyji1ZiOVMEc4FdG+4KEDkIAHKpw+BA7JyiXCZzCWFZEfax7acFengGOMbbcntBBCMBoNuGwDSjwtFfzfR7gNG4j+uohDsegeRxoEdYaRPEzZBByUAsGgyrSzQ4dCLDr5JM56vbb3bN8SOtXXmHJU09R0aVL1K8T2LOHnqNH03iF9+Oz+uabEzoIAahq1Yrqli3pNNd7P6TZp5/yv8cfJ9go+gmhRitX0mPcODL27DnQGAjw9cMPJ3QQArDn6KPJ3LyZVm+95WnP3LaNpRMnxnSslv/6F91+/3vP37W6RQvWX311PLpaq50DB3LEtGk0XrnS017dogUbLosterlzaSmdnvCe7/b27s2q22qNCJXS/MkVceB38sm7eOihI/jyS+93QFaWZsSItTG9yDPPdKSszHsN3blzBTfcsCbCHqCU6hDTi0T2EnAv0NbbrFeD+mOMx5qOXa31H9Q/FzBKSoN+HbeaYkbYhstBDwS1JPpj6UbAfOxwvEmJHYQAqF2glwH3Gxt+DLo/qBgqcOquuHmu5nv4smQMQiDOoVm1aZHVgkn9JpEV8A7gHlryEJ9u/TTq4/x78795/JvHPW1NMpowuf9kGgWSEH1RyOcobjJaA2iexomhcpLD3diDkG+A5IS3OTyHXXawNVU8S6k1yo5sLzOxByEv4DC1gT2M1jjcSjHhTmAHU6I+gkNjYAHeQQjAeIp4uWHdE+lu+1lnsTHXezMoY8cOeo0ejaqK/nv4yEmTrEHI9jPOYMOQIXHpZ13WDh/OzlNP9bQ1+eYbuk6N/qMYqKigV0GBdxACrLvuunrNEMVMKVY4DhWdvF+lrV96iXZ//nPUh8nesIEejuMZhKAUywsLKY9hcFlf1U2bsuyee9DZ2Z72I6ZNo9mn0Z/vWixaRKd53nzzYJMmLJ08mWDjxnHpq2i42pYoyM4OMnnyUpo1q/a0z5vXGTd9LTpLljTj4YeP8LRlZmomTlxKTk7k76lgMBinQalahVsNzLwxNwd0z+iPo6/DHoRsAK4BFWxID6Oj3gQmGI3NgAWgY7ljNBV7EPIB7oxLMjwAmF+KvXGrTUZJZ+I/CHkU1DMN6Vwskrq+R59WfbjxuBs9bVW6itEfjWZHZd2TFVsqtjD247EE8b5XR504ih7Ne8Szq7UrYg52lY2OwNMs8Iyy/ZVwGlBitJYDl+DEYdYmWm24DbciUbjTWGf1zZ/D9dgV0lbj/2WVGA5VwKW4xRbCjaSYaKfaHsUOK3yTPoxvaPfEwWH1Lbew59hjPW3NPvuMLjNmRLV/++efp82LL3raKjp0YHlJScJnEWroQIDlxcVUtfIWcGv/3HO0+cc/ojpGt/vuo+lXX3nadvXrx7phyUv/q8rJYVlJCTrD+1Xa7d57aWL0zY+qqqLnqFFkbvfeFNw4eDDbzkle4bQ9xx3H6hu95ztVVUWv0aPJ3FH313zWli30HDsWFfSe71YWFLCvZwzXfSLlunUrZ8wY702KYBAKC3uyaVPd9/127sygoKAnlZXe75KbblrNCSckc3UC9XfgIaOxFfAs6GyfHQy6L2CGcgWBy0Els7pXCVhh6H2xf7YI9O+AEUbjVuASUJUN7Vx0lMZdx8OMYMkFHW1xlgm4lfbC/Re4s4Gdi0myFxrk0h6Xcnansz1t6/euZ8In5gDVK6iDjP1oLN+VexeQvbDbhfziiF/Eu5t1a8YI4Auj9Vy+4K5a95tEa4I8ix3bdxuONShIrJtCgx87ZO1uiq3ZGq8STsCeFnQHBY41KEgsJ8LgRzMDp45a7sVcgl0xZRNwGYOo9tlDHIJ0djZLJ0+murl3XcpOTzxhhQmZmnz7LV0feMB7vAiDgkSLNPg5cuJEa7bG1Prll2n3f94w6arWrVk6YQI6kNxTxa5+/ViX560xEmm2xnTEww/T3Jh18BsUJIPf4Cd7/Xp6FBV5Z2tMwSA9xo0ja7P3q/S7X/2K7375y0R0VSTY+edv5cILvX/PLVsyGTOmJ3VNWowf34O1a70RHz/84XYuvTQlazbfBbxntJ0KTKp9Nx2adcCcdSgGFUtYexyoIDAEdx2YcENB1zGFrXsDs81G3Bmd6MPa40KFBj+Yg58HQddRwU3/DHvAsQsYBCqpi2ImfSCilKLwpEK6NPVOj7+2/jUWLo+8jEnZ12V8uPlDT1uvFr246/jar/sT5k5245Ze9f7BNCU41gizhqKcecCRRvtzOP6rsyac4xsOptDMYQJH+O2CQ3OCLMBesHIcjhUmlRwOL+BOVYZrCTzLtAgVkxyOQjPLaNUorsMhtuBdcdAr79qVFWPHehu15kjHIXu9/826wN699CooIFDuXabIL0wqWbafcQYbrvBGPgT27HH7WVHhu0+jVas4coJxM0gplo8bR2WHeIWYx2bdNdew43RvwZ/GK1fSbUrkqMuW77xDxz/8wdMWjBAmlRQRwsFa/utfdFgY+XzXefZscj74wNO2r3t3Vt2Z1BuVIs4KClZxzDHeS4aPPmrB7NmRo7oXLOjAa695b2h07FhBcfHyZE22GlQlbnnnLcaGW0HXVjnsEbAK/bxJA0qzN4zagBvRYd5wnAH6WJ8dAJ0FPIVd6OcBUNHHjsaV+gC7jG4j3FCzCLF/uhPuGkPmGGAEqP/Fu4d1SfpABCAnK8c3X2Tqkqn8b7v9O1j83WLmfDXH05YdyGbCKRNonJHCOFmHz1DcarS6MXeOT/JaMbcBZrmWldgVp5LLYQF2XGF7qvhDhFCzGYD5QX0NuC8R3YtBAfCu0TaALT5ryBzICzE/qJMpavBqxOIgtfW889h08cWetswdO+g5bhyq2p4g6z5pEo2XLfO07RwwgA1JSIiuzZqRI9l1kjfasMnXX3PENLvAjaqooNeoUWTs9oZ4rL/ySrafdVZC+1mrQIBlJSVUtm/vaW7717/S9m9/s56evXGj70zDitGj61U5LF6qW7Rg2eTJ6Czv+a7rgw/S9AtzUh1afPQRnWd7b7gGa2bsElz0QCRWTb5I06be75LZs7vw4Yf2NePXXzfhoYe89wMzMjQTJy6jZcuk5BFHoFYCV+ONQlDAXNA97OfrwaHnh9uImxCdwsgD9TpuAn645rgX8eaNVnCjQMxyyP8GRiegc7G4F6zrlqPAutEK6ADwNFg5zbNBPZWIztUlJQMRgL6t+jLie94Qu4pgBaM+GsXuqgMnxC3lWxjz0RgrL6TghAKOyTFzpFOgiFLcP2q4rsAThFcuKeHUULWtcJUEGIxj3VlIhZuA/xhtZ7HEGGkXMwx3SjPcBuByHJKQaFYLhyoyGQx8Z2y5mWJ+Y7T5J5p1TlqimUhTq++4gz3f+56nrfnHH9N5lvc7vd2f/0zbF17wtFW2acOyFIQymXRGBssmTqSqpffGXYf582n12muetm5Tp9L0f94bQLv79mXtCDMEOvmq2rRxZzOM32f3e+/1DABVdTU9R48mc5u3hOmm3/2OLT8zK3In3+4+fVgzcqSnbf8AMKwsb+bWrfQcM8bKC1l1113sPSYNzneiwbp338edd67ytAWDMHZsD09Z3j17AhQU9KK83PveHzlyLSedlNiClNFRf8XNrwzXGpgfmjkI0UdjXxAHgStApUPkQSHujdRwPqHn+gLAjO/chpsX4j/VnDRKA9cBZvm0S0LFAcIVA+cabZ8DNyeoc3VK6dnyil5X8KOOP/K0rdq9igmfuiECQR2k8D+FbC73xlWe3+V8Lux2YdL6GYXhwJdG289DMyDg0CqUF2LGBtxFoRVrmRoO+8hgELDT2DKWYn4CQAl90TxobA+iuBzHirVMjbGswu9OjWYu43EzPIvJxV5peyuZXEK+FWspDjP77z4381Yj7zxvHjnvu+X4myxdaocIBQIsnzCBynbpsYRDRceOLC8utvJFeowfT6O17vm/9auv0t4IEarOyWHppEnozHhXd6+fnf36sX7oUE/b/pC4ffsA6DJ9Os3/472Psrd3b1bfak5Yp86Gyy9n24+857tGq1cfCIkLBulZWEjWpk2e52z9yU/YfFHarZUnGuBXv/qOCy7w3i/bsiXLky8yadKRrFjhjfg444wdDBmSHqfakDuAxUbbaVBT6EVHqkg5CdRLie5cdFQ1cCV2wZvrQYeK8ehuwGPYZbGvA7WMtKDc3FY3VzfcI6BD0+P6HGCUsT2UZqBqT75LoJQORJRSFJ1cROcm3rrYL699mT+t/BPzvpnH+5u86/B0a9aNMSeOSWY36+bUJPhY+SKTKeEHwBzALHPyd5xoKzQkibt+ibkKfQDNUzgcFcoL8cYGKEpiXD8l8Rz+hl2ZoxXVzGc8x6GtMDSN4pqY1k8Rh7Tybt1YMcb4ngldKDZas4aeY8bsvwiusW7oUHYM9F1QNWW2//CHbLjUWzwuY+dOehYU0GjlSrrfY4Rnh3IaYlk/JRnW+vxum3z7Ld1+/3ty3n2XTk96ixjuL3Ebw/opCacUy4uKrN9t61deof0f/0inxx4j5z3vfanybt3svCVxSLj77pX06uX9Dlm0qAWPPdaRP/6xPS++2MazrW3bSoqKlpPiyVaDqil4Y65bcRfoC3HPwycb297HvSufRtQa3MGIGdUxM1Tpaz7W+ik8Uo/1UxJMvYX9uw0NBnVv3OgdM9z+htjWT4m/lL+lc7JyGH/KeDKU93cz5bMpzPrKO5uXHcjm3v730iwz4rqJqePwKcqqQJBJkH8AFxvtK3Hf9Om3UJ7DfLBWie8AfIydaPY6x1n1uNPFXbixm+EGUs2H2IlmUymy6nGLw9zW88+37kRnbtlCn0svpcnX3jVHd/bvzzrjrn26WHPjjezu29fT1mzJEvoMGWKVkd146aVsO/vsJPYuSoEAy0tKqGzjvThr96c/0bugwI1tCbNi1Ki0LHFbnZPjhu4ZpYm73n8/XUpLPW2RZubEoaFJkyCTJi2lUSPve7e0tAv339/V0xYIaCZNWkbbtuk4Ya++xb6BqXAves1COJuB3OSVuI2FehGs9cda4A6czjDaP8KdDUpHE8Fa/+wY3NB7c0X5J0E9loxO1SblAxGAk9uczPDveSNlKoIVVGtvDtPtfW9Pj7yQOqa0rgAAIABJREFUSIp4FHdxmHDNjcdVBLgsTfJCIhmJvcKp+XNsBIakbYlbhwrgd9iVPcyf49/YU5VCAP6x+QGjfGxV69ZpkRcSic7KYum991KV402GNX+OPX36pKTEbbQq27Vj2cSJ1u/Z/Dk2X3ghW36RgpLuUdp14omsvf56T1ugosIqhrD61lutXCVxaOnde6+VL1Jdraio8L7H8/PX0a+fGTWdTtQCYKbRaJ5rNTAU1Ork9KlexgLvGG3mz7ELN8m+nLS0vzSxmX9j/hxf417rpVzanDmv6n0V32///Yjbf9z5x/z2yN8msUf11JgRwNJanjGGQuuNnl4c9hHgciBSzKCbaJbuJW4dVqKsOzXhtpHBJaFBixCWOqsV1ZS4NSo7pZuKTp1880VqVDdv7uaFZNW9uFoq7RwwgPW1VCTb16sXq+5KUUn3GKy/8kq21VKRbOu557IpNzeJPRKpctFFm/nFL8z6Kgf077+Ta65Jq7yQSG7BjZyI5P7UlbiNlqrCLU1c21pow0GZOcFpRoUqkkW8UbwPNy8kLUa3aTMQCagAY04cQ8CnS52bdGbcSeNS0Kt6KGA7WCV9a7yMY039padCPsdesLDG/TikSaJZHYp4HrBrfbryGUeaJJqJdLWve3c2DPFf42rDkCGpLXEbg+1nnsn2M8/03bZy9GjKj/BfNijdrMvPp6KTve6Czspy80Iap7Cke7SUYlVBge8sWkWnTqwYd5Cc70Rc3HnnKho3totOtmpVxcSJywgE0i+K26bKiXyHfRGQZsm9kajVRM5hmQfKrJKaptSbuGue+LkDlFklNWXSZiACMO+beVaZXoDvyr9j7Z70vvm+n0MmRFxd/Xs4tE5md+rNoQMQKeh9AE56vXciKuFU4PwIW80kOiEsmVu30v5Pf/Ld1vSLL6xSq+mq6ZIlVjJ0jSZfpvkNvjBt//533wUmVWUlTb76KgU9qp9O8+b5vncyt2whe926FPRIpMrTT3dk3z77lLpzZwarV6dRwYVaaQXcHWFjdyC9p433082BGyJsPB50ClZGrQ99NHZ+co20uvZJm4vJV9e9yvMrnvfdVhGsYOzHY9lXvc93e5qZAPwgwrbuQGmEbenDHWQ8CUQqnXMukQdb6SNy2eQad1PMT5PZJXGQ0Zoe48dbJVVrtFi0iI6PPZbcPtVDxp499Bw7FlXpnyPa6YknaPnWW0nuVewar1xJ1/sjTdTCkRMn0njlyiT2qH5av/aaVTa5RqCigl4FBWTsSVk1TZFEixe3YO5c/5XVq6sVo0f3ZPv29CilXYfbsRdsrtEB+ANovwWS080MIFJy1qlgrQeXhiKWTa4xFLT/NH8KpMVAZM2eNYz/ZHytz1m6cylTPkvzqCaHn4FVOcv0O4pJ/UphtSsg8ixCjfGh0sTpbDp22eRwATRP4kQccInDXKfHH6/zAr1Laam1hkW66V7XBXrNgGvjxuR1KkbRXKAH9uxxFwSsSN+0r+z16+k+ofZig41XrqRbLQMucWjYsiXTs3aInw0bsikq6oFO6+gsfSpwTx1P+hFpH56l/RZsNt0GOtKAK108SN2zHjNAH5uMztQl5QORymAloz4axa4q70qhF3W/iNbZ3iimP6/6My+ueTGZ3YueezH7BPbvdArmAjOaByjhpCT1LDYOP8SOj9wL1kKGmQT5Aw5tSEfFjAQuNVqXY1c1aw88xQKrtrY4zDX/9FO6zJjhaQs2asTGyy7ztKnqanqOGUPmdrOUfnpo/9xztPnHPzxtFZ07WyuOZ27dSs9x49I21KzbffdZoVc7Bw60ShM3/eILuj7ySDK7FjVVWUmvu++2yiZvvvBCqzRx27/8hbYvvJDM7okkCgZh3LiebN7sLRDx059uoUsX70D67bdbMn9+h2R2Lwa6Fe7ddzPy4EHMtdWgEPSPSEv6eLDWdqvCLumrgDmgu5KW9CAg32jcBMw22prjzlKlPPYv5QORaV9MY8k271oqfVv1peCEAopPLiagvF2c9N9JLN+1PIk9jIIbyvQEdgzkTBzuAoqM9sYEeQ6HHNLJJFrjJjeZ88A34XAr7qI+4brj/tyRb+ekQgknoK0vD7dsMlwLfGpsO4cvkFXDxH4ZO3a4oUxGSdVVt9/OqttuY8tPvRF92Rs20KOwkHS7bdnkm2/oOnWqp01nZLDsnntYUVholSZusXgxnWeb56vUa/3yy7T7v//ztFW1acOykhLf0sQdnnmGVm+8kcQeRueIhx+m2eefe9p29+nDylGjfEsTd580icbLlyexhyJZ5s3rzAcfeN+3vXrtY9y4FUyevJSsLO93yUMPdeW//03LNWXmAD2MtudB3QrcbLRnAPNBd0xGx6KnmwHPAk2MDYWg7sIOqW+LexGfZjFzujcwy2wErgNGYJcmPgV7oJV0KR2IvL3xbeYv817btshqwaR+k8hUmZzR4QyG9PLOku2p2kPB4gLKg2lVwrkI+LHR9hk53Bb692Swqkwdhf2GSSVFOfOAI432BTj7R9IjwKoy9UuKuSnhvYuWQ/PQCvDmF8ooCnkvVJr4MszSxJpCiq2/oTgcaU2PkhKy13oLZGw97zw2X+zm/q0cNcqqMtXynXfo+Iw54ZY6gb176VVQQKDc+125duRIdp14IsHsbJZNmGBVmeo8ezY5H36YzK7WqtGqVRxphjIFAiwrKaGyXTsqOnVipbn6eIS/YSr5vT+qW7Rg2eTJ6Kwsdg4YwIarrvJsj/Q3FAe3jz9uzqxZ3rXlsrODTJiwlMaNg/Tps5sRI9Z4tldVKcaN68muXek0ea9vxH/B5lDZfFWGu7BhuE7AvFBye7p4BL8Fm+G+0L9vwV0QMNyZ2DeZU0hn4f6uzQWb7wP111Bp4ksBs1b0jaAjJbUnRcoGIhv2baDo4yJ02OLiCkXhSYV0aXogZH/ksSM5qbU3iumbnd/w0BJzBi1FSnzjHneTwSBuC01LOqF1N8AshXIJDlcnvI/RKOY27ESzbwlfGdVhGwEuAWPdDc0UHE5PdBejNAMw4x5fxOH3+x8V8jmKW4znBNA8jYN/1qA4bHT0uZte3rUrK8IudiOtu3HEtGk0+9SccEsNv7vpO844g/VhpYj39urFqjuNtLZgkB5jx5L1XeS1DZJFVVTQa9QoMnbv9rSvu/pqdpx+4CvHb92NjB076DV6NKrKGxmbCtkbN9KjqMiaMVs+bhzlXQ6c79YOH86uk72h3U2++YauD5qRseJgtWNHBoWFPamu9l6H33XXKo455kAk0xVXbOCss7Z5nrN6dSMmTDDvFaaKPhG412isBAaDCl9IeDhgluX7OWmzMrkeDNZ12AbgclChKXEVWncDc92N0aB/kuAORut+4DSj7UOg8MBDtQr3ZzWn7ueAri2fNqFSMhCp1tWMXjya7ZXemOrBPQdzTqdzPG0ZKoOJ/SfSMss7yFuwfAGvrXst4X2tlUMHgvwBrPyC6xnHF8ZzIy0w8wiONRJPrhJORVuVIMqBQTh4g5kL+Tf2wCsLeIrJ1kg8uRyGYiearQGuxPzgFVGGXWO7IzDvoClNLOKu6ZIlHPHww542nZnJsvHjqW7uXZh2T58+rLnBW+VRVVXRa/RoKwcg2dr55BdUtmnD8qIiMMJ/Nv/613xnrESetWULPRzHDWRPoW5Tp9L0f//ztO0+4QTW5dnrlK7yWYm82Wef0aU0tYUKVXU1PUePJnOb96Jy46BBbDv3XE+bzshg2cSJVLVq5Wlvv3Ahbf75z4T3VSSW1lBc3IN167zpFD/5yVYuusi7hp5S4Dgr6NTJe9/vlVda88c/tkt0V+ugm0OEyAOUUSNc7cK9iDfLnk4EfUaiehgdfTR2ZEroxrEybhyrr8FaIDkAPAW6MymlLwBuNBq3AZeAMip3qL8BDxvPbQU8m6rSxCm54Jr+v+l8svUTT9txLY/jxuPM36OrY+OOOCc7KCMVYfyn41m7N0VT75FL3D6GwxMR9nkDu/RbM9zwpwhLNydYpBK3ijtx+CjCPr8HzBVSe7OPsoT0MRol9MVONAsCV+JEXCV1BPadmp+RNndqRDJFKnG7+uab2X3CCb77bLjsMrb9yJt7mb1+Pd3vqauATOI0WbqUbvfd520MBFg2YQKVbdv67rOyoIB9PXp42nLee49OTz6ZoF7Wza/EbXVODksnTkRn2qHZOjubpZMnU93MG0ff6fHHyXn//YT2tTZdZs60qqrtPeoo1txshs+7Kjp0YHlxsXslGqb7pEk0SqNQMxG7J5/syJtvegeZ3bqVM3bsCt/n5+RUMX78MjIyvPfR7r+/G199lZpLhpCZ2CVuXwQe8H+6+hR7jZFM3HyRFBW8iVjidgKol/33UfOBx43GFJcm1t2Ax/Dm6mrgWlDLI+x0B/CB0XYq7vITSZf0gci7G9/lyaXek1vTzKZM6DeB7EDkwdiZHc/kkp6XeNp2Vu6kYFEBlUH/2vgJ5lfi9mua1Jkv4QDmVE5fYKr91KTwK3H7N4qoreyMphHXAOa3Zy7FVrWGxJtCs1BeiPnNXIRj/a4PcKi5U2NW9rjnIChNLOLMr8Tt9h/+kI2DB0feSSmWFxVR0dl7Q6z1q6/S/rnnEtHNWgUqKug5diyBfd6bj2uHDWPnwIER9ws2bequSt7IW0Cly/TpNP/kkwh7JY5vidsIv+tw5d26sWKMMWEbDNKzsDDiWjCJ1OLf/6bj497rlki/63Dbf/ADNlzqLfqXsWsXPQsKIq4FI9LbkiXNmDHDm1eWna2ZNGkpzZqZgRIHnHLKLoYN896cr6gIUFDQiz17UnHtq/OAy43G1cCVoGqp1qGmAebKsN2Ax1OUL+JX4vYtoKSO/UaCEfECZ+NeEyaZzsStBGreYZoGyn8VXgBUJe7f0Cz1eAfoC+PZw2gkdSCypWILxZ8UE9Te6f5RJ4ziyGZ1xz3e0ucWjm91vKdtyfYlzPhyRoQ9EsTNh3CMVjeG8G4rhtDcN0gmV4J1lz6PYuvDnVgON2CXuF1Ftm8ModcothLgCuzSxA/iJHnVzt2+iWZv0IdJde7r8CnK505NkGeYaH24xSGq/cKFdonbjh1970ybqnNy3HwR4y59twceoGmSVyzvNnmyXeK2Xz/WX3ddnfvuPeooVht36feHFSWxNHGk8LYNl19uzT752Xr++Wy+0HsuzdyyxV1fJImhZllbtrgzbMZrrvCZffKz5qab2H3iiZ62ZkuWcMSjj8azmyIJdu7MoKCgJ5WV3u+SW25ZzbHH1r1w5bXXruO007yfh5UrGzFlSre49rNu+njsm6bVwFWgIkUehLsWu+DNBbgX90mkc7FL3G7BDcmKPCoEQO3GvYFp/uGKQZ/rs0MiTcReQHsxkVe4D6O8OcChRtxCAt3j0bloJW0gEtRBxn40lu/KvQmQF3e/mJ8f8fOojpGpMpnQbwLNM72x2k9++yRvbUjSqsBuidv5uHkR4W7Gsaoq+BvLGpRP3oJmJuMjrugZXyWcwIGKEDWqgMGMtqoq+CvkX9hrjrjTnfdGXNEzvhz8Es02ksVlDLLycfwV8TDwR6O1GxXWdKc4BPklA++P1W8ZXdrT7uOPZ62Rt6BqFuAzEq0TpfVLL9HuL3/xtFW1acOye+6xysJGsmnQIP/SxD6J1onil/C/u08f1oyM/lplVUEBe48+2tPW4qOP6DRnTlz6WKcICf+bL7qILUY+TiQ6M5OlEydapYk7Pv00rd58M25dFYlXUtKDtWu9M2Bnnrmd3NzoFhANBGD8+GW0a+edDfvrX9vy978n636ZdsPI/SIPUFEm7KptwGDMgjdwP+h+De1hdHRvsMLINXANqFpWfQ2nPoP9VVFrZODO7iQpgUf7Jfzvwk2yj7LMnlqInSPTBngymaFmSRuIlH1dxoebvSUhe7fozW19zb9l7bo27crYk7ylGjUa5z8O6/eub3A/6xCpxO1CnBhL8RbxIm6Vg3DNqWYBDo39dombSCVuFaNxeDfGo00EzHjKo9mbhNLE4/FPNFMMYYxVoawu1+F3p8axEsDEISRiidvrr2fXSbGtObreqOQEodKzScgX8X2dmhK37c3ljWq3sqDAU8kJoOXbb9NhvrmMUPzVVeI2WsHsbJbdc49VmrhLWRktklCauItPCeS9vXqx6o7Y0s8qOnVipRlqpjU9iovJXhfrV5xIhfnzO/D66968kI4dKyguXl7XZKtHmzZVTJiwjEDAe0Ng8uTuLFuW2EuGkEeB44y213GXJ4iB+hAYZzQ2wk2WTvDaahFL3E4B9RefHWqhSrFLE3cFnkh8qJk+Av813IaDinUa/mbs0sRn4am2lVhJGYh8tOUj5nzlvRPVJKMJk/tPpnFG7B+g8zqfx2+6/8bTtqNyB+M+HkeQBE69Rypx29ia3orWaLAu/P1K4sWbX4nbf1BkDYzq5pYmHoJdmngwDlfWr3tRvW5jqnkWO9FsEkXWwCia4/mXJob7cEjSnRqRbL4lbr//fdZfcUXsBwtb2yJc65deou1f/9qAXtYu0szL+quusgZG0Yh04d/1oYdo9tlnDeprbSKVuPUbGEXD98I/GKTn2LFkbdniv1MctPjoIzoZi0JGGhhFY+uPf8ym3/7W05axYwc9CwutBTdFevn66yZMm+bNC8nI0EyatIycnNjLSg8YsJOrr97gadu7180XKS9P5OWcHgxcZTRuxFPiNiZTAPNL8SjsmYp48ytx+2/sgVG0hgNfGW0/B26t5/GioGsW0DZnXmaBMgdGUYhYmngs6PPq08OYe4D7R3kfWI6dtByTRYsW7cRdNt5jb/VeK6E8U2XSNLP+VR+qdTW7q+yQh+ZZzQn4jK8yMzO7nnzyyWusDbFwyKGRUaq3Efv2rxdSH9NoxG5jqrMcjcO2CHs0lGISrazWcnbjWBfh0XNoSiO8c88BqurMman/62XSyCf8q5ztocFRfY9r/43LKcex4kHjZRdu5bTewNIEvcbBqgwYCowF6j2tsGjRolzckAIvrcnYab89g02axHT33RTYtw9VYXyUMjOpbpqYKjequpqAT/hXdfPmVqneWGTs3g3Gha7Ozq7XxXQ0VEWFlWSPUlS3aFiUZ4ZPKeWG/o1rE9i710oo15mZBBvw91fBIIFdu6z2YPPmvmF3WuurTj31VP/qjdEZjJsE+zqQ7Lj3ZOmFu1bWbnyuW2KxePHiIq21Y7bv2xegosJ70zojg1qT0+sSDOK7qGGzZkGruhaA1vrBU089tYEXxroFbpWrcOWgGnBe1Nm45z7TttqT3htCt8KeRdgTfSiT7zGbgBXFUg0qQTXcdQbgN3O0M7RoYX2P2xw75aAilBOTCMuAHsDp8V6evhyfD3STjCY0yTDLTTdMhsogJyv6Wbzy8nKzhnXszDU14uEmynF/b8miGcXWuB/VvVBP1MW63+tVQUJ+jtQuACHiTmu9VvnFQChFdU78IwGCjRtDgi7W/eiMjIT8HGYZ3ETT2dlUZ8e/jH0ifje1CTZpAk3ie77TgUBMP0cgEFgd1w6IemncOBj3r4JAAHJykj0TphJwQ1FVYEcgJJhKwA1etRe78mYCqWoSce3jrveSEvGey/s6zseLl80DBw5M3Fy8ECJtKaU+xK5SJ8ShatuOHTtizfUTQoiUiPdAJPHZjPWglJqvVKKm+oQQ6WzAgAGVJDHxTogUc84555yGRwAIIUQSxHUg0rJly5la60XxPGYcrFRK1bVAjRDiEDZgwIAZSimzXLUQhxSt9YP9+/eflup+CCFEtOKaI3L00UeXL1q06OfAU8BP63p+EnwEDOrXr1/yl9QVQqSV/v37371o0aK/AtfjrqjbKcVdEiIeNmitP9Fazxg4cKAsMCKEOKjEO1mdAQMGbAZ+tnjx4jO11udrrTvE+zWisB14Y8CAAf9QSiVvKV0hRFobMGDA28Dbqe6HEEIIIRIwEKnRv3//fwH/StTxhRBCCCGEEAevpK2sLoQQQgghhBA1EjYjYnH4GYqTPG2aNTg8Vc/j5aFo7WlTvEthgmdhirkKM7ZcsxiHV2I+1gM0YSc3YA4INX/CsVbrjJ9SsljHjShj8RrN33GIfelkh+4oLjWOpYGZCV2Xw6ETylrtFTRzcOpRrtVhIIpzjGPtxuFRQKquHYK0u4DTDYC5gMULCv5bj+N1Ay7z2TRTuSGjCaGhI3C1z6a5CmLOkdNwKvZCdruB6YoGLBha9+ueiLsycbhy4BEFMS/WpeFXQB+jeZnyW+AyjrSbI3my0bxWwZP1PN5QoK3R/J6Ct+pzPJE8H33UnP/+17u8Wk5OFb/5Tf0qir/wQhs2bfJ+XR1zzB6+//1EL4Glc3EXgQy3BJS5Qno0x8rE/d5tZGz4B6hP6tW96F5XASOxF1J8HdSH9Thee+Banw2PgdoQ+/Gift2WuKu6m54BtbIexzse+KXRWAk8DKrSZ4e4St5AJJPPqeIpzC9Thz04/DGmYxWTj2amcWm4Bs2chnazTpoVwBzwrL69G4eBOCyJ6Vg7eAgYZrS+Bfy+QX2sSz6VOATQTDa2DGMy/SmI4YLJIRN4Bs0ZxpZpCV8c0GE9DscDQ4wtP8L9UEU/eHDoAPwfms6edsXVMR1HHFQUVGp3pV3rs6ChfyyDB03oswA/MDY9kshBCICCDRr6AlcYm87W8MtYBg+a0GcBuhibrknkICTkS+Ax4BSjvT0wJpYDhQZTz+EdZFYCZzWgf9FaAjyNcb7TUKXc90jUQoOQMqN5A/B4g3ookuKoo/ZRWNiT9eu9gwel4KKLYhuMvPVWS4qKeqLDzkg5OdU89VRslx719CXwBN5VxCtBnw0q1nVrioHRRttnQIIrvikN+jvgYWPDBtCngFoX/bG0AmYDFxobFoC6t0HdrJPaDror7mAu3MWgzwwtFhkl3Rz3e/J7xoY7kjEIgWSGZo1lFfhe1M1hPD2jPo7D8WimGq3VwJX1ugseK4c3gIlGazNgAQ5NYzjOIOxByBbgitCq4Ynl8Hvgz0Zrb/ZZJ7y6TAZrEPIpcHd9uxajEcD/jLafU8wdUR/BIYBb6a2zseUxiuRkfxiYinvhHa437kkmFhOxByGfAnfVs1+xuh77s/Az4M5oD6Ddc8KT2IOQ+codICSUcmc/BoF1E6NAx1CJUUMr4Fnsma47FLzfsF7WTcEq4Crs8910bd9Vjig0uHzIaA4ClytY37BeimTIyali/PhlZGR43wr33deNr76K/pJh/fpsHKeHZxCiFBQWLqdLl2QsUK78vsuygPmgzdm6WuhzsK8PdgODQquUJ5h6BnsQ3xF4GnSGzw6R3IU9CPkG+7ouUe4AFhttA4EJMR6nFHsQ8gLwQD37FbPk5og4/A17xNuKap7FsU4YtimhC35o4mlXFOHwWry6GQUHeNVo6ws8GOXeR2Hf4dLANTjUY1qtXjSNuAZYYbTn4vhO+dmK+Tlwm9G6iwwG4ZCcBbUcdhFgEOD9AtNMpMS6KIxkLPATo+1rmnBTHHoo0pxyP3vXAsuNTb/T7kC3TtoNJzIHv7uAQcp8byaICr0e9utN0PDDKA8zGjjfaPsayGtg96Km/E/mAeBJbQ+QIpkD1g2uv2HfCU0YBX/HPie0Ap7V9gDJotl/vjOvVh1ln39EGjvllF3k53tvtldUBCgo6MWePXVf+1ZVKUaP7sWOHd4glksv3cDZZ2+La19rpx4GK4KlG/BYaIagDroj8Ae8ESUAI0B9EYcORmskWBEs5wCjottdnwaMNxrLgUtAJTpGLkSFXs+abb8DtDlAikCPwA4lXg1c5c4eJUfyk9U7cyfwntF6KvYsg20304HjjNbXOc4Kq0gshyBZXI59R2oYjhUm5DWNRrh36nKMLVNw+Ev8OhmFUWwlwCW44QrhplJihUZ4OXRF8wRuWMsBiuGM48u49rMuhfwX+yIwkyDPMNGKq/ZyOAt71e19wCDuZmf8OinSmYKtuF/q5q3FqdoOE/LQcAT4fBZghCK5n4VQXot5cyATeEZDu9r21XAmUGQ078MdTCX1sxDK4TBnpNoDf9D2RYyHhpuAi43mVcDVKvlhlndjn+8GYIcC+nkUO7/lDaI5V4q0c/XV6zj9dO816sqVjZgwoXud+06bdgSffupNa+jTZw833rgmrn2M0nXAMqPtAuDG2nfTNZEH5vpNc0HVK3eq/lRoBoY9xoZi0GZunEG3BuaDkWMLt4H6KF49jI76FvumjQLmgT6y9n31CdhpAFXApaASH10UJvkDkXwqgcG4YUjhbqOYX0fcz+Fa4EqjdQNZXM4gquPbySiMYQNwOVivPQOHYyPut4UHgH5G64fAuLj2L1qFfIB9Id6YIAtwrMGSy9kfC29e2MykiKfj38koOEzHvdMSrhsVPI59geia6F7YYF7YKG7C4T8J6KVIY8r/c9gIWKDtGwcAhC6Kn8D+LMxS1LMQRwMpmAnW57Ar8LiO8FnQ0Aa3v2be4M2KlH0WbsR+7R9Ry3dlKNndjM+uAgYr+C6+3aubIuL57hYNF0XaT8OlYBXi2AhcpuxzjjgIBAJQUrKM9u299/1eeqkNf/tb5Ptlb7/dkmee6ehpa968mkmTlpKVlYr0RbUN/5s294M+vZYdC4HzjLYl1DmASRT1OVhRDwHcEK0Ii91qBcwFehgbngc1Pc4djJJaiPudH64NbsicOVgK0c3xiy6CQlBJX2crNeV7HVairKl+hWYeE7BHcSX0xZ5SD6K4gjHEkFwUZ2442H1Gq/sHfsD6A0Mxv8WN4w7n3ol1rA918jjcC5iVL44CZkXYowQ71OMzcqw7scnVhOFgVRv7JQ43+zxbUcEc3LvZ4RZQFHOejDh0TAFrZtIvlLJGMXZ1qc+AW+Pcr1gNx56N+QU+/dL776Bh3ppdqCJ/ByScqpmZtGdjxmn7ggZd893rTaYFGK0g1mTauFGwEjtfRAFztX1Bg4ZjcOO2wwWBIYouilQvAAAgAElEQVQUnu9Eg7VpU8U99ywjEPAOIO69tztLl5pvW9i4MZuiIm9eCMCoUSs54ojyRHa1DurfQIHRmAU8FaroZNA/wg2BDrcPuAyUOSuRRGoOdiW7TrgzCn7Xx7di30BYSRJDVyO4BfjYaDsde4a7xnSwbpi/jn09mxSpW0ekiOdxp57DtaaKZykNm/JyaEyQp7HjZCdQxMsJ7mU0CrEXbjyBHdzvaXHojrZO6m5sumPFpiebJpvrAHOe95LQTNQBDudiJ6ztJoNB3JacWPiI7mYnAS7DjdUMdx8lfN/T4nA3bmnPcMlMNBNpSNXkatm5U4NC1Yv2025MsXky3o0bypTCk2ut+SKTtV1c4k7spEu/Kf+kU/75KQHgKW0Xl/BLunwRjO/iFFBufsojRnNrjHwR7Q6iFgAtjOdOVKTF+U40UL9+Oxk61Due3LvXzRfZt+/AJVl1tWLUqJ5s3+6dpMzN3cRPf2pOsKXEg0RV5EN3wD8vZGRiS/VGbQRg5qf8DCvcW58KTDKeF5rxVCn+g6hIRT5GgTZy/vRQ7OqKG3AHhSmZbU3tgoZtuB0wY+pOYx0lYY+ng7H+CLxFH89zUsetcHUZWBW7rscJJQGV1lSWoI3xnIdwrA9yaoxmE+7PYVbsepSS0O//nprKEtYXyvWMsz7IqVHIYvwqewSZjxP6/ZdwGljvHzfxK9Elh0XaU24YzWDs3KmHdWhtCE3Ez8JIZZ/UUkL5V+zKAubrUFlZ7VZZ8Uu6HJToksPRUu5352NGc0fg6Zp8kVBRATPpcg1wZQryQiK5Hbtil/n7fwT7fPcv3Jk3cYgYOnQdp53mPdUsXdqEKVO67X/86KNd+OQT7/ojRx21l1tuWZ2UPtZNRSzyEUqCJjSr4FeF71lQcxPcwSjtzxcxb9rcAzpU8EZHqsJ3NygzByxFVMQiH6BDv38dqQrfEFApq8KX2oHITZSTwSDsE97dFPMrHAbj3p0Mt4lMLktJXkgkDqtRXIl9wpuJwzGsYwoYd+RhEckrcRsdh7ewT3huvshkWlLJ0/glmjk8kZwORslhGnZlj+7AYzi0IcizmIlmittxrEGxOEyFSrw6RnNj3HyRlkQo96zSbG0H5V7YmutWdAMeC+WF+J1c71T2DaJUuwH/KjcFGvySLoO4g5CkJl3WJpQvMgT7fHenhgu1ezF0nbFtC25IVuJLuoukCQRg/PjltGvnvdfx5z+344UX2vLuuy156invqbZp0yCTJy+lUaNEL+UTCxWxyIe7LgejsKvwfUPqQ5kMyi+cNhM3z6Id/lX4XiDaSqlJo/yKfIRmpHQO/lX4xoOKfUHuOErtQARgHN+iyDdaVWhxQjMuOwhcyVgrhCj1inxDAFoA/8ROiNpOqvNCIpuIHQJwDPv4GPix0Z7CRLM6NGIo9p2aX+EOAM08pOcpssIEhZgMvGS0HY0bi2vmKHyBvbhUurge/yo3i7BzFP6EHUKUcmr/OgNWyJuDWybXSrpUJLWke1SUf8ibwr3QMS8gNHBFKMdEHGLatKmkpGQ5AeMqbPLk7hQW9iBojDdGjVpBjx7JqYofG/Uhdv5HI9x1yhyjfR+Qm7wSt7FQpbg3ZsJ1xS1iYlbhWwlckcwStzG4Cbd6Yrgf4Rb+MKvwvY49I550qR+IABTxLHZyXnvc5MNw9+Hwj+R0ql5GA+8YbT2wS9yOwGFpkvoUG6dmmo61xhbzbsA+AlyGk9pY+Ijc0sSDsO/UmD9HOiSaiTSkiOGzAJeGLpbTjoJtENVnYRUwNI1CmTwUfI594yMTd4Yn3OtEVx43JRQsBGYYze2w80KmKPeuqzhEDRy4g2uu8eaL7NkTYNs2b17IRRdt5uc/T4u8kEjux14guRt2Fb7bQKVzRcqh2EU+zO/JUDh+qvNCIlF7gd9iF/kwf45NuCFZKY8uSo+BiMsv6z/cB3S2ysymF4cqMrmU2stETqfICpVILw4bQ0nftb1BR1JIOiSaRVbIv6l9gaJKAgzGsUprCgGAYn/uVG2fhRsV6f1ZUHWHglbilrhN68+Ccktn1hYKugF31fGUn1zrcCu1h7+lrqS7SKr8/LUMHBh5mZ5evfZyxx2rktij+lCRinyEew6UOQBPMypSkY9wY0GZN5zTjKprEdog7oyOeZMtJdJnIOLUlHLzPYFsAQaF1iBJb2NZhYoYrvQf3ITF9FfIm8DUCFufxiFNEs3q4DCVSCEairsptBYbE8JDwZvYOQg1nlF2SE26egiIFAs8KpUlbmM0Enf1X1MQdxCS9iVulVsQINL5bitusYB0DN0VcRYIgOMso3FjO/ejcWM3L8RvW/pRWzEqC4ZZWsu2NKM+xQ4pq/EP3BLvBwE1H3f21c+9oP6ZzN7UJn0GIq6L8F81t4qDKVlPc0GELZW4J8v059AY+EmErekZjuXHoRuhSkcWneJyw+KgECqpaiZc1jh4PgtuvHOkVeIPps/CqdiFAsD9bk3l4gqx+jWRz3fpf9NNxM1777X0lO6tEQhAht87JH2ZuRQ1qjlYrn3Qmdil/WtUkaahqzbdAfhBhI1p9X2fPgMR/5KqNdys/wW+X9rpxeF67DKSNU4F7klibxrCr4xkjWE4DElmZ+olctnkGlMpiXhhJkSNaUQazMJ12q7Jnnb0/gowRFrC+QEdeZCSNrR7LvArmwzuz/iMtle5TzsRyibXaA/8Qfv/jOIQY5btDbdnj7u+SEVF+lyqRaZ/h1tG28/RpHBx1BiNx16wucYFpGuBHo+IZZNrOKDNhXhTJj3e3ZNoTZD5mCVVvX7EF4xOVpfqpYQTqHvxrNsp5tfJ6E69Ob5lJE0zGG8tHpZe1jEZu2xyuMb/z96Zh0lRnfv/c5oBRkEEXFFAwSVxvYlRYxa3mPiLS8xNVEBRUdCwKSqKDjDM1AyrIiqogHHBBRRQc+81qInJjVtu4prFhURRNOAGGkAEBWbo8/vj1AxV563q6e7prqrB+jyPzyOnumvOTPepc97t+5JlEdeJItGUFABcSdWWGvvN1rJLbdKYimxk6KUjRpq4S0TzKRjtNjIkOBrSRE/gXm0LhCQIDWE9CbwEdaJO2c7YsiXD+PF9AqMhTSxbtgOzZu0d4ayKQfdGCg7ZDAB9QRSzKR4d1LDZ5nrQR0Qxm1ZQRXgUH8yz9D5Xmjh2kmCIKDZzD1JG8vfY4SNNLfUcH820CsShM1keQspILrH+rdDciUPPiGZWGBM5AJnznkWqt3RmKw8wi47RTKxAHE5H6oKvBf5ojR3Al8mTK02JHw37Eywh/pg11hnjia+MZGIFouE0ZG3aOkyjPC/7Q6JlrMch00XfQEoTnwpcHsmMCsQ1kOYh97v/Rab5TdBwQgTTSomJadN6sWyZ/8jwjW9soEsXf+nQokW78/TTXaOcWgHoDsDDyMyDx5HpWLNBJ9Rpo/fC9Fyyz8X2Ga4jsMjty5FA9PeQ/eC+RNYH7g3cCzp2p038hkgdVwBnWKPvUMlZKHGQbEeWBTiJDL3PBhEheAKHM5AhyV2BB3CEtF28zKIjW1mIlJGcgslntotZj2BNAgu3jJE3D+kVvRgja2cXs15AHYOimFpK20A3bzYiQjANU8tmq6Z8g5ajoZGjTYTgHuRaGAGchZQmPk/LJrKxo+E4oNYabhI4CWqodr2GY6KYW4GMwnx/vKzARN5s46kdsFDLJrIp2wFPPtmNRx/1H2W6d29k2rTl1NW9i/KsWK2hrm5fPvwwVxAtNq7DpJ17eRH4GSYS66UT8BBo22EbMzqDaUa7u3Xhdsz59FFrPMhJlQB0N0zqqn22vALzedjSxKeSAAGleA0RhyPRQu99Mxn6U8Vn1HI75o/qZW+MfGPsVlwzDoOReeIfQHO39csxillejoWEyRGv4UbADjk+B9S50sQDkNLEl1HHzyKZXz4Y4+5BZJ74LTj8CofVBKnVaG5jIgdFM8mUNsANyLXwAuC4Xa6DZLpHahgQxeTywa0xuBe5Fm5TRu0reC3ALVo2vooNtwP8/cjNdaSCVxW8BCJttz2wQMPOUcwxHzR8C3No89Ism+yqr823ru8BzNNx79UpJWXlyo5Mnuzvq2u6rb/Lrrs2cOyxn3H22at91z//vB1jx/aloSE5Rx/QpyEN6HXAAFBbMM4DW7XyUODGCCZXCDXIJrWvY/qeNEkT241F+4FuKW03QnRTtNVu2PwQqF96pIntrphTQedK2y078T3cprEzwXmy11DDK55/D0NacadQx9XlnF7e1HMIcIs1ajrAO3wKGGnidvRDNpgZT12oMlW01HEmxkvqZS0VnI/jKpZVsxK4EFs1QnM3E0WznLioRxaavUoXTw8Fh6cJ8tRsZTEOO5Z3eilJR5uCxJHW8Fqgv3LVjJRp/jcIqaAyR8vGUXFRB9gFia8BY5r+4UoT2wIanTD1IrGvBU8qU2/r0iK3p0gTNyIbqvUlIdLK2qTvPQAilfVahU9CfDjwT+s1P8bzmaW0bbZsUVRV9WXjRr8WweDBH/Htb29rOH7FFe/z9a/7s/XeeKMTc+eG1R9Hje6JcXTYltFgUG66pNqKeU5+ar1mGOgwUZ+I0UH1WBuBfqDcD0CtwTibbafNLNBhoj5RMxpE/fE7+Goc1auY+hEvrpCJDhP1KTvxGSKbmIPZKLwswbEO9Q4byDAQW5ZRMwUnZ/Fl+XGoJMsC7A1b4eBYXoAJLMOE5b1k0Mxncs7iy/Lj0Bst0sc0iouothoUOSwBUVPRla0sxMlZfFl+HIIKzTbQjn6MFnJ1bcVTkxIh2nQDtjdXDVykrGZdytSKzLJu0RVYpHMXIpcdDSciN5wNmP4U9lqow9QoeDkEuLlM0yuEMcjU3bexmnW53eCDGqqdpWFo+aaXN7cDB1pjj2P9jZX7GSE/o0k6XIozpQ0xY0Yv3nzTf2T45jc3cMkl/ozhDh00U6cup1Mn/9n3vvv25Nln4w706TAVvlmg/ss/pN5nW3aIl9tB22siYrRRZJUKdSNB/cM/pJ5FKrtWAotBxyx4o4/CpNB7aQAGgvrMGp8FWJ9R074XT71IPIaIkbg9xxpdSYcAbztADa+gRFdgsxCcUGnWKAiSuH2ag8QXwuBwD7Ir8O40sCA2aeJwidsZ1AoPo6E7Y5BdgXPJUZafyexBsKzncCaIiBo4ZKngAqSnZih1DCzPJFOSjEfi1l4LNynpbW9iDPC8NRarTLcmdC2MUNLbjnIbAQIfW5cu0cS3FkIkbjdjjKn19uuViVoNQPbguFmHyy+XHW2i+rb3931gkArY75SJWtkOlSZp4jD55ZQ2wP/+bzceeWQ331iXLo1MnPgu7drJo0+vXpuprvbb1lrDxIn7snp1LpHRsjMJaRi/CuKc5qKeQDr5OgML3GL3GAiVuF0I6t6QN00CfmeNHUisThsdpsJ3NagX5OuVBgYD71kXTkdmxURC9IZIsMRtIxnOYZzIud5GbS4rLoZ6kWCJ29XAQPoFdss1dGIEYFnanMg/hPcyGoIlbl8Cxoe+ZxSb3VQz+zAwxi3OjxaHDA3MRxZ13oEjcq63Uc0HqABPjWZu4qWJU8pBkMTty8DYsDe4qVrnAbbX6Sotw+Rlx60luB8pcXuXMuOBKFiFiSjYKjdztRThKDs5JG5HK/hr2PuUMQptxZhKTKpZ5F5LHRxlbQTOUdIJ0owyTq5fWcO9gHuSLE2cEs4HH3Rk4kR/+r5S4Dj/Ys89ba2FbfzoR2v5z//0f1XWrq2gurov2WwcXwUdlCroRvKUXX/gZSz+NESAI0HUCUdFkMTtMqxoqx+VxaRo2YI3g0HH1U9qNjIdeAmyZMCDWkewyMeMOKSJozVEjMTtYqTE7XhqhAqNTbgV54h87vLihMp6no8jVGj8jGnKPRTSxHVualF0OJyKlLhdRzv644gvqJ8Jdu4hYDbIO5lE1KLnQYVmb2CUInJTyxPADGu0M1tZkFhp4pSSo+EUpHrIZ5i6kJxrQYk83KZh5mlZOFhuqpESt0uRaaECBb9BOok6Yw7xkUkTuwftu5ESt48os+m2xFTgSWss8oZq2q21Qe53NUrKiAcxBClN3EYaqqV4aWxUVFf3YcMGf5Dy/PNXcdxx61p8/zXXrOTAA/1Hhr/8pTN33hl1VrfeA1OzZZ8dh4GSmQc+VAMmYrnGunAFaFtJrszobwOONbgJY0zZtbwWahUmUmw7nOdEL02sRxKUXQQXupGPHKgXkYJJsUgTRx0RmY1s/BW0+QXj0GTF2aH3G3CEwk15MIfTxQTJejpi8wvGaVJj8GEUbqKSJjYSt0HRpOFMEJtf2D0WI4tBd6ORByJLNasPbPxlZD0docsfRpCn5lusEQo3KdshrsRtkBLfcAXL87mHgoeQB91uwP1aqj2VBVfi1t5YNgHnKtmjIozxSJnuw5FqT+VkFAglvhXk9FRuQzU5haTXcoAmUpnu2SCU+J4Crs/nzQrCvJbXuwpcKW2EmTN78tprnXxjBx+8keHDP8jr/R06ZJk0aTmVlf6A5Z139uDFF6M6M+oMJuVTZh6gbHXTEFTQOnYdD3rfVk4wT3Q3TLTVzm27ApStbhqCClrHruMhKmlifRiI9gmuoqMKzy7ycz3wa2sscmni6AyROoYgJW5NOoAj0gHCcQi34pwIugIbidtvWqMv0ENY17lxmIspkvISdiAqLeESt7fhsLDAu10G/N0aO46lEUgTT2E3siGFZg6v5n0fowoW5KkZhSM0/1O2I3JI3M5RZo0UQphMt93/ouRo2I3gosvLlFyfobjSxEEy3aO0NA5KTksSt/neJ4c08W1aGgclx+3FYneRXgUMVHJOobjSxLajpSNGECGhDdVSvPzxjzuzcKG/PcVOO21l2rR3ad++Bae1h759NzFmzErfWDYL1dX78u9/R1IvUgucZI3ll3ngQz2CjGx2wyg3lfkXCZW4fRhUS53hbWqQkc2g0oMyoDsTHG2tBtVSdpEHpTGRVzuTpx/owa2ZYSFEY4jUcwhaqMtkUQzEEQWSLeNwHXFYceESt/0ZKqI0LbMDw4C3rNFTcES6VKkJkrh9jS5FSEQ6TeFMIU1cjSPSpUqHQ4YtgYVmi3B8sp753m8FSoglGE+NI1JEUrYfgiRuX6eIJk+K0LUwTst0qZLhpjLdBSIlcrEqQr5WESLTDXdrmS5VMnJI3I5VMmLZIooQmW6TalY2r6U2xaszreEscL6SUZp8uAHZUG0/EtlQLcXLqlUdqK3dF22tpAkT3mOvvTYHvykHP/3pp5x6qt9HsGZNe6qr+5DN351bBPp4ZN2oJXFbEKORtV5B6VKlJkjidjmm2XGBqKZ+Unat1wjQdrpUqZlDcHZREQ2m1SeEOG1AH17M5Aolms7eiizyw19HLS8XeUdNRwaxOSA87VDpHo5Lj2Yl9oGiHSuFxG2+XMvnTOJkGjnAupK7PqM1OGSA32NL17bnjQCJ23zv+ZYrpewP2WZyiA+0ns6YDdr2PtgKRvlTy69x+D62HHO7mBTNUsqKe4D/A+bA6mVpgMRtXihYpglYCzLCUEo6YyQZbWdP0WtBwRJXLraTdamce8bOyP4tWUw6U7E4mM/XjjLvRJGfcR5kgJ9bY5+50Y2CUUYoaRCmsNeHhh2K/a6mlJ+GBsXUqf5M58rKLIcfvqHoe1ZVreD002Vw8Msv2wmp3xKyHtPPxsvHoJYWdzu12W2GeIh1IWtSwFS5zKqXkE6hZQESt3mi3gf9fYyQhBeh6lc6dCUmim8re71S/N9NPQP6GIxIiJfCHezF/HSMFfo8pgg8KY24UlK+KmzAHPb2I896hK8Qd2A8VdXEKIebkvIVZAAmNfEpZMSwVFyMyWSYiPGuR01fjNDERowxXzSvvPJKrdbaKcWkSo3W+uajjjqq3FkWKSmF8i4mwn5MfA0NU1JSUlJSUr6q9Mf0ndixpRempKRsv6SGSEpKSkpKSkpKSkpK5ERTI5KSkhI1O0PBCmgtcR2yniIlJSUlF8dj1ITmkJ9i2DEYtbkiCm9TUlLaGqkhkpKyfdIRWVzYWkI7c6ekpKSEcD1wNKYh8QjCRRR2xaicNcmG/jem03VKSsp2TJqalZKyfaIxajql/K8x0t8gJSVle+DnmGafh2GaZd4HeBtrKEzPlaWYAva/ACeTGiEpKV8J0ohISsr2ySekRaApKSnx8wEwFCMvPQXT2Pg0oKmRxh8wMq7vYBrkLkb2sElJSdlOSSMiKSkpKSkpKeXmDUw/sWMxUuU93PG9MQbIQcAiUiMkJeUrRXQREYdDCWp4VyO6a+ZHPd8my07W6Aoc0am8tJjGfban+T0c3i7iXhkUJ6KtZlvteYPxRXXgzffnVgAnBFx5FYfVRdyvCyYH2E8n/syYMurDT6VbYFNL+GNRTS0demJ3K1VspZanSTfH7RIN7TBrwW5496qi8LWgCVkL8LwyPWPKgjaNqETDO+D/iml4p83h8CBreCvwtCrjWtDQG9OZ3EuDgmeKvN9ByI7z6xRFN9PN9+fK/Q7WKJN2VMz9jsZ8t7ysVPBmMfeLkQ7AwcA+nrGdgeOAJzBRke2Kf/2rklWrOvjGOnXayiGHFLc1vvnmjnz2mf/otssuDey3X7n7WuojkQ3vPgD1jyLvdzzQ3hp8C9SK4u6X189UmOe93aT4H6A+KOJ+O2Aav9q8DGpd4ffL++d2Ar4TcOFFUEU0U9S7A3YXdQ08DapsXTKbiM4QydCRLEswRbSGLA3Uczw1/Lmgezn8mCyP4Y/obKBd4EZcag4E5lljq5jMNws2HhRj0UyyRpdREXi4Lh0OjTj8FLjUuvIiDsfiFNzZfR6yk/BjjOF/i55jPuzHepYyFtlway4wvKB7TacTG/ktZpPchqaW1nV2josKgg+mrWEZ5e0QHjkKtmo4AxhlXXpRw7GKgtfCXOAca+xx4PRi55gn64Eq4CRr/JeYtJi80VAJPAb8h3XJUeVfC58Dd+I/qKJhqDK/S964Rs0fge7+YX5GmQ0RzN70KLCDZ6xRw/HK1EnkjTbPtyfxH542AEe1epbRkcGkZNVjPpc/A59iDMX7gYGYaMlsTPPST+OZZunJZDRjxvRl48Z2njGYOXMZ3/lOYWfGpUs7MWTI12ho2OY36dBBc/fd/yzZfHOwO7AEv9PmM9BHgCqwGa/+BXC7NfgB8I3WTLBllAZ9LFBnXVgK+ihQXxR4w5uBX1hjz1G+JqBNfAGMBP7TGn8EOKuwW+kK4FdIg2oWqPKe4VyiS82q4RXgGmu0PVkW4vg2itw47Ik5+NpzH86ECLxDDvcA91qje9DAAhYLKzuceo5F41ijm8jQn2v5vHWTzIPuXA28Yo0ejelymz8OlyKNkJV0YBDljiL0YysVXIDctIbhcG5B99rIrdhGCDzNwW22o3d3zGZfyv/+X6S/QXRcjVTyORqEkyAn2igC2UbI+8CgckYRABRkMYe5j61Lv9BmvBBuQxohz1Dg36MYFKzFdPVusC7N1AUcUrTxtC4EsbfcpOB/WjfLllHwKnK/qwAWatgl3/to2ANYgPTgjlAQyemzBPwQs9fcg+lgfgXwfczBE8z6+xbGOLwC03F5GoiMhzZJr16bqa7+l28sm4Wamj588okdEAjn88/bUVXVx2eEAFxxxft8/euFnp+LQT0O3GgN7gwsAt0h4A0h6EOBm6zBrcAFoKIwQCcBv7PGDgZuKew2+mykEbIGOA9UmYVdlMaoy71nXTgT9IgCbzYFaYS8imk2GgnR1og4zMJYXl56Yw72dmpE0PszmIeyHfL+JQ7zSzHFvOjESIzCh5cT+QdVeb3foTtZ7seOSCmuKDpVrVBGsZl29Ac+s66MweGMvO7hcDhGmtFLIxnOYVxEnvNqPkBxAeYg5uV2JvK1vO7hMAC40BpdDQykX1669yltGGUOvQORa+Fqbby0LaKNItAN1nAjMEBF5N1VsAq4CLkW5mryWwvadLsebA2vBs5V+fWAaDXKGIW11nAlsFjnfzi9Hpm68BIwtpXTyxsFtwIPWsO9gHt0HvudNvvzfOR+d6dqW1LaV2IM2/sxqa8zkd/Rv2OMk0EYb+9lQLcI51hWfvSjtfzsZ/7HwNq1FYwf34dstuWjj9ZQX78vH37Y0Td+7LGfcfbZhWdTt4IqZETvSEyPqTzQnTBiBHZ6ey2oP7R2cvmhssB5IDJYBoO+IL976P2AO+xB4KLyppZ5UWsxz2s7an+jiVLlgz4F4wjwsgHoB6rw9PYiiaNYfQjG4+HldOpEmlAQdciQ1xuYB110jGEjGfphHpjb0NTjtBiSU5iIzj7W+EPUilBleZnAOygusUbN/Bx653yvQ2fMA2UH37hiHDX8Xymn2SK1PIE8BHZmK4txqMz53okcgEz5yALn4/Bh6SYZORswh7lS/vdqpL9BhChTPHuxHGaelmvVhyZkLUC1Itq1oOA3SOdAZ8wh3p6fDw37E7IWFJGvheuA31pjQWtVoI0i0+XW8DqgfxGpdq1lBOa75eV0ZCpgELWYaIKXN5C/W9IZBXwbI9H7SY7XaYy070GY9LmIDnTRMGbMSg480H9k+MtfduKOO3qEvGMbCxfuzlNP+csz9thjC3V176FatmNKiGrERCxtR+PloH+Wxw1mI2vPnsJEwCJErQbORTpXZoO252ehO2Ke9ztbF6aDerRUM8wP9SJQbQ12xESp7JoyC90Ts97sb9AwUJHWnkVviDisIxNgxWmmU5+jNqKOE5HerI1APxyiiEv6qQncEDLAfTjsGvq+Oq4GEXF4h0phEERDLQ9h8tq9dAfmu0XtYcxFelmfoFYYBFExHnnoC4rYbMOhkq0sRnpZp+DwZGmnFzlfYPKxS/nf65H+BhGj4GHMRumlGyadJlcOxRxskQNjEMTVGXoCpjbCy+HkmI9bF7IYWRA9TRH9WlBNzoBt6TtNDNAyetmMNhGHoAj7CCUdYGVHueSTkqAAACAASURBVAYQ0gCark0H8UC0Kagdbw1vwkSmot/vWsc7mGhUvqwhhu9cuenQIcu0acvZcUf/2feuu3rwwgvhZ8Z//GNHbrmlp2+sokIzdeq7dOkSR2sntRKzBr3ppgq4G3Sf8PfpCzHGqJdVwMAoCqIl6mlMWpIXN2KjczltZgB2xOFFzHM3Dm5Appvuj4zYeNDtMM9J+6x6O6gFpZxcPsQj31sTGCLvSJZFTBNWJkxmD3RAnqxiJI5IkYoOhzuRIfK9McaI/Ns6HB1QnL6ZDP2pEmkh0dGdK0CkhB0LoobFUMdQZN75B5iHTDzqUg5Nnho7DeYyHFHD0sQsZN75c8hCtpSvDqORykbHELIWNFyCCfN7WQVcpGT6SSQokxJ2DnItjNSyhqWJm4FvWmPPE/YMiABlvOdBXss5Wiq8oE2q64PIGoxblUyRigxXocvOt24PLNDSq4o2RcEPEFwXst1GJb8K9O69mepqf6Anm4UJE/rw6afS1/HFF+0YP74PW7b47epLL/2Aww8vmwhfHqglmP3TS1dgYXC9iD4w4PWus0GVTyG0ZRwQojpBNSwu+ieYKKcX19mgoo62uig3JYx/WRf6gQ5zcNcjs4teJ+rsIpf4+og43AT8tzW6H5ssK84hQwP3s01zvImF1Iqi8TgYgSwalHl3Dl0xGun+RaoY4xbyx8coNgP9MMo7XsbicLJvxOFQtFikWeACnJiVThxTGIw0hu5iIn2s1/YDEYUyhWZO2kH8q4oyTdb6I9dClbaK9bVpwnaz9bosMFDJovFIUaZIPsgxMFtDX++AhrORylprMfUtdtF4pCh4FukYqAQe0DLPPKjo8jVk0XgczAT+yxrri1EIa8atCwna7xYpqdbY1plI8L6zXXPyyWs44wx/ZtOaNRVUV8t6kSlTerNihT+7+Lvf/YyBA1eVfZ55MAaE4mmAyIduirbamQeTQNlF4xGjwkQ+hoK2nK26N0ZwwfshuUXj6r2yTTEvVJjIxyzQlrNVn4h0jJjsIlS5NaADibOhoaZjYNX/2a7HvYlq4EfWa5axg1AriAeHDW69iP0BTqa+eVN0w5bsa71mCbXcWuYZ5ofpg2IfzM2m6LAXYCRug3Pha3GIqNCsBRweR3ozurKVRTiuEegEhi2NV8HZvvKSUwpHEb4WNGYt6OYQvjgM1ynpYYsFZfoyzLCGuwKLtOsQ0RBadKmkhy0uJiNVbg7B42HVQc4ft+iymD4qpUY1H1jEfneW9kuNjwPL+WNks5Ox35WWZ4GH2NZh/StDVdUKDjjA/7V8+eWdmDdvmy7Bww/vxm9+4xd92333LdTXR10XEoZqwBx+11gXrgbtFfm4FanC9yzGK58AlJseJkU+QLvp5zpMhW8WKNvBEBMqKILtGoHaNQL1HoREW4vvB9N64u2sPpa1ZAKsOM3N1PNN6jkeqLHetQnoF4nEbb7U8BqKq6zRCrI8yBR2weFyTPGdl5V0EHmW8eKwGLjLGjVpAotpx8aQQrODmRrJ/PKlB1VIT81RwBRmNRVyiVz4G3CIuNAsJakoY2TYB/TdMJ74dhiJWyn3TOLknscSrHIzTTcXNYr0oBlRSNzmi5viFqRyM0TD+bopHVbWhQxPksRtjnqRmzQcoU06rK0WtglTZP+Vihps73TokGXy5HeprPSffW+/fS9eemkn3n57B266yV8X0q6dZsqUd+naNUkBe7UCGU11BXn0PqD7YwSKvLgpl3HUhYSh/kCIyIdbL3IdUoXvFSKUuM2TaYSKfOgwFb55oO6LYnJhxGuIANTwArLqv5IsC8kGWm6X4/C3aCZXALXMwRxevPRiC48ipe0ayHB2ZBK3hdCFyzDpDF6OZym/RxaafUx7zkmcxO1QGjC55WutK6NZw2+RhWbP00MUhqakXI7MyT8e+D0mBdDLKuCcqCRu88WtFwlaC1dgCuptgZAXMF75ROF2uT8f6bWcjUnxtYsu71BEKOmeJ8oUtdrPmo4Yb+uDyCbDVypZv5eyHdC375dcc42sF6mt7cM11/Rl82b/8Wzo0A/5xjfirAsJQz2Mccx46YYR/7CdOSaNu6gu5mUnTOTjcczz0stnmLqQhEXzlPv3FSqHAzCReluFbylGKjtW4jdEABymg/BGH4ibBuFhMU5h3XUjZgiIporfRdaFjHUNsOQxmi8xnTntiNMJ1r+zKC5gPIlIVhU4vIfMkVeYg6SXdbTjXNd4SUlpxk3pyW8twAVx14WE4aZYBa2FE6yXrsMYU4lcC27Kmx1x6oyJ8Hh5A3lwSBIzkBGnAzCRHS8PKalomLIdccYZ/+a00/z+yNWr24u6kCOP/JwLL0zmVutyFVLk40hkXcj1oH4TzZQKRYWJfJxAgAofqHeimFXhhEoTn2D9e5N5ndoYxaxykQxDpLkRTM78/HeQedvJwmlqBEOuRjBPUCu6kyYLh7dQItxqM4lakbedLByWQM4aHI1iMBOil/VMaRuo/PLzp8QhcVsICoJUbmyGxCFxWyAOuWtwNmLqQhIrcau27Xe5anCSv9+llISqqhX06RN+ZOjevYHJk98lk0lOFrdEhYl8eHkRmWqfMFSYyIeXOaAeiGhCRaKeoeUanMtA/T2K2bREUgwRcFhDhvMITm0wqk5OG8iTdXiV8LzB9+nA+SSpLiSMWh6EUFWyZzg4KYVmLdCdMUhPTROzqBVKNikpPpRJmwlTLGpLcs/XYKRkg7hFwa+inEwxqObUjtDGeJcqYpR0zxNlUuVy7Xf9FTFKuqdExg47ZJkyZTkdOki170zG1IXssksig5QW6m1gWMjFNcDZboF7wlFBIh9N/B0j8d4WmAShIkIPgLoz5FrkJMcQAciyN7ImBOBDOomUpyTTK2T8VcYJhYlkYvqg9Ay5+mzi6kLC2MiOyL4CTTwd4UxS2iiupGroWnDrMNoCOyJrKZp4OsJ5tJbumJQsmy1E3Mm+lexF8H73ETLFN2U7Zs2a9jQ2yuNY165b2X//2EXfCiHs7PMWsjlpkukdMv5nULkyXpJEJUZsKIinI5xHiyTHEDGSqreHXO3DRhJjveWkjlNAKGg1cSp1XBrldIpGMR44KeTqeBzRDCeJKDZzF7BPyPW7cEIfOCkpTYxFSog3X9Ph6yRp3IWUEG/iTh2+ThKDK5u8CCkhDqYW7xEdfC1RuLLJYfWO+0Ib2e9SWs2aNe2prd2XbED70zVrKqip2Red/BwKQB+N6Q0TxDEkPi2rCT0Mk2IfxFDQtgJqUpmFac4YxE2gbQXU2EiGIRIuqeplAI5QbUoWDj3RgTKS29BMxxGqTcnC4Ti0kJH0ksF0jw/zriaDOkYhZZO9dAfm4wilmpQUAFxJVSfHSzKYDtm2JGKi0EYZ5ec5XtIN0ycl6WvhVqRsspfDkDKciUK7XdUJ6Kruob+GC6OZUUpcZLNQXb1vYFf1Jv70p51ZsGCPCGdVDLobQQ2b/VSDtlWbEoY+FHLW8Lo94fS+0cynWHQ/pGyyF7cPlk6E0yYZhsgabkBKqv6TIKlGh69HM6kCMYfZB5CpD3a+sjG6nJxGV3wY4yJINtn+PfZGdhlNDvV8Cy1kk7dgmtV5OZY246lJiRJN81qwD+f2WtgDuFsndC1o82ydbg2HrQUnijkVgzbyvRdawx8hpYkv1bmNrri5Afi2NRa0392qZd+mlO2Iu+7qwYsv+o8CvXptpn17fwjkttv25o03OkU5tQLQbt8QEW21n5PGgYkOSxeKGd0ZIzlsH87t36MrsMBtcphA9AHIiGoW2VOpJaMrMuI3RBxOB0Zao2up4MeY5ixeOgEPcWMiQ+8OZiP38hqmkd6vrfGgzt5JQGHSN2wZyUXswDGYPE8vp1GXwMIth85kWYAx+rxcC5yGlGMdT11o6k3KVxDXqLgTWRuyGHOItHP4gzp7x442tRQPINfCWOBU5FoYq8PT0GJDG3lbu1dBU+F6kMrNXRr6RDG3QtBwOlK3fy3m+2NLE3cCHtKmtidlO+Mvf+nMHXf08I3tsEOWGTPe5rLL/OUUDQ2Ka6/ty/r1iQxYXg781Bp7B9O64H5rvAewEHRQbVTc3AZ8zRr7DUaG2Ba8+S6J6QzvRTf1JLJlk6dinuu2NPEw0OdGMbNcxGuITKIXRpnJ60nUKC6imn9huszaDWYOZX0yrLhm6jgRqLJGjZSvwxd0YAiyUKsfDhdHMr98cRgDnGGNvg38gmv5nAznYhRdtqGZSr3oOBo3c5EPlMdwmOlKE19uXcuguZ/J9CAlxXAVcnN9G7hEESrTPUWbDSpJzEGuhceBm0KkiTPAfE1y1oI2RZeLkJvrRAW/d6WJb7GudQUW6dypIpGiTSHvPdj7HQxW8B7GmWWr3BwC3BTB9FIiZO3aCqqr+5LN+oOo11yzgr59N3HOOas44YR1vmsff9yBSZOSVtKoj0Q6jBuA80B9BgxHeuJPxCj4JQh9EbJh8yrgIlBfEixNfC3on0QxuwKYQVDDZqhzpYkHIZ02c0EfGMXkwojPELmd9jSyEJOn7+VGat1mTw5hDWaG4RC7FQfAZPZAswCZyjQcx12A4/iE4AYzt1DPf5R/knngEFRotpmMRza5hleQBld7sizEEZ9jPDgMAwZao+/TwbMAa5kH3Ge9Zg8aWMDiQBWblK8Q2kQxbe90k6TqegBFoEx3BbBQh6u0RYo2RsZ51vD7wCC3l0WTNLEt07078IAOVnSKg5uBb1pjz+J/Xl0NoknsURgJy9hxa28eRH43ZirTGd4rTWzvd7/Q8pmW0kbRGiZO3JfVq/2ZPSefvIaf/MQ0N1QKamreY6+9tvhe84c/dOOhh3aLbK650TtBYObBGFDPm/9VGzFOG1v6qx7098s9w/zQBwIzrcEsMBCU26RWvY3s6+NmkGi78XZM6DMJyi6CAdtkk9XjSMeG+znq2Jw28RkiHzEV6T18CRjnG3EIs+JuxyFWKw6HDA3cj/Qe3onDfOu1z6LEQb+SLIu5Tnj6osUx3kNkB/grqeGv1mtn4m6cHnoTrgATHU5gzmMjGQYwDn/72k6MAP5hvfZElob2gEn5CqAJWQtwlbLC88qokth9aHoB98RdL6KDPemNmM7p9kF3JHItnIB0OkSOhrNBNFf9BDhXeRw7bjf4gcjeG1drGeWNgynA96wx4dhRJnIelGo2V8vIVkob5N579+TZZ/06Bb17b6a62t/PuUuXrUyZspyKCv9X4aabevHmm4nI1psL4gz2GKJxqnoNmbbqGuY6ZqeNrsSk29pnsDpQVuNUtRiTuu5lN+CB+FPNdNAZzG2cquzGqVXAn62xoMhWZMRjiBiJW7u2YB3t6I/DFvF6hyArrjOwACfW0Hs1Mp/6DRCpPwbNROD31uiBfMnNpZ9a3rgqEKLQ7GFqmRPwek1HBmNSCbycSZ2wxqNjepMKhKgfmkBNQG+BMYR5aupwSIinJiUG7kLWFvwamB3y+sHIbuSnQ3wy3bpZEUXUFtQqmeqKonkt2N3I6zTxyXS7Erd2LZ3GdIAXPQlUcDdyBczT4X0Byo4Orh/agDGmNtuvV/AEpqDdS2dgsZumltJGef31Tsyd63egd+iQZerU5ey4o2zNdeihGxk69EPf2JYtiqqqvmzcGOfZVw8HkZWyEhgEKkBsWM3GRAS99ATudYvd4+JWEFkpzyAj4k1cimlq6OV4zFkwJnR7CMkuQv2PfL1qAAaA6Gl3BWg7HTkSojdEwiRuFSOYIDb0bfRImBXncBxSbWkTcC6O2NCb3pOlPecBH1tXBlPH+aWfZB4ES9yuQHohtzGWtZicSX+XVM2M2KSJNzIbqTDzB3JJeTq8jhI9X4ynJunSxCklJ0TidgVwoZIeagAUrMM81G0HynQtc3Wj4jakxO1TIFTkmlHwOtI51A64V4c3QSwbOSRur1dS/KMZBQ8hPYPdMdLEkZ/ctBH+CJJ0H6qk+IeXccCfrLHDidFrmdI61q9vx7hxfWls9H8Vrrrqfb72teAjA8CgQR/zne/4yxNWruzI5Mlxtf3RhyE7j7tp9OrfAW9oYjiw3Bo7jTDHbdkJlLhdDZwLKqRhs9qEibzaH1gN6Lj6SU0DUacrs4t8qBXI+kBX/UxH/sWK1hAx+ff3Ije22dQKa9nPUMKtOIf/LNkc82FKUzhObGwjcXg153vHswrFudhSjZo5kUsTB0vcNpBhAE4LHeAdXkQaYvFIE9cRVmg2EEdIYvoxUZ8QT00y5VhTSo82hzx7LTQCA5R85vhQ8CIwwRruiPFgR7oWtHlGDrKGVwMDlaxR86FMQ9kHrOGewH0xpJoFSdwGPXOCuBzptQxyHJUV3SxXKva725X8O/tQ7ncP+d0bpXP3RkpJIFpDff2+fPihP4HjpJPWcuaZn+R8byYD9fXvsttufr/fk09249FHo85s0mGZB9WgZOaBD/UZxoFpO22uB31MqWaYHzpIuTQLnA/qw4A3eFBvAKOswQymziLiflL6VOBKa9D9OyuZXeRDPQIi68XtBxOtNHG0hshS6pGh/tfokqfspRPoqTepRU5ox+BSo9jCnUiJ28U43J3XHWp5CumpNws8KmnicInbKmpE5CnsHtdhFGu87E+U9SIOB6IDC83OwxGRp2AqAz01p1LHFSWYYUrCcSVugzbXsUpGYcOYjvTUB6UWlQ1X4tZee1ngPGX6beTDUKSnPiiVtmyESNyuwxiFLWyuoExkuh9Smrg6YmniOuR+FxR5CkQ1p7r4onEK07Nm31JMMCUaHnxwD55+uqtvbM89tzB+/IqQd/jp1q2RyZPfJZPxB2anTevNsmWRdjOYA8Jh+ltkn6IQ1MtIT70b/dS5GnyWEB3WQPs6UE/mdw91F1h1wKaf1DzQEZ2rdZjDdDio8OwiP1eCVQdsHEBO6+ZWGNEZIkbi1i4ENrnJo0WefjgODxNsxS3kdspvxdVxDcESt3ZucktMAFG7cBjr813QreZ2gmQ9nYKkIjUdGAzYHoT+OAxu1ezywSGs0GwijqjFCaeKYE+N5roEShOnlJ4guecnkOkHobipW4Ey3Zryy3S7tQNBa2Gygt/lex9XmljKdMNULcP/JSdE4haMxG2+mytu2pOU6TYpWmX3WmpT7D/WGt4I9FMyrSMUV5rY7p/SJE2c0IZqKV6WLt2RW27x+y0rKjRTpiynS5fGvO9zxBGfM2SI37e2ZUuG8eP7sGlTFEc5PRhECvkq4EJQuTMP/NwI2LULfZFN+MrFjci02Rcw7SIKIUia+MdE0k9KN6nwyewiVO7sIh9qM8ZpY0sTV4E+uTUzLIRoDJEwiVvlkbgthO4hVtxHZbbiHI5GiyY2mzEHb/uDbOleTaF3O6dyJHWcU/wk8/rZw5CFZmHqZLkZxydufxE77eM2HA4veo75cQuy0OxZDhbqZC3j8DLy4JAsaeKUkuNK3NrSqB8AF4TVhYShCJfp1vJ7WmpmAt+wxp6jiKZbKkDNCXPoXahlQWTJcCVuFyIlbmcpqU7WIooQme4ySxNrV/6YgNRdJdXJ8uFqZEO1o0lkQ7UULxs2tGPs2L40NPjt6lGjPuDwwzcWfL9LLvmQo4/2B/qWL9+BGTN6tWqeLaMPQfbqMZkHzRK3+aJcNSdsNaezQIfXppYEfSYwwhq0JG7zRTX1k7Id6ZNB2wp5paYehKhOkDpZHijTK85PBrg/KmniaNp0NrAPMtSzjloWF3W/UWxmImeyVYTZszh0CFTeKg27YqcMKJZRKzaJ/HB4H4dTsQ8QOncudytRKDairRS3DH+mRsh65kcNz+BwBrILdfmKXK9jJ77kJUxR1jba82v6Ffn3c7iJOj5BixSdnrRQJ5DS9nDrHr5Epns+HyBxmxcKntXwE4xn30vZ1oKbWvYK8vdY4tYaFMNMjJMkyrXQlGrg7WuSRXZnLoSRyMgzGIOkhVzwogna7z5TJh2kYBRs1nAmYHsosxo6BilvpSSDjz7qwKBB/nN6ZaXmlFNy1XSH01QvYsv/AnzxRbtA5a0SsQeyFuF9UPlnHvhQa91mgHaUdZNR0QpS3ioJ7ZDPyb+Beq+426nXQJ+GSYv1UsY0M90Bk0pu/x6/c5svFoFaZCqZ6Gpd6En5npPbfjomH+x5jByrLVuZkpJSXjZg6oP2Q9apfNW5A5PWVE24nGJKSkrpGYBJ/XiKGCWcy0xfjOTzRowxXzSvvPJKrdbaKcWkSo3W+uajjjrKNiJSUuLmXUyt2zHxNTRMSUlJSUlJSUlJSfnKkhoiKSkpKSkpKSkpKSmRkxoiKSkpKSkpKSkpKSmRkxoiKSkpKSkpKSkpKSmRkxoiKSkpKSkpKSkpKSmRkxoiKSkpKSkpKSkpKSmRkxoiKSkpKSkpKSkpKSmRE01DQ4DJ9KBBNMfagMPqRNwvXyaxD41Wx9xK/k0VnxV1P4d9sQ3C1twvv59ZAfQOuPIhDptiv1++TKcTG9kj4Mp7OGRLdj+Hdym043xKm8Dt5h303f1IyY65rbnfh4ryrQVtetEErgVF4Wuh1Pcr4Od2w/znpUHByiLv1x3ZpKvo+xXwc/cEdrSGNyiK25809EA2lyz6finRsWZNBV9+6T8ydOyYZdddC2zkneN+lZVZdtmluPvlj+4FtLcG14EqssFpqe+X189UBPfLWwWq8Fb36AymF0aJ7pf3z60EgrqerwBVRBPbUt+vMKIzRBo5Avg1poliE+tw+BZOgY3c6jmMBl7A/2BuBE6k3A/mRi4Can1jm3gDh6Nx+KKge9XRH81Ca/QTNvENKKshshWHGcB/WlceBs4u4o7XI7uuvgx8r4h75c9GFPAY8HXfuKIKuK6gezlk2Mh/Az+0rtwNDCl+kikJZytwA/Aza/wR4Kwi7jcNuMoae4VyrwXDEuAga2wcMLWQm2jjGPkv4EfWpXkKBhc/vbzYBfP36uKdkoafKrN/5I02BsjLyIPHZcCtrZplyxyB+Ty8+93nGo5U8FYhN9JwKPACfsNmK6bT+h9aO9GU8vLBBx255JKv0di47avQoUOWefPe5GtfK+zI8NFHHRg48CDWr992dFMKbrjhHY4/fl3J5hzCGch1sxL0N0EV2Cpe/wD4HX4n7AbgKKCMhojSoKuBi6wLT4P+IahCW9NfC0yxxt4GvlXkBPNlK7AAOMYanwFcXcT97gbOscYeA35SxL0KJrrUrFoeA262RrsCi3DokPd9HDqTZTHSOzQBhz+2bpJ5UQ/83ho7BJhV0F0c9kfzS2tUoxiCw4etmF8+aDoyGHjPGj8LhxEF3cnhVOAKa/QzoD8OW4qeYX4/ewMZ+mF7rjWTqC/44FeDNEKWYg4tKdspykS6BmO6vHo5U8PIQu6l4RRgtDW8ARioYHPxs2wZZbpD9wPhDKnX8P0Cb1eNNELeAi4vcnp5o8wmfokc5i4Ne+d7H23eczfSCPk1cFurJpkHCh4HbrSGdwIe0NAx3/u4kanFyOhKjUqNkDbBYYdtZOhQ/5a+ZUuGsWP78sUX7ULeJWlsVIwb19dnhAAMHLgqCiMEULcBD1qDvYB73UhDnug9MIdo+/w5HNQ/WzPDPBkB/N0aOwEYX9ht9LeBOmtwE9AP1Poi55YnqgEYiHRYjwb908LupUcijZCVwCBjuJWfaGtEenAt8Gdr9EiMFzFfZmN7wM0D+fpWzCx/HLK05zzgI+vKEOo4P697zKIjZnPpYl2ZRm1hXr+iGctaoD8IY+FGHI7I6x4OPYF78Xv9QDG84ChXsdTwGvLwV0GWhTjsmtc96jkec/jysokM5xYc5UppcyhYBwxAroUZmvzWgoaewH3YawGGKniz9bNsGQWvIyOTFcCDmvzWgobjMEa5l01APwWft36WLaPMs9F20uyGOcTne3IbhYxyrQAuVNGlWVYBf7LGvkVh0drZyCjXUwXeIyVmBg36mO98x382XbGiI5MmBWVxBjNzZk9ee62Tb+zggzcycuQHJZljngwHsbefhllveaAzGCNkT+vCHaDmt3Zy+aE2YQ7x9t5eC/qk/O6huwGLkKllV4L6a2tnmB9qOXCxPQjMA71PfvfQhwHTrcFG4JzCo1zFE60hMpQGzIZvh96uwBFpQhKHi0Ec9lcBA4uqCSiW8axCMRATHtuGZg4TxaYhWcPNwDet0RfoYaV8lRuHF4EJ1mhHTJTKNpLs91YAC5EHnNnUCq9JeXGYi3m4eQk2kuR7dyfLA8gDzkhqhNckZTtFwYtIj1hHYJGWDgMfbl3Ig8i1MFfBA6WbZcsoc4C3N/SewH26hbWg3cM+ci1cpqQHsdxcDtgb+nHYabEBaOPcsg/qDcAAVda0Dz/KbOgDAHtDH6WlkSTQJn3kAmt4FSbCVmgKSUqMZDJQX/8uu+/ur+N48snuPProLi2+/7nndmbhwt19YzvttJVp096lffsoyxeVyXaQEd7poL+Txw0cwD7sv4HMqigz6g3gUmvQNZK0bSRZaPewj33YfxjU3FLNMD/UwxhnhRfXSNK2kWShO0NgdlE1qP8r1QzzIXrVLIcVwCD8XikTRncCi34M9RwCzLRGs8B5OHxc4lm2TC1PISM5ndjKYm4UH+w2HM4Chlmja6mgv2uoRYvDdOBRa3R/4I4W3jkJmff+Gl2Kyk8sBcOQnudTcYSHeBsOGeB+ZJHWIhzuLu30UtoAM4D/scbyWQv1yPSn15GRuqgYDtgpDqcga1eacY2Uu5DpT4sV3Fna6bWMW9jfH7BTHMZrmTbWjIadMZ5KO/2pSslofNlxi+IvJGC/08FFs2BefAgyHz8LnK9kND6lDdCtWyOTJi0nk/EbDtOm9eatt8KPDKtWdcBx9kVb9saECe+x115lzfgMQb2MqY3w0h5YCLp7+Pv0CZiaNS9uSqmKIfNAzcNEsb24aWM619n4KsBOf3oHGZ2IitHAX6yxoLQxmznI7KLfICMkZSce+V6HJciHrLHigupFptPJrQvx58kqaWI9bgAAHyhJREFU6nFEvUaU1CDzdA9lvcgNNkxkP+SmrlFcRDX/KsP88kFjPG/2z+/nRqAkDj8GxlijG4B+jC5caagkOO7Pl0pH03D4bsi7qjAFn17eBn5R4tmltAFUjrWgQ74TGn4AXGMNb8SkMsWyFhSha2GqDi+avxZZmBhUrxEZCpYh/+4ZYL6rIhXEHKCvNfY4cFOJp5c3yhSt2zWEXYGFGrnfaajERHjtupCJyhT4prRRjjhiA7/4hd+O3LIlQ3V1XzZtksexrVsV48b14bPP/HUh/fuv5gc/iKIuJJRZGEELL72RKZUueneCo62Xglpa6skVwAjgH9ZY0DPdRR8NTLYGG4Dz3GhRDKjNBDttqkCHFJvrXwDnWYOrgItARZdd5BJfH5EeXAU8b40eDUwUr93IbcDB1ugzHMSk8kwuT0w62EAQEZlh1DHQN3I77dnKfIzHzsuN1AovbLQ4rMGkENgRmVtw+Ib12j0xYUlZaOYIL2y0OLyKfIAYT80UdrFeewwmTOxlMxn64YgFnfIVQdFcO2WvhZnaSqfUzd4zsbkOV3JzixQFryHVU5rqRXxrQRvvWb312s1AfyU3t0hRJrphRyd3J6BexBUXsIsu3wcGRVgXEsYYZEQm6FADJtXiP6yxZwjaG1PaHIMHf8S3v+1fVsuXV3L99bJe5NZb9+bvf+/sGzvggC8ZNSrSupAAVKjIh1v87EFnMOmitvNgIah7yjTBPFFhIh8TQVtRbt0Vk45uOw+uBmWfZSNGhYp8gLai3PpQpGPGPcuq6LOLiNMQGUoD7TgPWfU/Boczmv9VxzmYVC4vq4Fz6ZeAPFmTFnYRtra+Zi4T+Vrzvz9iBlJq7SVkqDIeHJ5HHswrgcVcx07ua8IKze7EEXnp8eBwK0HKHlu4h6YceYfumAeKP4dScSU1Ii895SuGMlKpdsF2JaZeZCdolrgNWgt3K5PuFzvKHGjtGpVewL1N9SK6OZ9YFF2OVjLcHxcjCVa5aX52aggtulTwaVlnlweK0PrIq7QnzUMbI9iWFv0EODetC9k+yGRg4sR3RR+RRx/dhcce2+Yj+NOfdmb+fH8rnx13zDJt2nI6dozcaR2AWkew4M0M0F6Rj/HIdMqgaGdMqNeRNSpu3Z926/50mArfEuCWMk8wT1SoyAdo12mjw1T4HFD/W+YJhhJvZ/UJgXl1phDIoTcTOQDN7db1LHB+BBK3+eME5tV1ZiuLcajE4XRkYdQ62kUgcVsY04DfWmMH8GXzl7sOE7b08gYRyHoWyAikssfp1HEpzaoSAYVmtcyJYnIpbYLrkH0rDmDbg74WWXSZRLnnYci+FadhCqbD1sIjShZAxoZqlsQUql2OhpM0hBVd1igikXTPC2VUu+zDlwLmadhH+79fTWhgiCJB+11Kq+nevZHJk98NrBd5991KVq/uQE2NrAsZO/Zf7LNP+XoEF44KcqYawRt0F9BBAhNNEreRqPDlh7qDEJEP1wgJUuFz67+ikbjNkzCRjybHWpAK39PIXiiREq8hAuDwMIgDYHdgPlt5BNcD6WEaDk9GMrfCqAZspYHDMVZ0kKznECaIsGa8mFSzC5Cb3gAc5hBWaJY0iVuHdWQC5Fg103G4AzwRN8M7VMZWaJaSQNxUniGAnQMxQMNcpNzzF5i6kEStBVdydyBS5eZ6zKHXLrp8l/iKLkNxmwDaEqEZzLP1fmTR5W9JoMStMo0y7T4m3TDRtYeRCm3XFdrIMaVt8K1vfc6QIf5MmC+/zDBuXF/GjevDunX+upCf//xTTjklMtG3QrgRExnwsj9mbQbVhVwB6m9RTKxARiCdNqdgxErs9hANQP8oJW7zQ23CpKdusC5Ug55DsArfOUU0ciwp8RsiAN25EpkGcCwm3O7leXqI9KFk4DRLNdppAOdgNhovt+Lwq0jmVSgOq8lwLjINYBjy+3IpDnEWmoVTw0tIZY+OyC7pptCsqoyd7FPaJMpNicGk+HgZilwLlykTHUwcynQXt2unOiANjgaMNGysVbBhKLgHI8ntZS8Q0u+rMP1CkpC/EsRVyP3uexjHlZegFMGU7YhLLvmQo4/214ssW7YDf/ubvy5kv/2+ZPTolVFOrQCUxhxw37Mu/BSpwvcQKDvLJSGoz4GzkCIfQ5B1IeNBRa7Clx/qTeSzPYNUazXZRTHVhXhJhiEyirCqfy9rqWBALBK3+eKYwkhyF0a+SpcwRYaEUMMzyOJVm4U43BPBbIrHYSZS2cOPYoxbH5OSIlDwLC3LIC5SsqA6USijctOS8+PaOCRuC2Qk5HR+ZIHzlBQQSQzKRKf6IesjvazF9D1J7n6X0moyGZg06T122SX8Y95hB1MXUlmZVLsaQK0lWPDGyzvEqMKXH+o1csicuzwB3BDBZFqBChL5sJkCKhEqfMkwRACcnFKRcUvc5o/D4xAi3wsbaBejxG1hTCJcKnIZOySl0CwnYcoeTSyhVshqpqTYTIHQdNC2JPc8hPC18Bhwc4RzKQrV3HcgNAWuXhGrpHteqNyHMg0MVtLDnLId0r17Q2C9SBPXXruCPn2SVBcShnoB2SC5CdfZHJfEbSGoOYQ3ov0AuCBhdSFhBIl8NPEcLTvYIiM5hghAho8IVgZ5k4NEDmJyUaEG0xImiKZ7ycTUi4QVSP6Sa0XhaFL5grBuyoobiV/WMyXhuCk+YXqZd8QtcVsAXyC7fDdxUwIkbvNlLcYgsdmCqd9pK3xI8H63jLQu5CtF166NVFTI5bfLLg2cfPLaGGZUNGFnnz+CeiXSmbSOsLPPAlCxq/DlyRbCn/e3grJTjmMjOYbIVLqR5X5kYRPA11kaamknC4fD0aFFkv1xRD5zMnEYgJRN3nbVEcWhSeV64FuBVzS/xBHFoSkpPkIkVZuo0VKFJKlMA44MuXa7loXSicOVTb4XI0tp0wGjQGULgyQOva1APWi/OxCpNJSynfLllxnGju3Lli3yOPbvf7fn5pt7xjCrYtD7Ee4IOAn04ChnUzz6FMLTs64E/Z0oZ9MKxiNVTpuYBdqWno+NpBgiis2BMpJeJuDww6gmVBROqIxkE0aL2mHfyOZUDBMDZSS9GC3qG0N/z2RgZJNtpR0v+2MUMVJSAtHmO9LiWtDhaz4RaCPZa2vle9mPtrEWaiDnPnAKMDqiuRRFDtlkL+O17L2Qsh1y3XVGsjeMxYt347e/7R7hjIpBu5K9omGzl9tA24IMCUPvTbDKaROmQTLaFiBKGPpYcgtd7AHc4zabjJ1ETAKHq5AyknY6jZFqdNg9mkkVxS/B08TQYP8e3YD5OFSQRBwq2crDSNlk+/c4jPUJLtiaxD4Yz6n3gaIxaR1e+lEnlLRSUtCmieFDyEiBvRYOJbwuLHY09EZuroFrQSdQurcJDSciZZM3IqWJp7rd4pPKleS532lzYEjZTlmyZBeWLNnFN9aly1aUdQyeNq03H37YMcKZFcx0ZOaB/Z2uBBaAtpvpJQTdDlMbsqt1wf49eoPob5cg9K6Yps72GdP+Pf4fMCaSKbVA/IZIPUcBk63RzWT4IbJDcQ/gQbfDd7Jw+AVGqtfL+1RwOLLBzPdIUKGQxc1IGclnqOAIpDTxCOrE7xw/DhU0sgDTj8bLzWT4GXZetuZWHPE7p6TcCHzDGnsWAtfCMG2kfhOFNpvRA8i1MAsje2vXKNyi4T+imFshaJOKNR+ZyjQCKdPdHhOlSpwbWcNRwFRreDMm+nGfNb4n8KAOTt9KaeOsWNGR66/v5RvLZGDatHc455zVvvHPP29HVVUfGhqSmHWogxo2r8V81+1u3YeSXFEMB9P8z8trmDYSdu3L2aBtOdwEoBVwF1I2eRFwAlKaeBLo70UwsZzEe6B36EqWRUiN5tHU8FfMJvNP69oPkJr48VLPIcBN1mgjcA7VfIBRebELWqtwODmK6eVNHWdjeiR4Mb0UjGKZlCbWzGYifaKZYN5Mxhh7Xl4Gqlxp4knWtUrgARwS6qlJiRpt9OSHW8NrgPOV2ZQuQBZ3z9Umvz9JTESuhVcwUr3PImW6KzGHeDsiGhtuXcj9mJ4hXu5R5vA+CynT3ZvcKXWRo6ErBO53Y5TpKzIC+Id17USkoZXSxtmyJUNVVV+++MJvY1588YccffTnjBr1Pocd5tdjWLq0E7Nn2+fLuNG9MP197GjrRaCWY5qp2lLal4A+L5r55Ys+ARhrDW7AdID/EFMnaEsT3wT6mxFMrhDGIBs2u8qO6jVkBKQCeBD0LsRI3JGFu0AcYh/BYTYADhvI0A9pxU2kXmyu8TCdTmRZDNYhVlGDwx8BI02shMSn2VwdsbnGw0T2Q4sccY1iCI6rIGGkiW1vRle2sghHbK7x4PBj4Gpr1HSXdpo7rdcjJT4PAWaWeXYpbQAdHHrXwEUKVgAooyVvp2PtBCzQ8qAZC9qE3m2nzQZM08KmdKYgme4DSZbX8lrM7+JlGW79l9om0/2e9ZoztZGwTAqzkfvdEuBW8EkT2/tdnYbvl396KVFxww29eOst/5HhiCM+5+KLzZm9okIzadK7dO7sD1jOn78HzzzTNbJ55kZXAAsB+xB7E6j/Mf+rVmGEPuwmKHNA22nsMaF3J7gD/AhQriNcvQCimXYlsAh0Qpw2+miM48mL27NIuY5wdRsmbctLL+BeN5oSC/EZInVcBvzcGl2Brclfw2uoACsuy0KmiAUQPRu5FTjYGn2Kg7jeN1LLIkyBohezABbHHHq/nfZsZT6y0Ox6ai0ZyR5cC6IB4FGYXgvx4rAn5m9sf6+H4fCW53VZ2nMe0lNzMQ4J89SkRIluLkYUaT03KHjUGhuLbAB4JEadKlZ0czGiXAuKbRLiqrm7rlgLg7UZjxW31sNOY90E9FNskxB3u8EHeS1naIjda+kaRHYa60pMB/jmyJqC15HF9hWYFC07dz2lDfK733XjV7/yf5TdujWKXiJ7772ZCRP8GUFaQ13dvnz0USJ8HVOB71pjLyMiC+o3yAaArrCPDq/SjwSdwaR89rAu3A3KLg2Yhuwn1ZKwT0Torph9S2YXoezSgBHIflKnAZeVaXItEo8hEixx20CGATgBPR9quQ3zR/bSky2iGDlajMTthdboKtozkH4B+vBdGAm8ao0ez1LGl2eCefIR04FjrNEXCVJdMJ3tByK7Ao+mThRgRoepG1qAyav2MhcnoDnReMI9NW1Hmjil9FwH2PKMLyGLpHG7Xg9AFgFeoYlPpttNZQpaC79UZtyHglWY+hb7mTVbE99acCVuF2GMQy9XKPib/XoV/MzqiEk1i02aWJsc8+nWcCNwjgrQ+VdGAtV+ZvUE7m0L0sQp4bz/fkcmTfKLpSkFEya8x267yabkJ520lp///BPf2Pr17aip6cPWrXF+FQIlbk3mAWpLwBvGA/9njR0Ooa0OomI8Up1uKYGHctXktPnIujAAdFirg6i4m6DsItRs+VLV5LSxP6cbQMci8hG9IRImcasYS43wLnoZTpAVV5dTnrV8BEvcZlGcz3jxRTWM5ksynIvsClxLHSeVYZYtU8dpSIlb80V1xBfV4LAcqa6j0Mxz1ariwEFqZr9OlxxSng6/QR4QzPcz6dLEKSVHw6lIidt1QH8lH9oAqKAorivTrYlNprsGxPPkDYxaUyAKngIriuuuhTikiXNI3D6kcivWXIdsBtiSBHPZ0IRKuo9X8mDmZRh4oriGoO9nShthyxZFVVVfNm70J0AMGvQxxx0X3nD86qvf58AD/UeGv/61M7/8pe3Ej4pQidthoOzvrIsyNbPS8B4F+melnmF+BErcbgLOBWWf0VzUaoKdNreBjqmflB4F2H/DoH3Jg3oJhAO8PUbVLJcEc1mIIyIyBylx+wS1LchfOqwjE2DFaabjCG9+eTESt4uQBZ2TqBX51n5qeAMlNpMMmgVualF0TKIXOkDiVjEYR+Rb+3F4GNm8qBuNLOR24cEsLw4nAOOs0Y20ox+jRb61TTXyQHAY68WhLGU7RrseZ+TmOkRJB4gPBY8AtuepG7BQS29+WdFwPMESt/2UdIDY1IBb17aNIG9+FARJ3L4DXJLrTW6a0xBkZ+T+2tSRRM1sZFQpKFXFh2r2LgsD+DotI3YpbYCZM3vyz3/660IOOWQjw4aFNfE2dOiQZdq05ey4o//sO29eD154IepAnzZtFGSa4BxQMvPAh1qJySCxRT7uBh2x4I3ujknJsiVuLwP199zvVU8jU9FNb7XIpYl1UFTJjdQrmV3kZwbwP9ZYLP2kojVEjMStnYP/PsEKNJIawqy4+UzL2Uin1NyMzDt+loOFAk0wtdyBWQRe9sB4AKOJtxqJ2weRhWazqBUKNGFciZQmPoaPIuwK7BBeaDZBKNAEvb+RikBPzaU4ooYpZTvElbhdiNxcb1HwqzxvMxq5FoLqG8qGJnQtXKpMukFOVJPSn5QmHqllfUPZCJG4bQDOUzIlVKCalP4CvJZaSpOXDbcni11nswojemCnhApUk9Kfn/YYAzdx0sQp4Tz33M4sXuxvgdaly1amTl1ORUXLR5/evTdTXb3CN5bNwoQJffj000h9HfUEZR6EdyK3UEuAW6xBt75BR1T4opuirb2tC4tB3ZnnTRzgD9ZYxP2kdFO01a6zGQsqV3aRi3LVzQKliXNEU0pPdIZIsMRtFhiEIza+cBxmIItG92NTRFZcsMTtGuD8wLqQcIbjKRp1+TF1QvGpXARJ3L6K3PjCcdhEO/rjKRp1GUtdBF2BTV1IUKHZvThCkz+calaiAqSJ4a4EShOnlJ4gidvXKEA2VTWrkwiZ7ms15ZfpziFxu0iZovW8UMYxFLQWZmuZg1xyWpC4tUUyQlGEy3RrW+GwDOhgFb4sRrHMFgbIxc1Ir2XCG6qleFm1qgO1tfuiPStKKaipeY+99grOfg7i5JPXcNppfn/ZmjUVVFf3IZuNwn+pT0SeD1ylN9VS5oGXazBy1V6CFJ/KRZDEbYvRVj8qi3Gg22fXoaCj6ic1l6DsooKMIbUW4yxptC7MBB1ZP6loDJEwiVuoxRFWZUs0WXErrPGz3YhL+QiTuIWLcMR8cuM0aVSzybrbFByhRFFagiVuN9COfjjWfFpiAsuQuYgZNPOZLAyEUjMOWWi2jB2KUH+o5TGCPDVJkiZOKTk6uC/RBkwqUyGbK6pZr91HBrhfSwOh1FQhDZ6g+bSIgseRh+iuwKIIpImDJG4fw/QKKZR6ZEO1QyizNLFu6ksk97t6JeeTE7Vtv7O9lmdpU0eSkmC2blWMHduH9ev9GUADBqzmhBPWFXy/qqoV9Onj36Jffnkn7rmn3FndeneM0EWQxG3LmQc+VJjTZgzonxQ7w/zIR+I2X9QHBGfz3A66zP2k9FBM6qYXdz6q5RCbD/UcIf2kopImtvPjysMXHIfJPfbmH/+bYiVfHdbgcA7Gc+flYBwqCz5M50uWH2K8dV7+jiMiNPnh8CoOF2GaVnk5AfhTUfds+WdmMJr0dgjy10wQEZp877mQOvZB09c33sgPCFDpKQnT2JlN9MAuQs1wK9eKCE1+dOca1rAVk+/pvee3geeKm2hKUnELok9EroUlSjZSzQtlDuu9MQXSXn6ATMcsCdrIbu+NLMi+TcnNPl+uNbe21gLlWwsaDsJEV72/RxZT2F3g5mqkibU5KNRgpbxq2EfJw32pOAETvfFGcNYiIzR5oWCtNupsF1mXDtKwQ6EGc0p0vPpqJ/bffxP/v727jZGrKgM4/j/sQl8gBAqVVlPRUgsVKNLdBqSiEinvASWgxRYwxtgY+8GXD6BBOgUrpFETCFEsCtGIlKqxhtQGNeIbocStVQkVECgWGistQluaYqF7/HAXu525d3de79yd+f+SSXfOOff2yTade8/c5zxnxowDtyTjx+9nyZKtdZ1vwoRBbrnl2Yo0r+3bD+XVV3sq9h1povlUFoF4FkL1mQcHCc9AXAiUTzzOgLh26IlDK5xL5RPihyCUP6GpUlgHcQlQ/vTgHCqLTTRJHE/yWVn+ef99CNVnFx1sOUlBjfJU/feRPGVpqUByYVlPshmUaShSvl4ludk7AXi2zbEUzV0kefY3kHxQSsrHApKNzx6ick1Ap5hOkpKzh6S6Wd0GBga+RBH20koRQljR19dXdZqplJPNJJUlz2z3zuqSJEljVoxxxMp67TQ4OOgXXCq04alZk6lMO6rGn2n/pjRSuy2nMh2nGuOaHUgH+hj1VTv6OslmhFK3ejv1lV+e1uxACmwc9d37PM1QFc9x48Y9uG/fvt1UlvRvt9d6enrKU6qkZroOmFPHcZPf/GH4RORwkgVEtToCJyLSfJKyo2q+U4detVqFExF1t6Oo77reTXqp73f0/+0EZs+e/fLAwMANVBZ5aKsY41fnzJkz8kYlUmPeT7LZat16SfK0ljRwjucaCUDqECtI9oKpV72LzDrZvcBfGji+kWOlTrCVxq7vLzQrkALaQWO/m38Pf9Pf33/7wMDA4SR7B+W7qW+l/SGEW/v6+gq5bkUd5VsklRbrtTmfzfMkSZI63IYNG2YAC2OMJ4cQjs75r98ZY9wE/Ki/v7+uyn+SJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEnKX2h3AGpAiSMIzAPOJDIZmASMB/YA24DH6OFhvsIz7QxTkiRJKudEZCwqcSZwPXAx0FvFEY8DdwPfpcSuVoYmqXtFuAc4NaP7JeCCADHHkCSNOfFC4OYqBg4CO4FdwNPAY8CDELa3MDg1WTU3sSqKEkeRXOg/XOORJwPfAM4Crmh2WJIU4XjgGuCQEYadDfw+n4gkjVGTgL46jx2EeD9wPYQtTYxJLTLSBUNFcjOzgAFqn4QM57+3pFa5mtE/Y67NIxBJXesQ4CrgUYgntjsYjc4b07FgOcexn3XACaOMfB7YCGwCdrQ8Lkk6YFEVY66IMLHlkUjqdlOAu9odhEZnalbxBV7npyRpD2l2AiuAH1DihYN6SswALgA+A7y7lUFK6l4R3gtU8+3jkcBHgHtbG5GkDrQNWDrs/USS1POFwISU8WcnT0XCk3kEp/o4ESm6ZVxOZF5G7zP0cH5mVawSTwN3AHdQYgFknkeSGpGVcrUf6EkZ60REUq1egbCysjl+D/gjlZ81AP2AE5ECcyJSdJEbM3r2AJdUXZq3xCpgVbPCkiSAmJQM/2hK1xPAU8ClZe0fijAtJKmkktSgsB7i34FTUjon5R2NauMakSIrMR2YndF7GyWeyDMcSUpxKXB0Svt9wP0p7YcAH29pRJK6zaEZ7f/KNQrVzIlIsZ2X2dNLyuNJScpdVlrWKuDnJE9vy32ydeFI6i7xPOBdKR1vAI/mHIxqZGpWsZ2W0b6ZG/hnrpFIUpkIx5H+hcmGkKRlEWEtlalbMyPMDfCnVscoqWNMhnjrsPe9wEzgItK/WL8bgimgBedEpNiOzWj/R65RSFK6RaRfR4avR7uf9DUk1+JERFL1jgGuq2JcBH4ILGltOGoGU7OKLWuR1c5co5CkdNektEXgx8Pe/4L0z6yrIoxrSVSSutlvgWUQXm93IBqdE5Fi25vR7sVbUltFOJ30YhoPBw6kjgZ4jWStSLlJwMUtCk9S9zoH2ARxcbsD0eiciBRb1u7ob8k1CkmqlPY0BNLLhKdVz4Lshe6SVG4P8Ouy1+PAvpSxhwHfhnhZfuGpHk5Eim1rRvtplDgs10gkaUhM1oUsSOnaD/wkpf2XwPaU9ouGFrxL0miehzC/7HUKScWstSnjA/BNiN7rFpiL1Yvtd8CXU9onEDgfeCDneCQJ4EJgSkr7LuCmmH5MWqrpmxOa25oVmKRuE7ZAXAi8CBVf0k4HTgX+mntYqooTkSI7kj+wi73AhIq+yPWUWEuJwfwDk9TlslKqjgY+Xce5nIhIakDYCfEp0ndXn4ETkcLycVWRfYG9wD0ZvWcBN1Z9rhK93MS8ZoQlqXvFZJH5JU085ekxfdG7JNXiyIz2yblGoZo4ESm6Xr5GUnUmzVJK3E4p8z9fMgFZxpXARgb5YitClNRVFtD8yn1XN/l8krpKPA2YltGZtj5NBRHaHYCqsIzFRO4cYcQOYA3wCEmO5HgCU4nMBc4Fpg6N+xklLm9tsJI6WYT1wBkpXVuB/45yeADemdK+DZgW4I0Gw5M05sWFJBsSlnsCwqxh43qBtwHnASXgrWknA96RrCNREblGZCxYyncocRLwuYwRxwKfGnolMlaLSlK9IswkfRKyBzgxJH+Odo5NwKyy5inAfGBdw0FK6lQnQaz17uY3TkKKzdSssaLE54HFpNfLlqQ8fCKjfU01k5AhqzPa3VNEUjO9DHy23UFoZE5ExpISK4GzSWry12o7wW8bJdUnJteLRRnd99Vwqqyxl0U4qraoJCnV34APQniy3YFoZK4RGatKzAGuBD4AzKUyzS4CzwGPEFhD5AFKmYveJWlEMVlv9quUrv8AU0MNT2sjbATek9K1OMDKOkOU1BEy14iMZgvJWtnVwBoIbm8wBjgR6QSr6WETx9DDMUQmEniFw3iR69jd7tAkdYaYFL1IWwy6O8BTNZ7reJK1beVeCskXKJK6VpxEelGLNIPATmA7BO95xqD/ASns3lUw6Yj5AAAAAElFTkSuQmCC" - } - }, - "cell_type": "markdown", - "id": "b5ca8346", + "cell_type": "code", + "execution_count": null, + "id": "d350220b", "metadata": {}, + "outputs": [], "source": [ - "
\n", - "\n", - "
\n" + "answer = \"x\" # replace x with a, b, c, or d \n", + "alg_1_deps_check(answer)" ] }, { @@ -320,52 +376,22 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "f9f757a5", - "metadata": {}, - "outputs": [], - "source": [ - "using Distributed" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cb00fd41", - "metadata": {}, - "outputs": [], - "source": [ - "if procs() == workers()\n", - " addprocs(4)\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "e4697fda", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "matmul_dist_1! (generic function with 1 method)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "function matmul_dist_1!(C, A, B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", + " m = size(C,1)\n", + " n = size(C,2)\n", + " l = size(A,2)\n", + " @assert size(A,1) == m\n", + " @assert size(B,2) == n\n", + " @assert size(B,1) == l\n", " z = zero(eltype(C))\n", " @assert nworkers() == m*n\n", " iw = 0 \n", - " @sync for j in 1:l\n", + " @sync for j in 1:n\n", " for i in 1:m\n", " Ai = A[i,:]\n", " Bj = B[:,j]\n", @@ -373,8 +399,8 @@ " w = workers()[iw]\n", " ftr = @spawnat w begin\n", " Cij = z\n", - " for k in 1:n\n", - " Cij += Ai[k]*Bj[k]\n", + " for k in 1:l\n", + " @inbounds Cij += Ai[k]*Bj[k]\n", " end\n", " Cij\n", " end\n", @@ -382,7 +408,7 @@ " end\n", " end\n", " C\n", - " end" + "end" ] }, { @@ -395,21 +421,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "13920a31", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "using Test\n", "N = 2\n", @@ -426,7 +441,7 @@ "source": [ "### Performance\n", "\n", - "Let us study the (theoretical) performance of this algorithm. To this end, we will analyze if algorithm 1 is able to achieve the optimal parallel *speedup*. The parallel speedup on $P$ processes is defined as \n", + "Let us study the performance of this algorithm. To this end, we will analyze if algorithm 1 is able to achieve the optimal parallel *speedup*. The parallel speedup on $P$ processes is defined as \n", "\n", "$$\n", "S_P = \\frac{T_1}{T_P},\n", @@ -436,111 +451,108 @@ "\n", "$$\n", "S^{*}_p = P.\n", - "$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "df7aa7ed", - "metadata": {}, - "source": [ - "### Complexity of the sequential algorithm\n", + "$$\n", "\n", - "In order to analyze the speed up of the parallel algorithm, we first need to determine the time of the sequential implementation $T_1$. To this end, we will study the computational complexity of the sequential implementation. From now on, we assume that matrices A, B, and C are of size $N$ by $N$ for simplicity. Remember the three nested loops of the sequential implementation:\n", + "The ratio of the actual speedup over the optimal one is called the parallel efficiency\n", "\n", - "```julia\n", - "for j in 1:N\n", - " for i in 1:N\n", - " Cij = z\n", - " for k in 1:N\n", - " @inbounds Cij += A[i,k]*B[k,j]\n", - " end\n", - " C[i,j] = Cij\n", - " end\n", - "end\n", - "```\n", - "\n", - "The inner most loop has $N$ additions plus $N$ multiplications i.e. $2N$ operations. These are repeated $N$ time in the loop over i and $N$ times more in the loop over j. It total, we have $2N^3$ operations.\n", - "\n", - "- Thus, the complexity of this algorithm is $O(N^3)$\n", - "\n", - "Remember that when we say that an algorithm has complexity $O(X)$, this is equivalent to say that the cost (e.g. the time) of running the algorithm is proportional to $X$ (for $X$ large enough). In other words, the time of the algorithm can be written as $CX$ for a suitable constant $C$.\n", - "\n", - "For the sequential algorithm with can model the runtime as follows:\n", - "\n", - "- The time of the sequential algorithm is $C_m N^3$\n", - "\n", - "where $C_m$ is a constant related with the FLOPS in the master process (the smaller $C_m$ the faster the computation).\n", + "$$\n", + "E_p = \\frac{S_p}{S^{*}_p} = \\frac{T_1/T_P}{P}.\n", + "$$\n", "\n" ] }, { "cell_type": "markdown", - "id": "2ebf04b4", + "id": "7192ee22", "metadata": {}, "source": [ - "### Complexity of parallel algorithm 1\n", + "### Experimental speedup\n", "\n", - "To compute the complexity, we need to remember the main steps of algorithm 1 from the worker perspective:\n", + "The following cell measures the speedup of parallel algorithm 1. Do we achieve the optimal speedup?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fd20dac", + "metadata": {}, + "outputs": [], + "source": [ + "N = 2\n", + "A = rand(N,N)\n", + "B = rand(N,N)\n", + "C = similar(A)\n", + "T1 = @belapsed matmul_seq!(C,A,B)\n", + "C = similar(A)\n", + "TP = @belapsed matmul_dist_1!(C,A,B)\n", + "P = nworkers()\n", + "println(\"Speedup = \", T1/TP)\n", + "println(\"Optimal speedup = \", P)\n", + "println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")" + ] + }, + { + "cell_type": "markdown", + "id": "044c4d97", + "metadata": {}, + "source": [ + "### Communication overhead\n", + "\n", + "Since communication is usually the main bottleneck in a distributed algorithm, we want to reduce the amount of communication per unit of computation in a worker. Let us compute the (theoretical) communication overhead for algorithm 1. This will help us understand why the speedup of this algorithm was so bad.\n", + "\n", + "Remember, algorithm 1 consisted of these main steps:\n", "\n", "1. The worker receives the corresponding row A[i,:] and column B[:,j] from the master process\n", "2. The worker computes the dot product of A[i,:] and B[:,j]\n", "3. The worker sends back the result of C[i,j] to the master process\n", "\n", - "The worker receives $2N$ scalars (e.g. floats) and sends one scalar as result, making a total of $2N+1$ scalars. In addition, the number of operations in the worker are $2N$ (computation of the dot product of A[i,:] and B[:,j]). Thus,\n", - "\n", - "- The communication complexity is $O(N)$\n", - "- The computation complexity in a worker is $O(N)$\n", - "\n", - "We can use these complexities to model the communication and computation time in the worker as:\n", - "\n", - "- The time of communication in a worker is $C_n N$\n", - "- The time of computation in a worker is $C_w N$\n", - "\n", - "where $C_n$ is a constant related with network bandwidth (the smaller $C_n$ the faster the network) and $C_w$ is a constant related with the FLOPS in the worker process (the smaller $C_w$ the faster the computation).\n", - "\n", - "\n" + "
\n", + "Question: How many scalars are communicated from an to a worker? Assume that matrices A, B, and C are N by N matrices.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b05e43f9", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO multiple choice" ] }, { "cell_type": "markdown", - "id": "9ecdc891", + "id": "e661d4f9", "metadata": {}, "source": [ - "### Theoretical speedup of algorithm 1\n", + "
\n", + "Question: How many operations are done in a worker? \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f60d9fc", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO multiple choice" + ] + }, + { + "cell_type": "markdown", + "id": "55eb3ff5", + "metadata": {}, + "source": [ + "From these results we can conclude:\n", "\n", - "Now, we are ready compute the speedup:\n", + "- The communication complexity is O(N)\n", + "- The computation complexity is O(N)\n", + "- The ratio communication over computation (the communication overhead) is O(1)\n", "\n", - "$$\n", - "S_P = \\dfrac{T_1}{T_P} = \\dfrac{C_m N^3}{C_n N + C_w N},\n", - "$$\n", - "\n", - "\n", - "We have assumed that the time of the parallel computation can be approximated by the time spent in a worker (since all workers run in parallel). Thus, the optimal speedup will be achieved when\n", - "$$\n", - "\\dfrac{C_m N^3}{C_n N + C_w N} = P\n", - "$$\n", - "\n", - "Since each worker computes a single entry of the result, the number of workers needs to be $P=N^2$.\n", - "\n", - "$$\n", - "\\dfrac{C_m N^3}{C_n N + C_w N} = N^2\n", - "$$\n", - "\n", - "We can simplify the equation to get:\n", - "\n", - "$$\n", - "\\dfrac{C_m}{C_n + C_w} = 1\n", - "$$\n", - "\n", - "and therefore:\n", - "\n", - "$$\n", - "C_m = C_n + C_w\n", - "$$\n", - "\n", - "\n", - "This means that if the master process is as fast as the worker ($C_m = C_w$) the only way of achieving the optimal speedup is by having an infinitely fast network ($C_n = 0$). This is impossible in practice and shows that this parallelization strategy is not efficient.\n" + "In other words, the communication cost is of the same order of magnitude as the computation cost. Since, communication is orders of magnitude slower in real systems, the runtime in the worker will be dominated by communication. This explains why we obtained such a bad speedup.\n" ] }, { @@ -598,21 +610,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "cdb46cd8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It's not correct. Keep trying! 💪\n" - ] - } - ], + "outputs": [], "source": [ - "# replace x with a, b, c, or, d;\n", - "# and run the cell to check you answer\n", "answer = \"x\" \n", "alg_2_deps_check(answer)" ] @@ -627,7 +629,7 @@ "These are the main steps of the implementation of algorithm 2:\n", "\n", "1. The worker receives the corresponding row A[i,:] and matrix B from the master process\n", - "2. The worker computes the product of row A[i,:] time B\n", + "2. The worker computes the product of row A[i,:] times B\n", "3. The worker sends back the result of row C[i,:] to the master process\n", "\n" ] @@ -658,26 +660,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "365dc58e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "matmul_dist_2! (generic function with 1 method)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "function matmul_dist_2!(C, A, B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", + " m = size(C,1)\n", + " n = size(C,2)\n", + " l = size(A,2)\n", + " @assert size(A,1) == m\n", + " @assert size(B,2) == n\n", + " @assert size(B,1) == l\n", " z = zero(eltype(C))\n", " @assert nworkers() == m\n", " iw = 0\n", @@ -687,9 +681,9 @@ " w = workers()[iw]\n", " ftr = @spawnat w begin\n", " Ci = fill(z,l)\n", - " for j in 1:l\n", - " for k in 1:n\n", - " Ci[j] += Ai[k]*B[k,j]\n", + " for j in 1:n\n", + " for k in 1:l\n", + " @inbounds Ci[j] += Ai[k]*B[k,j]\n", " end\n", " end\n", " Ci\n", @@ -700,23 +694,20 @@ " end" ] }, + { + "cell_type": "markdown", + "id": "c13dd6af", + "metadata": {}, + "source": [ + "Test it using next cell" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "267ac8b2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "using Test\n", "N = 4\n", @@ -728,25 +719,53 @@ }, { "cell_type": "markdown", - "id": "e2c6f60a", + "id": "e8553faa", "metadata": {}, "source": [ - "### Complexity" + "### Experimental speedup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3c3ffb7", + "metadata": {}, + "outputs": [], + "source": [ + "N = 4\n", + "A = rand(N,N)\n", + "B = rand(N,N)\n", + "C = similar(A)\n", + "T1 = @belapsed matmul_seq!(C,A,B)\n", + "C = similar(A)\n", + "TP = @belapsed matmul_dist_2!(C,A,B)\n", + "P = nworkers()\n", + "println(\"Speedup = \", T1/TP)\n", + "println(\"Optimal speedup = \", P)\n", + "println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")" ] }, { "cell_type": "markdown", - "id": "c2eefd9b", + "id": "e2c6f60a", "metadata": {}, "source": [ + "### Complexity\n", + "\n", + "The speedup is still far from the optimal one. Let us study the communication overhead for this algorithm. Remember, algorithm 2 consists in these main steps:\n", + "\n", + "1. The worker receives the corresponding row A[i,:] and matrix B from the master process\n", + "2. The worker computes the product of row A[i,:] times B\n", + "3. The worker sends back the result of row C[i,:] to the master process\n", + "\n", "
\n", - "Question: Which is the complexity of the communication and computations done by a worker in algorithm?\n", + "Question: Which is the complexity of the communication and computations done by a worker in algorithm 2?\n", "
\n", "\n", - "- a) $O(N)$ communication and $O(N^2)$ computation\n", - "- b) $O(N^2)$ communication and $O(N^2)$ computation\n", - "- c) $O(N^3)$ communication and $O(N)$ computation\n", - "- d) $O(N)$ communication and $O(N)$ computation" + " a) O(N) communication and O(N^2) computation\n", + " b) O(N^2) communication and O(N^2) computation\n", + " c) O(N^3) communication and O(N^3) computation\n", + " d) O(N) communication and O(N) computation" ] }, { @@ -762,191 +781,40 @@ }, { "cell_type": "markdown", - "id": "aec3851f", + "id": "1c54b0ae", "metadata": {}, "source": [ - "### Speedup\n", - "\n", - "Based on the complexity of algorithm 2, we can model the computation and communication time on worker as\n", - "\n", - "- The time of communication in a worker is $C_n N^2$\n", - "- The time of computation in a worker is $C_w N^2$\n", - "\n", - "Thus, we can model the speedup as:\n", - "\n", - "$$\n", - "S_P = \\dfrac{T_1}{T_P} = \\dfrac{C_m N^3}{C_n N^2 + C_w N^2},\n", - "$$\n", - "\n", - "The optimal speedup will be achieved when\n", - "$$\n", - "\\dfrac{C_m N^3}{C_n N + C_w N} = P\n", - "$$\n", - "\n", - "Since each worker computes a single row of the result, the number of workers needs to be $P=N$.\n", - "\n", - "$$\n", - "\\dfrac{C_m N^3}{C_n N^2 + C_w N^2} = N\n", - "$$\n", - "\n", - "We can simplify the equation to get:\n", - "\n", - "$$\n", - "\\dfrac{C_m}{C_n + C_w} = 1\n", - "$$\n", - "\n", - "and therefore:\n", - "\n", - "$$\n", - "C_m = C_n + C_w\n", - "$$\n", - "\n", - "We end up this the same result as for algorithm 1. Which means that this second algorithm is also not efficient in practice.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df8087bc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c9a52b9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "edd3cb69", - "metadata": {}, - "source": [ - "
\n", - "Note: Do not forget to execute the cells below before starting this notebook!\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36579ad9", - "metadata": {}, - "outputs": [], - "source": [ - "function answer_checker(answer,solution)\n", - " if answer == solution\n", - " \"🥳 Well done! \"\n", - " else\n", - " \"It's not correct. Keep trying! 💪\"\n", - " end |> println\n", - "end\n", - "alg_seq_check(answer) = answer_checker(answer,\"c\")\n", - "alg_seq_loops_check(answer) = answer_checker(answer,\"d\")\n", - "alg_1_deps_check(answer) = answer_checker(answer,\"b\")\n", - "alg_1_complex_check(answer) = answer_checker(answer,\"d\")\n", - "alg_1_time_check(answer) = answer_checker(answer,\"a\")\n", - "alg_1_v2_complex_check(answer) = answer_checker(answer,\"c\")\n", - "alg_1_v2_time_check(answer) = answer_checker(answer,\"a\")" + "The communication and computation cost are still of the same order of magnitude even though we have increased the grain size. " ] }, { "cell_type": "markdown", - "id": "5d2c98d5", + "id": "63f5e59f", "metadata": {}, "source": [ - "
\n", - "Question: Which is the computational complexity (in terms of number of operations) of the sequential algorithm above when multiplying square matrices of size N by N ? \n", - "
\n", + "## Parallel algorithm 3\n", "\n", - " a) O(N)\n", - " b) O(N^2)\n", - " c) O(N^3)\n", - " d) O(log(N)*N)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e219a674", - "metadata": {}, - "outputs": [], - "source": [ - "# replace x with a, b, c, or, d;\n", - "# and run the cell to check you answer\n", - "answer = \"c\" \n", - "alg_seq_check(answer)" - ] - }, - { - "cell_type": "markdown", - "id": "0c2aeebd", - "metadata": {}, - "source": [ - "
\n", - "Question: The serial implementation above is written using three nested loops. Which are the ones that are trivially parallelizable? In other words, which are the loops that contain completely independent operations at each loop iteration and whose order can be changed without affecting the result?\n", - "
\n", - "\n", - " a) loop over k\n", - " b) loops over i and k\n", - " c) all loops\n", - " d) loops over i and j" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7ad3f54", - "metadata": {}, - "outputs": [], - "source": [ - "answer = \"d\"\n", - "alg_seq_loops_check(answer)" - ] - }, - { - "cell_type": "markdown", - "id": "48b73bf6", - "metadata": {}, - "source": [ - "## Parallel algorithms\n", - "\n", - "We study three different parallel algorithms. For simplicity, we assume that matrices A, B, and C are N by N square matrices.\n" - ] - }, - { - "cell_type": "markdown", - "id": "d04c4659", - "metadata": {}, - "source": [ - "## Parallel algorithm 1\n", - "\n", - "Each worker computes one entry of `C` in parallel. We need `P=N^2` workers." + "Let us increase even more the granularity of the parallel tasks by computing several rows of C in a worker. Each worker computes N/P consecutive rows of C, where P is the number of workers.\n" ] }, { "attachments": { - "fig_matmul_intro_q_1.png": { - "image/png": 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IM0kKF0BvAKf2xqJHH3306yLSCE5IIf++cuXKs4FLszbE2GCZ0Itzh2OOSHd8F/h21kYYRh/jSXp+Tx/JxuOIrKGXax/D2EiZ2otzjyB2RGpauyEio2s5Xw1pydoAwzAMwzAMwzDWU+tIbMnC0gdefYDfvv7borGtB2zN6D1HIyJVvUindnLTn2/i3Y53i8Y/t8PnOHTrQ5NO+eBDDz20xWGHHfZ2VS+URCvfQvmIN/o7IuZXPdfFbMNapuM7hE1cyRSe7bmR3RDRDFyMK9YrZAkRP+nBfAcSFv21AxcS8W7CGbWh1p9fK19F+YI3+ioREaA9NdNoXBSacNeCn1r3HYEf92C+jwJjveF2YKLAOz2zsqLX3RqYQRhcukrgrz2Y77+AL3rDrwKR1PFaUDgAGOcN9/jzUzgTGOoNPywwr4cmVvq6XwGO9IZ79PnV+jtqpI1+DpeGUsgKYBJIR5VzCRAR1iTeD/L/emhgpa99GuAvsJ4Gub4Hcw3CZan08w7MBvl9j8yr7HUFmEZY19PDz0/3wtVDF5IHpoC80RMLK3zdGn9+id/RlcBF1X9Hq6fWjshmpQ4c/L6DufaZa1n6r6VF45v325xjPnhMVS8y+y+zuf3524vG9th8D84Z6n8f1jNw4MDNgd47IsJPUC4GNi0YHUUr/2AaP61qprXMA77kjS+uqxMCENFBxMOENUHDmMlHmcw/Kp7rMjbnPe4B9vKOnFNXJwTgIl4nYgVwYdF4Jx/naj7GBN6reK6IvVBuAzYvGM0jfAFzQjZYBDoVfgV83z1cxzCFAwVeqHQuddvM9wAf8g6dV08nBEDgDYXlwEXeoY8rfEyo/FpQ2BOXM198LcAX6umExDyBW2R92RvfGpcuVDHqirWvpfjvuoJ00nR/ClxG+Lu4Arimyrki4Hxv7Cng7B5ZZqTNr4DLcUGKQtYCU6qc6yzCdJh/ABN7ZlpV/B/uO/2+4mF9GeS7Vc41i1Ct9Uf0vBawQkRBH8Bdn00FB44DPRTkmcrn0gHAQsK/6yX1dUIA5F3QvwNXegc+C3oQSBUKnLoz7r61tXfg2DScEKhxalY5Nu+3OZcceAn9csUO3HXPXMeTbz1Z8TyPvPEItz13W9HYJk2bcOlBlzIgN6AmtpZlKk8jnOmN5lDuIqpCOSniAkIn5DkgnfS2iO8Qyg4OoYNFtAVedmneYzbhzfaHRFXfbHvKFNwPfSH78jZXVDxDxEBgMcULL4AZTLOI44aOOGWhm7zhIcBCDSNO5ZhN6ITcD1zVC/OqYSrwM29s32peXyl5LcxMI/oeOzqn4AqtCxmhcFql8yjsAtxKsROiwKkCf++tnd0RO57H4uoPCrlM4eOVzqMuj9pfZK4Chgv8q3dWGukgq3G1gH4g9CLQz1c+jx5C6ES3AyNBlvfGwsqQl4CTCIMR80A/WPk8ehqhE7IMOAUk3xsLK0N+Dsz0BjcDFoNumnBCKa4hdEJ+h9txSYOrcQG0QnbHqU1WiDaT7ITcBHJPb4yrhlT7e3xk8Ec4Y+8zisY6tIOLHr+It9u736xYvnY5k38/mTzF39WJ+03kA4M+UEtTyzONeYQqG9sBd7G4yMtOZjofA6Z7o2uAEUQ12LWplK2YgFMkKuRj/DOwLZmI0wkV0l4m+ceqPkR0AMfgxBYKGU8rlW613USYVvhzPsKM3ppn9BnOJelaoLLvgLp0rOO84ZeBE1PYRQDc7g7u2vOvhXFauZLhDbj0qEJ+Qfh7VTfE7eycgFMzKuRGraCvTOw8LiSI3HK9OJWXVBDXv+YCb7gfzsHdqrvzteueQnBPOV3gT7Wx0kgHeY6wVjUH3AG6Y/fn62BcP5f+3oHzQXrbfqEK5AeEcuixberbloAOBfxUrjxwHMirtbCwQqZDkIY+lIql3vUbhCmkbwEjQNp7a1xlSFfQxs9gGQbqpweXYibw797YH4HzemlcVaTdaJBjPnAMn97+00Vjr773KjOf8B3UYvKaZ/Ljk3lzzZtF41/e5ct8cSc/lTkFNmMc4c3gM/wp2EIv5hKGkGcRYaR1AlGwEKovZ8bOTxipuYDWYLemmOnsS7gt6JyCKFgI1ZeohPOj3EzEbmXPbWUEoWLK68CxDKezhlYaDYy4QMBwwqaS52uYJlSEOh31q73hTuAkCZ2CuiKx80Po/LRpuFtThLr37/dteR04NnZyUkOc89PqDQ8EFmu4W+NzKfAJbyzJKUiD6wmdn12B27R4t6YIXbdIDXLZbxW4PeEUo+GRRRDUkm4L3A3aXQBzHk5SuZAkpyANzgd+640dAlxS/jSNdx3wdx1aQapJa68BkgeOx9VtFTIK9Pjy5+ruwC3+IG5Hp/K09pogsfOD7/xcC+rv1njoFwgdjneB4SCVp7XXgNQdERFh6v5T2XHT4iDAz179GUteKN3GZO6zc3n4jYeLxnbbfDfO36f8ur9unOe2x/Fzr5XpRIGH2YWwhgXA+73x7xBl1J01SkwHE5R5zGSnEucMIs9iwoaVU4iCNKl0iPgh4WJwS2AR15OcsxexB8ocb1QRTiPilTpYaTQw4pqbhdcCLFC3eAzQdVv6wbUwVcI0qVQQlw7mBwkG4RbxA5POUdiDcEtfgdMEliackgYX4/LSC9mLMKK6DnUF9n7txLs4Zyr1XlLxbtiphOlgRwPjy5w6BfgPb+yvEKQFG32L8YR1EIcDk0ufot8EvuYNxmlSkkH9osTpYPjpYGeDfqXMiTdCIPTzczKTZpdluJ1iP8hyM+iHk8/RfsCdhEI/V4P4aVIpIb8jrBsagEs12yL5HN0e12PI9wHGgfy51hZ2R+qOCMAW/bZIrBe55plr+PPK8DN47M3HmPfXYpGT/rn+zPzoTAY2Jd5X0yHiKSS46bmcuyjIuYNWJuDUaAp5kVBxKl0iFhMuQrahg7tLpJrdDPgX6s9wBXlZciHwG2/sYJYnFKeurwvxL9RLmdbrbsRGH0WcgEObN7wVcKcmi3vcDOztjT2AK+rMkouAX3tj+5Fgl7qb1iLCa+Fy6X1n7h4jLmXjBAiCAier2wEtQmFn4DbCnYYxPVEOqxXiCtRH4IqTC7lS4SD/+eoWpn4R82pcXUhdRQ+MerOuXsT/O06NlYs8dD/C+2qcjixvhs9PC3kROJninVcB5oN+IHy+joyfX8hruILoDDMPJOm3ehBuEe8Hl8AFeA7zxh4hFAlJm8sIf6v3gCDQCmgOl/Lp1zTfAnJnPYzrjkwcEYChg4cy7kPFKXZr82uZ+PhEVnWsWje2fM1yJj0+KagLuXDfC9lrC79GOgOm0Yb7oxayM277fP0NcTqHxGpbhbSTYyRREFnIgjOBP3hjn+IZz9NuZTRuS7OQZcBxRKRQaFaGiA6aGQn4P9DfopWvemPJhWY7pFZoZjQuZwG+BOIn8YoQ4+LppKLL49JOZfKRdYuV4Fo4U8Po6tXAgd7Yw1Sv6FNzZN1iJfg8b9ICB1C7AkBh0eXNAnfX18rukeTFygBgkRZEVxW2wdnrB4DOkLorChnpIEk7WzngzjhSHaPxgjjYbZ0E4gcZMkBKinzEOwcxuifhgjgOMkgjZB6UEvnwdpX1aOAM73lxkEH8IEPKiOLuR/7u9YhYHKCQVuAz3tjTwLfqZFy3ZOaIAJyw2wkcvt3hRWMvrXqJmU+6epG85pn6h6m8saY4zfrzO36eL+9SNm07bcYCf/HGjox3QCBicFwXEhaaTQ1yLbMhYjVNiZGaybTGKQLTGYpyrXc8j3AcUZBrmQ2TeYmkSI0ynxlxjm0rwwh7PbxFMyMYE+RaGhsZEkefSVC5Ufg8gLrCxqSiy+MF/ll/K7tH1qVvhCo3GuebK3wDON07/hYwQsK840wQl76RqHKj6/PNk4ounyLU+M+Ssio3BXUhfvHyYglz0o0+jdyK270rZDuK60WSVPh+RJh2mSXn4uqvCikQ+dBSKnyXgPhplxkhnbi6Or+e73TQWORDk1T4AE4DqbsKX2WIq21NEPkAjUU+tKQKH0hmKnyZOiIiwrQDprHDJsX1eD9+5cd878XvseC5BTz0erEgxC6b7cKk/SalaWb3RF0FPkG9yKVM598oVWgWZVJoVhrXvyRU9lDuJGKPuC6kuNBMmF5l/5T6E3Ef4SJxMJ0sZAZ7owm58MIpVfVPMTZoxElpJ6rcKOyGi1r7RZczJFRiyRRxBa1+8GAwLhK/J2EaWpfE7QspmFcNSSo3+wDXKiQVXXZJ3KZadFmOAmniF7xDw9Sl514I/Kd3LD1JdyNtxgN+34ojgAtAxxCq8L1KahK3lSJdgjeByAfol3H3YV+F7yFCIYqMkaU4Z8T/bGfHSl9JKnw39qB/Sp2RkiIfcZF9kgrfN6vrn1J7MnVEwNWLzPjoDJqk+LO54qkrmPPX4t28/rn+XHbQZWzWXLJvYnZEPIkEN8Nm8vyIMBXiRZKVbbInYiHJyh6/Jyw0e4C9g0hlo3A+Lh2ikEPp5GHCQrNrmBZEKo2NHHF1E34keltcisx+3viDVCj1mwEX4lKtCjkEJ1fsy8heJ1DnDs3VE9eLnIhLfStkNK6ux7+XjW1EiVtxu03HEUYtryVcQHTVhaQn6W6kiKzCLeJ9Z7mVMHjQiesX0hiZB0XI84RBG8Eten0n+g1gWHoSt9Ug90PQf2xznOPkq/A9jtsNakQuJuz5tBcu9d5X4bsj3p3LlMwdEYADtjqAsR8qzpRZm19LpxanBZ8z9JzGqAspxTRuwuUpFzLIe9xBjmMbpC6kFEnKHv77eA04vmElbiPW4tJO/M/Zfx+PkE5XWqNvcgbdXwuv0wB1IaUQVyQ9jO6vhUfJRuK2IuKUt2MIP2f/fcwXp2zTkIgT1PBVkgYSiiGcI2GtkrFBIU8R1os0E6rbRXEjvgZFFuNSyQrxr0sFRoG8nI5NPWIyociH/z7exRXZp67CVxnrpIn9+hv/fTxLeeW+1GgIRwTgpN1P4uPblG44+9kdPsvX3//1FC3qIQMZB/ytzDMmMTX4ojcWEavJcRylO/e6QrNGl7iNeBEJIjWFrKCJEbHTYhgBBfUipdSKuiRuG/paELcLW67R6EpcXUhDXwviFMlCFbz1PENYUNqIXA78d5nj9woZSbobKSO3EDZILuRBuu3R0RAkiXwUcmV2EreVIh04aeJy/Z/Ggvg1wQ2GlBL56CK+r0lDqPA1jCOSkxyT9ptELsGkHTbZgSn7Zy7gUhkXspJQx76LHxMFW3+NyVSepnRR3JVEgb5/YzKNe4H7Shwdw5RA398wioilX68qcfiqLCVuq0HcdVDqWhgr5QMojUSEc6x81uJSmTIruqyUuF5kPMkLhRcJG0saGzZnkvy9fQMn1duQu63FyBpKR9gfBRqsuLcU8jKla1gWgPgqqQ2K/JzSO8PngvgqqZnRMI4IwILnFgQyvQBvrnmTV/7V0AHH9UQ0Q8nu6h8iYkia5vSYiG0pfTM8mKixvjslmc4hxEpHCfhFdIYREEuqltpZO0jD4r+GROFgwmLoLvrStXAiyQ0m+wP7p2xLb5hI8ndnW8Kmt8aGzQRC8QtwwhK7p2xLD1GhdGrnrrjf0T6ADgK+WeLgPqC++mmDonsS1id30VC/9w2zmPzpP3/Kvf+4N/HY2vxaJv9+Mqs7V6dsVY+YCfxbiWO7EqrUNB5RSRnJLj5DaWercSgtm9zFBbSWXJgZBuqKLudR+lqIVW4aG3VFl3dR+lo4X+FLKZrUI9QVXfqFvIW0xc9paBS+Tiib3MVAnDSxL3lqbJDopyndEC/uj6O+YlMjcg5hw+YutqVYmriRuZlQNrmLQyDoB9eAlJRN7mIUqN8PLjMawhFZ+q+lzHiivODM3975G1c81eBZTVGijKTPN2hlXDfPyZoLKb2L0MWMWJq4kZlFKJtcSA7lDqKSi0zDuIDuF+itGvawaDRmU36BLrj+IjulZE/V6DoZyrIL9EHA3XG3+IZEXUAqoeNxEXtBg8m7G3VA4wV62V3VXYDb4h2HBkUPAb7dzZMOp+HTszSpYbPPBNBSDlejcC3d73rcDPrhNIzpjswdkfZ8OxMfn8i7He8WjX9l168wpH9xFtP319cWXQAAIABJREFUX/o+9y+9P03zKsctZm8n/EyvwJdqVK5meoOmEET8O2F+5HuEUchm8txNFMh/NgatjMcp7BTyAqGq2TbAnSzuG+k1Rnqok2z0IyTJ14Jb/DZk1FJhHK5wsZB/EHYc3wa4s4FTzW4gTL36KaE08UGUL2jPDHU7UksIZZPnE0oTn6JwQiqGGRmgcTf1QFL1HsJeM0cRqms1CDoYFyDwd1uvJZQmngp6OA2J7kPo/HcQSvrGu+S6cypmVY0Ox/UlKuR1Qin6QbhdqsyDNpk7Itf/6XqeWVHcS2Xo4KFcuO+FtB7QSk6KTbzkj5fwwrsvpGhhBbhUptsJcyBnE3E+MM0bH0ie7xCxRSr2VcolDMH9MPoykmcScTauqU8hu+Led2NFaqazLxr8eDjZZDgVeNI7dgR/CuQ0jY0YhSG4VCb/WjhLnBiF79DuAtyuDXYtKOxLWGjfgXNMTiOUJv400HDKIOqUy/yatddwC/UkaeJvKXwlDduq5FLgUG/sEZyzmCRNfLNCQ0QtjZpzEfAf3tgzuO/5cEIFuytAD0vDsCqZB3zAG7sX5GzgW954E7AQdLs0DKsc3QzXN2oT78BUkPMJU+rfh1vE+/eHjNHdCXdbFfdbP45QmvijhI5W6mTqiPzqtV+x8O/Fa9vN+23OJQdeQrM084ltP8HxuxXvkv2r419c+NiFrMk3lITzNOCz3thTbMGE+N+XQqAytQfdb8+nibCGBYRFkouJ1nnS4yBQmTqK1gaK1EQMijvA+z8oE5nKb2Np4mPxFUqUqbQGf0NjIyR2JuYT3lyXyPprNkmm+4s4CcuGQGEzSLwWJgn8JpYmTpLpnqLwuTRsrAR1v5VzveE8cILAP2Np4qSGavM1/BtmhiZ/P1YQyybH0sSXe8c3w9WL+H9Do0+jnyQMUK7G9af4F8gjhL1m+gF3gvoNeTNEzyC5YXN8PcpcXECnkO2BBQ2WanYjSQ2b11+PZ+EaAhaS9DfMEO2H+6z978flIP8TSxMfA7zpHT8DtFRReypk5ogsW72Mab+fhhbI2gvC1P2nsuOm61P2x394PPsPKd6Nf+6d57jumQZJn52emPe4iiaGMyHelozivhuuIVchI4g4ue42VkIrEwgLzZ6nsDNqxApyjMCP1ChXENEokZqbCSOI9xMVRIWn8jQSLAhyKHcRsX29DTQanrMIo+lF14LEfTcIo5aXKZRuiJQuSdfCjyiQ5RZ4mjDlI4dL0cr8WohrPRZBsHt8iRQEdwTuJey7MQRYqG4BlykKOwO3Ee6YnSbFwZ2pwK+85+xLaSl1o8+hQ3BiMH40/QyQwh3KKwl7zexO6JRnhO4HXOYNtuM6wBfuUI4F/L4bR9Iwncl1JATrsGXAcetlk6VUP6mLQP1dray4EviYN/Yw7jclRl7CvVe/n9Q80HL1tHUlE0ekUzu56LGLWNm+smh85AdHcsT2RxSNNUkTFx90MVv2K3byFr+wmJ/982d1t7UsEduSTyw0O50p/Ml7bqkGMzcSBZ54ukznEDRQglgDDCfi7aLRqTxC6Hi5SM2lgSeeLhGjCAvNluLkPosvvGnMJdTY3g5Y0GekiY2aE0vc+vUF7cDxsfOxDnHa+BO95/bDLX4zrZ1Sl4Lo1xcsA04RijXSxaVW+A3VtgNu1ezTd68GDvTGHiJZ538CYUO1j5V4bmroOuUjtvYO3Sjw3cIBcWlzxxA2VDtdw5o3o8+hAiUyD1xjwwJEgVMIe+YMAy3XqDcFdBDJu60TQX5bPCTv4hbxvuzpxaCfqJeFlaF7EmamxIFj8QLH8izhzmtc56N+nU/K6NGEjVxXACNAvGCZ3IertytkMLAoK2niTG4ys/48iyfeKk5N3nvLvTlj7+SGuNsN3I7ogAjxgkkznpzBK+9l1F+ktMTtrUTcXuKcBwml31z6RJSoIV5/SkncCucR8XiJc64C/A6pu7M6w0jNdIYSFprlgROJSnZJHUcYqfkCDROpMdKkjMTteeIWv0lcQ3gtZCrTrTCU8EaTB44TeLXEaacDf/bG/pPuVQDrRgmJ27eAkeKcwyKkK3iCFzyBC7R7FcB6Mp1QVe2PlJBAF3gZOIkwanmzllcBNBqfcwkzD56jMPOgCFmOC651eAeuA82yF8RsQonb+3GBgwTkSUKZ82ZcvUhGQZuSErczQX6cfI4sxO1sFpKxNLHuAtxK8W6rAqeCvFDipHOB33ljh+DaT6RO6o7Ib177DXf8rTj4tmnzpsw8cCb9c6WdsU9u90lGfHBE0dg77e9w4aMX0p4P7klpkCRx+yybdFsvEQH+Vs5Q3IImC5Ikbu9jGjeWOUcZwCk45Z1ChtEaqDXUnyvYLK4L8Z25aUTBZ72eiK5Ija/s8e0+IE1s1J4kidsfANeXOiHukJ10LXwjVqtKlVji9i7Ca6FVnMJUIkLJa2Gmlu6LVDdKSNwqbkfH/6zXIW5RlxS1vENL94KpG5rcc+ldXAd4/7Neh8APCdXZtgQWaeleMEZDo0kLvTW4qLXvPBcgv8Q5s4XEi2jNoNeMtuBqywp5GTgx3sUpgVwPfM8bzFKaOEni9heEn7XPePAyXpzIx4W1MasatGu31VdsvB7E/6wLkHbc33Cld+Bc0C/X0sJKSNURWb52Oa1PtJLX4u7pE/edyPs3676R7FkfOYt9Bu9TNPbMyme4+S8319TObnH1EJE36nIILwhyCP1z8zRzIuHWewutwcVdXyK+Sbjd/xL9E3MIi5nIW+Q4gVCa+FqilLt2rkosNHuQj3BJt+dGPIkkRGry3MPFjSnHatQedZF3X+L2JeAk6eZaEBelH0EYpb9GnSpJmtxEKHH7c7rX+EdclN7fAWkG7klTmjiu6VhImN52tYS7TwHiakrme8Pb4iSWU4taqktvu4vwNcdJuPuUxAWAl+bSVxqqGcVonPoSOJHngiRnHhTzbcCP0u9JmSBJfdB9CIOmncBJIKUyDwo5lVDw5mjc4j5FdBihxO1yXEqWnz7vIatwQRtf5KMV9DO1srBCLiYMFD1GRU12pbgGOB7ECQnsWgvjKiU1RySveSY/Ppk31xQX7H9t169x5E5HVjRHszQz88CZDGoeVDR+x/N38Itlv6iZrWVxErcLCQsgv0UUqCokM5mlSELdgjKbGSU7etaW6exLqNDSAYzkokBVIZmp/JIw/9pFai5LqStwRFKh2Wv041iGB/U4yUzjBrxcbWAX1gbbncYGiCYXA3cAx0ioMJKIuG3uyBsegFM8SkWmW50zdKo3/BpwrIS1aYmIc2SSpIlvS1Ga+DLCgv9HKN19OonxhDLdqTVU03W540HB/y0S1qYlIusKfwNp4glauoO10ZjMJynzwF1vFSB5XIqWL3hzMuiJvbStQrRLhS/MPEAqLNiVFbjvtC/ycSWoXwtWJzSp4D/e2Ra/HqcE8hSsU0Xtogm3u+PXgtUJTSr4fxdXZF+hrKwsIdx53gq4I81Us9QckbnPzuXhN4r7Tu2++e5MGOr/Lcuz86Y7M3n/YlU7RYn+EPHqe6XSn2tGKYnbJURVSvFO437Cxc8gOllMxMBe2Ng9pSRuhYuI+E2Vs11MUqTmvRSkiWeQXGgmHM+k4Ae7O04jKVITBQVgxgZEGYnbyRJqrndHZjLdmvw6XRK31RbSnU6STHcKDdW6k7itdB5ZJ4UaRC2naSi1Xg+mEkogP03YV6EssTSxH7kVYIGG9yGjIdEzga96gy/hdhHKZx4UIa/h0mn8oMIs0L17Y2GF3AT4r/MAVTcPlYcJexXF6nha56BNSYnbK0B8hbJukDZCaeKdgdvrn2qmO5Hcw20siF/32h3fIpQm/hRFalv1JRVH5PHljzPvr/OKxjZp2oRLD7qUgU3Vr7k/t8Pn+Oquxdf12+1vM+X3U8gXC8LUllIStwNLFZp1y0UQLPyTJPFqTbKs57QeSEQ6aeKkSM1IIuoXqYkYSCeLCAvNLmFa4BhVMl+yNDFcThSo9hgbDknXwv/SgyZPQmmZbg137WpGLHG7mHDn5TIJHaNukXVqK2FDNQ3lIWtGGYnb0yV0jLpFkhf+OeCuOG2qLqjbefF7QKzG7Uz5jlG3CHwHV79UyBBc3UuDNVQzikmUuHWZB8UStxUiDyTMFwdTtI69ZnQkTkChkNgx6i6VKZErgP/xxpL6BdWaJInbR+h5E9exwF+9sSNxTW/rhHY10PZ3XuaA+I5RBZSUJp4Mmko/KcH9UR4CXqCXihyPPvroO7i28UW81/leUFDeLM1s2txzoahO7WRVx6pgfFC/QeQS/Kvm5uadDzjggKU9fkGAiC0Y4OX7DmD1un4hPeF6BrDK2+pcgxKxosdzlke4hMHB6BpWEVUecQyI2JQBDCgay9HRbc1Mz1+vmQEJ6V9rWBk7Rz2dN/wbr2ENUfULiAp5F3cj2Z2wQd7Gzlxcl+HJVFDjUC1xqlF4LcCqaqLvCfNuCt61AB0S/tDXhHgxmpQKudKX6q1y3i0I6xvW9GQxXeHrDSBM+9DYMerNvEMShnv1N+7m9TYjrAVojwUBejpnE8kpfm9XmnZXJSNxKXoP4AruN0R2w/UHWkXCuqU2aNJvQWf54vRu58wRRvQB3omb1tUB3ZzQ6V3jmi/2eM7+uGvFZ0V1O0VVveZgwkDHvypPZUqccxMIslh6+Tcu+3qlfgt6+ffXQYQlB2vjmph68Hdcw9nDah1NWUPCBb1J0yZs0lRbZ71JmtiiX+W7eGvWrPE1rKvH76lRC85kDe5zSwtlIm/VfFa3UK/XYj3p9TqgLu+jTj8eRqMRF6HX/DsUL9RTuxbi3hP1eB+pXgux/G7NfwulHr8T5V9vFe6/Ws7ZScrvw6gFUoffAsmT+ndB6hBEkbXUKRhQ5jXrEOCV9yijgFeH16vTb4H0OFDSW2qdmvVsjeerFW8ceuihPdgGNQzDMAzDMAyjHtTaEVlY4/lqgogsFKnXVp9hGIZhGIZhGNVSU0dkyy23nK2qj9Zyzhrwooh016DGMAzDMAzDMIwUqakjsueee64RkSNxqjONwOPAZw488MDXszbEMAzDMAzDMIz11Fz67+CDD34D+MJjjz32SVX9vKpuW+vXqICVwIMHH3zwj0Skjnq+hmEYhmEYhmH0hLppkB900EG/BH5Zr/kNwzAMwzAMw+i7pNZZ3TAMwzAMwzAMo4v0urJGfAFh/6IxZSkRd/ZwvhbEa1Ql/Iapdd6FaeUkYPuiMeUxIn5S9VxXswnv8E18h1D5HlHQrbN2tNGPf3IG4jWvUX5AxFNVzxexK8Ix3lwKzK5rX46I7ZGg2yso84h4owfzHYpwhDfXKiJuwvWcMDYw1DVw+iZhA7ofCvyxB/PtAhybcGi2uJTRuhB3Cj854dB8gapr5BQOIWxktwqY1ZsmiRW87n64zsSFrAFujPulVDvfl4CPeMN/F9eJvm4o/CdwgDf8isAdPZxvFPA+b/i3Ar/oyXxGmuingI97g8tBethFXI8HdvIG/wBS59pcHYZrAlnIMyB+h/RK5mrG/e76zR5/BPJEj8yr7HUFGE/YSPEBkId7MN82wKkJB24FWVb9fBW/7pa4ru4+94C82IP59gGO8gbbgRtA2hNOqCnpOSLNPE0Hd+L/mEb8i4jvVjVXK2NQZntLw6Uo83prZrco/wDmUdxxeBURhxLxTFVzvc11wGhv9BfAVb2ysTvG0E5EDuVS78hoLuUgLqxiwRTRDNyD8gnvyPV1bw4Y8SoR+wDHe0cOx11UlTsPEdsC/w9lh6Jx4eSq5jH6FALtcYf14FpQOKga5yHucH4P8G/eoRvr6YQACCxTGAqc4B36tMJR1TgPSnwtwI7eoVPq6YTE/AW4FfioN74NMKmaiWJn6jsUO5ntwKd6YV+lPAPchXe/U+gQ9x2pmNgJ8Rety4DbemWhkRZP4RzQXYuHVUFuqW4q/RJwO8Xdwd8CDuyNgRXyl/i1C7uIt4N+GuQ3Vc7VClzkjT0FXN9z8ypBFPRN4AbvwDLQj4L8s/K5VIBbgC97BxaDXNYrM7tFVoLujHPmCvka6CfjZpEVooNwv5Mf8g6cm4YTAmmmZk3mJUhc1M1jBh+seJ6IfVCu8UY7gRN7FAWvlogHgYu90c2AxURsWsU8wwmdkOXACXHX8PoScRXwfW90d1YHN7zuuBQCJ+RJ4IKemlYl44A/e2NH0sq5Fc8QkQPuBM8JgVuZZjf7jYBrcAvvQnbH3WSq4WJCJ+RJ4Pwe2lUtpxNeC18Azqt0AnX3hDsInZCF4hyEuhJ3Vx9O2NX9wniXoSIUBgOLCHe6zhV4qHdWdo/AS8BJhPe7WRpGlUsSO5fXecN54DiBV3tnpZEOshwXLPPv6zeA+rtmZdBdcddgoROiwKkgL/TKxIqQpN+yfsBCUH+3rgx6BOH6YBUwPO5SXmfkHkInfjvgLtCmhBNKcT6hE/Ic4bquXpwLPOaNHQrMrHKeNkIn5IfA1T20q2rSrRGJuI/Q4x1MJ4uIghtGyBXxgh82KRoXphHxs1qZWQER8FNvbChwbYVn70EY4VLgFCJ6sK3WI5QBnAL8wxsfRpS45RfSypHABG/0XZoYTsTqGtjYPRHvkmM4UPwDplzM9GBRWIrJwH94Y8+yCWfWwEKjwZF1N3Ne8A59Q52j2y3q0ol85/ddYLj43806IfHrEb7eTIV/r3Cai4DPe2PPAi29NK9iJPlmngPu0NBBKsU8CAJc9xFGQuuGwA8I7wmDgUUaOkgByrr7nR/giiS8/xgNjfwSt24oZCCwGHTz7s/XeMHPVt6Ba0H8IEodkRsgyGDZBbg13iHoBt0OuJvijBKAcSB/qoGBlTIeggyWI4CJlZ2uHwNmeINrgBEg9c0EWYfErxfstp8L6jtIJdBxhKnELwMnud2jdEi/WH0HzgN+640eQrjLELKKWcDe3ugD7B2kVdSXiDz9OI4wIjWaKEgTKuZ6BuAidVt4R64g4r9rZ2QFTOQtcozApSsUcg3Tg9SIYiJ2RoMtYhDGMoW/1NTO7pjKHwkXgc3kuYeLg7zqYiI+BUz1RlcDw7mAd2pnpNHIiEtvGAH4W9rXaJgmVIS6fO3wWoBxQrrXQlzX4gcHmoF7FLYud67CJ4Fp3vBqnDOV6rUQ13D4O1LbAHdruIgpQuFM4Gve8EvAyZJ+muUFhPe7gwlTAZO4ibC+5UEquVcajcglwP95Y3sCcyo49zLCOpNHgQtrYFe1nAb83Rs7Gjij/GnalXmwvXdgPkiPaqd6jsQ7MPzLO9AK6tfGeegQnFPYzzswAeTxWllYGfI8YdBGgAWg7y9/ru5LWAbQARwDUv/sogLSd0TG0A6MxKUhFTKBVv6r5HkRpwIneqPL6MdxDKeztkZWwCSWAcdB8No3E/Hhkuct52rCfM6HgSk1ta9SpvI7woX4QPIsJgqcJUe0LhfeX9jMZhp31d7ICoiYhYu0FLILa7mNcIHouNgtbPAXNsKZRPyhDlYaDYwkX4cDgMUaBg4AiBfFtxNeC3OEHgpx9BKB2RBchzsDt2mJa0FdpPVOwrrBbwmZXQtnEL724ZT5rYyL3f387A5gpMCbtTWve4SS97uzFL5S6jyFYyAQ4ngNOFbCe47RJ5A8robrFe/ASNBQdGUdehRwlje4Ehd9r6IWoFbICpKDNleCHlbmxKnA57yxZ+jWgakX8jQEWQ85XIqW7yzFqADzgQ94B+4FmVVjAytEluB+8wvZCpcy5ztLMTqIpOwimAryq1pb2B3ZyPdGvIgEW/2CsoCZhF7cdIYSbqnnEU5gElUUF9UYlw52uTfq/sBXB39gaOXruDzuQlwkNgou6vSIuAzwlS/2oHSkZjphqsdTbBFEYtNlE8ZCoDZ2FBHfSni2sJZ5hOoji5lWdZ2MseFwBQQ7k0mplF20EqpLPQWcXWO7qmUs4W7MF0mwS9dF0PxiWpZIZdHauiBdO5PhbswUDRc0aNdvb3ExLcBFAtUW09YMgRcJ60UEmK/hggaFvXB524XkgeOFDO93Rg2Q13CpML4zeROov/tFXJCcFEwbB/K3elhYGfII4W5MP+DOWNHJQw/HpUAXsho4FsTflUgRmUeoZLc9bkchaX18NmEA4UVSTF0twVnA772xwwh3uLuYBUHA/AHC9WwqZNdHZBr34raeCxlCB4toK9jyihhInrsI82RnMo0f19nKSphK2LhxX97myqKRiF3R4KbuctOjIDc9bZT+nAYs9cZHxDtR64n4DGHB2iqaGM6EdHLhS3IB75DjWFyuZiGXM93b1o64ACftWUiahWZGAyJdtVph7dTwWL1oHepyiv2b8SpcKlOGN9ey9SKXaigucR5h0WXSln/qSHJ9Sg64U0NxiaSiy/vB+y3OAHH1KTd6w0Pw6kV0Xd0Aft3AxUJD3O+MXiM/J6wviOuBtGCdo824FCA/vXhWXHCdNddSkciHbktyXcj4+kr1Vsw4wK9P+QJBurcegkuvKyTe8RR/xzNlpJTIx0RQr+ZPRxGqKy7DOYWZ7LZm29BwK84B/Jy6j/FPphc8ngVe/xH4BR8pek52OIWrYyFQ7DqdKC4CaqNUodl1RMGFnA0X8TruffjKHjcxPf78v92lLBH8oJzOlOBCzoapPEaSskeehUTx5z+dj0Hw/XGFX/WWHDYaHnFpNCMJa6du0Lg3hFLyWhgv4U0tEyRZsasfsFDjxY06lZWkosvh9ZYcrhRxv523esPbAXd11YvEogJ+0eVS4MQM6kJKcQ6hYpf/+d9IeL/7JW7nzdhwmEHoWA6lWMzn24QqfEn1kBkhJUU+4iJo4l2FJBW+RSDz62xghayrF/GDNt8GjT9/LaXCdwGIXwOWEVJS5AM0/vy1lArf8SCZqfBl64icyRqaGE54w7uAVr5ExEhcdLKQ12nm2EzqQkoR8TLCiYQ3vNlE7MU/uYLkQrO0JG4rI+IXhDc8Vy9yKVvSzl0kFZpF3J6OgRUScT2hsoeTPozYijyL8AvNhHOIAqfY2EiJJV4jb3ggrl5kS0rIPUuD9XYQt7D1o6e7ALfGdSFJN9fzJAwQZc03SVa5uVAhqegyj3NCUi26LEdcL3I84f3uPIUvq1sMneYdW45Lyaq/pLuRIuvqRfxUu9NATwAtqcKXjsRtpUhJkQ/Xl4OJhCp8z5F9KpOHJKXTxjtSujXJKnw/pFKl1NSQJJGPeEdKtyBZhW8GSPUNuWtIto4IwBSeRxjjjUrcnNDPy84DJzI5SCHKnmmJKQCbA/9LWBDlCs2yrAspzcWEkZq9WM3vgc964xkWmnXDAEYRRmq+hHMA/Tqke5kWpAkaxqUkq9z8nrBG4U+EzaUahdNJVrl5lLBG4XuEKUSZI+v6DAQpbxFOJjcouhRSlXSvCElOeRPcQsdfQChwQlxjYmxwyDKcAI/fJHQWTgDDX5+NBfH7BDUA8jBh/ccAXJ+yyBtfDQxLT+K2GqQNF5gpZGeciImvwvcicEKaErdVcCZu56yQw3HCH34d0gOEO+Kpk70jAjCNRYTFedvgig8LuZyIH6VjVI+4CPi1N/YBQonbcURkWGhWhqhrmy5Q9vCjAavJcSxRtrnwJXHSxMMJIzX++2iEQjOjARGquBbgmHix3HAIrICKroWXgFENlMpUhMDThIGPZtwOTyEPUJk8biYILAFu9oa3JqwLuUJc1NXYYJGfEMoxDyJU4bsFJBtFysq4krBB8i6EKnwTQBpZkXIUociH/zsZp+NnXRdSCnkP+DqhyIf/Pl7HpWRlnl3UGI6II6nqv5DfsUMgM9tYRHTQzDGUl4mcxbQgVaKxiHgtLvou9wUdz1QaodCsNFN5hPINitrJMZIokNY0DACEdbVT5a6FM4TGvhak+1TQdpzEbUNfC+KkM8ulgi7DdR3P/ObaDWdTPv0tO0l3I22mUb5B5dOQqPzYQEgpkY9CvgPiO+ANhpQS+ShkMogfcG4wpLsmtHFqoPhBtkxoHEck6pJyS7yBLAeGxz1IGpvJvISUTFf6A65gsfGZys+Ba0ocvYuIBik064aIayiVoiFcwNSg2ZhhFCHwc8IahC7ukTClplG5DiiVCzwxS4nbKhmP6/7rk8c5IQ0vcStOEKDU/e4tnFhAI6buGjVH8jh556Tsgn/h6kIaM/OgCHkLT1mwgL+VOdZgyJOEKWVd/Agn8d4HkIW43dckLgP53zStKUfjOCKOr5DcNbeDvlSspxxd4kg7YT5oYxIxEPiPEkf7wI9iTMQuxEpHAZqx3LDRJ4glVf2Cyy76zrXg8p1LdYnvS9fCIYRCAeB+W33p7kbmvyh9v2v8oJtRS75AWEQM7jvd6Lt7hfi1FF100lfWPk422Zf276KDBk1dDdFtCVXXumio3/vGcUSSJVW7cFX/ixN/tBuLiNMJZSS7OAQnydcXSJKR7GI0EcenaUyPKC2b3MU1TC+5MDOMLq6nlDMLp2moyd5w6DoFmKAnQRdXa2knpWFQdy9Ikk0G9x7v0TC/vuEoIZvcxTbA3Zr8Ho0NDvVlewuJm3Sq36SzAdFv4GS0k9iTDJujVskMwobNXRxNowr0FFFSNrmLCNRvxJsZjeGIXMIQ8izEl1Qt5nD+xEVpmdQjprMv3TfPOodW/isNc3pMlCgj6XMzM4LmYY3FP7mUUDa5kIHkWcRlQZGoYQAQS6p219hvloZdahuNSwgbGRYyACdNvEVK9lSNxo0MSd4N6WJn4DYNO1E3DAqlehIUktSJ2tjg0IG4Zn9JuyFd7Adclo49PUV3JRQc8hkJemIa1vQcTWrY7HM56IFpWNMLLqT0Lj6439LbY2nizGkER0RYw62EMpI/wd8+UqYxncPTMatKIgaRZwmhjOR93mNBuYWInVOyrDpmsCdhznueUL1lEJ3czfUMSMewKok4mlAX/C3gV97YnrzXeHKlRvYo7EGyhPgPvLFBuEh8Q0YtFY4irE1bgWuUV8ge0NAy1hcphqV4AAAgAElEQVQRpos+TShN/EUatLg3dpAWEN7vfkqY5jdF4dMpmGVkx004R6OQX+HuVYWcAfqVdEyqFu0PfIcw8+CHJEoTa4MGbXRHXM8lf13sr+EGAIvivhwNiP4bYT+49wjrA3cCbgPNPGiTvSPSylnAl73R5xnIN5BgIdlEnruIGnLrfRYEOwT3E/Flwi3JrYG7iQJpu2y5ngF0spBQRvJiXD6zX8x6IMsbsHDLOXkLCKOio3Cydn4x64m0clIaphl9A113swl2CC7F1bL5qikH0P1uaOqo2yG4lfBaOB34BqE08fEaNpHNHIVP4dSFCukSOElqqHa5wmFp2FYlZ+K+P4W8iNt5852nJmChhk1kjQ0CHYHrTF7Ia7jvgt8gOXZg9QPp2FYVl+HSzgt5GPgqbie2kM2AJaB+wDZjNIdrRrutd6ANtz79b288KUjVAOgQXOqqv7Y8C/f38KWJv0gDCChl64hEHIwGeu9ryDGCC1nJNNpwH2ohO+HkGzP34tYRcSphnvhS1v+YfAunmFXIJ6HB5IiXczXgbzn+EmiNpYlHEkoTn0ErX03Fvkpwzt09hHniNxDxXSJeI0mtRrmJGeydjpFGH+BKwmvhd0AUd7lOkukerzAyDeMqIa4xuI3wWrhJnNpX8rUAN2jY+Coz4g7wdxDeXMcLPCnwCARpu/2AuxS2TMPGSlA4iDDFZp1scqy+dqd3fDtggWZ9rzZqjO5BGKCM+xbJP0HuI9ydjFP6tFxKX8roUYQO9ApgJMhaXPDAV63cB7g6BeOqYSphk9qncH1PuqSJ/caiw0G7S9tNEe3abfUbNi8BmVMgTbzaO34JaLm03bqT3Y/bpWxJcp7s+UzlsYLHYwm9uCNp5dx6mlcx0xkK3OCNug7wEW8ATpq4ieGEDWYm0VpSmSpdWvk6LkpayFs0cwJRrFg2mZeAk/FVI5T5zAia5WTFdMJCsyfZoqCHQsSDJEVqOllMVDZX19gIUFeQON4bfgsYIbGakbjmfycRKqjcrGHjqKxoBfyCxD8C53U9iKWJfQGNzXD1IplfCwWpTLt6hxbFPUW6uJqwodpuNIi0srr0vbshSGW9QCiSEB8H+N2zv0DB38zo6+gAYDHhbuu3QX5c8Phcwl4zh1Ja1CdldGdcoMMPCp8KEqdLSifud/IN7zljQUuJ+qSMJtVjraJINlmW44LNftDmetBSoj5pMwGC+uPnKapxlCdx9SOFxEImWkrUp+5k54is5mbcjaKQ+4i8RX3Eu+Q4Dl+WUbmYqGzxZf2JGEieu/Bv2EJE5EUBpvAsblu+kBzKnXy7bPFl/YnYFQ2iM4pwCpO9BkUR90FQUzGYThYSlS2+rD8RSYVm79LEcCYEcnV9JVJjpIi6bsD+zVWBU8Rr1iWuVsRXuxkMLNLyhch1R+EIwhvOu7j+FP610ErYUG0ocG2dzKuG8whTd5/Da9YVd4NPaqj2DYUx9TOvYtqAvbyxH+J9xhL/jQj/RjO1tBSn0be4llCh7pcEDoaswaUdvu0993zQUvKyKaGlVPiuB/le8ZC8TJhqBtAG6l8TKaNOkTVUqBsP8qfiIfkFoRM4EKdqlrHgjR6CS6EvpB04DmSlN3494P2Nuu572dSLZOOIOInbY7zRl+ifEG0HmMpjSNAV2F0IUUlp1jRIkrh9kL2DL4Qj4lbCrsDb0s5dmUkTl5a4vYppQYTRsRXnkRypKSVHWX++zXYky3qOY0qwowYReZo5kTBSM4ZWjquPkUYjUyBx618L10gYbe/iPOAhbyxTmW6l5LVwuoTRdiRuBAi86h0arWR3LZSQuF2Dc6b8xRnidq1GEvbguFZLyy/XHXW7+n7092XgJEm434nbtfIDKl3SxKXkl40+gX4D930oZDkuJSuhV5o8R6jaJ8A80J3qYWGFzCR0jJ+EYJ0WI/cTBvkGAXdll2pWUuJ2IchtJU6aCfzYG9uLTIM2WkqF71yQ34XPF8XVJr3gHTiaMCsmFdJ3RJIlbjvIcQwXBTnX65lWzovLoF4kWeL2NeA4hpdpQLQZpwOep80R/CmIXqZDssTtI8CkkuecyZo41cxfDJwXF+enS0SOdu4kLOqcSxTkXK9nMkuRhEiNMrvhpYmNepAkcfsoMLHUCXGq1vGAH3U6R8Nt8roT1xLcQShxO0/ceCICy3A7Cr7KzWwNRTjqThmJ2wkCvy91njin0FeMGYhLNUs9aqnJu6wdwDESBkHWIS7I9V1veBfg1kaWJjbKoUmpgvFOnvj1BwXI4oTztgHuBs0ggKlJqYLxTp749QeFTKQ4DRHgYAjqhNMiSeL2Wbzd1mIkj0vR8gVvTgXNqp/ULMJ04PsISwYKkBUki3xclYU0cbqOiJO4XUwocTuJqYEKjU9pLy4K8rnrS1RS1vMEokCFppjzunIPA2ni1ji1KD0ivkgocbuCJkYQBV/QYqb4uYeAu0HewkzSjtQkFZo9jVOKKM807geu8kYH0cldDStNbNQchSMJ1UNW4upCyl4LEuThdg2zQMPCwXozmVDi9hnCtNAAgR8RBokG4RbxqUkTxwvt+YQSt/eKu+l2xyXA/3ljqTdU07jWhvB+N1VCGfEkTiOUJu4jDdWMYrQfbpfSF0+4EsRXZEriDOAJb+xTwJQaGFcFuh2uZstfO44FCTMPipB23I7lcu/AWelLE+vHgMgbXI1zpvxaXg9Zhtsp9gPON6cvTazjScougpPjnY8yyMOEgkmZSBOnvSMyi7DxV9LNL5mILi/O33q/kihQuKkPbnGaVGh2KVFw80sm6lJjKMIp3KQlTewkbpN2k8YxJbj5lZojOVLTwd2ppZpNT2z85WQ9o0CXvxRJkZqDWN7oTaSMWhBL3CYp8Y0T+FslcwgsIVzoDgHu0FDtqS7EErf+jWU1cKyEPSpKMYlQpjvthmpnQqDE9yJlI5Xrka6gUBi1HKmkKtM9CwIlvgeAyys5WaBU1PLyWIHL6DtcQSgn/QgVN62ULqnqoNcMqB+EqxOawzlTYeYB4qublkCSruM48JCWNLEOwe22+g20zwLx1U1LIEnXcRx4SEuaWPeFoH1CrOgopbOLirkc+B9vLHVp4vQckVZOI5S4dekAUZAOUJqI0l5clEJXYCdx6xea/Y4dAu+6PBGzcUVShZRaENWW0hK3NxGxsMrZkiM1z6QgTXwx25AvUWgW8WTF8zhVsKRIzZlEgea/sQFRRuL2ZnHXSDWUkun2+1/UHF2XqhFcC2dIeH2WJJYmTpLpPlND56DmdCdxW+k8ZaSJb9LQOag5cS8Wv4v0MuA4CW0qiSQvVgfgBBEatKGaUYweRbgjGTuZUj7zoAhJ2tnMAXeCptFrZhrwWW+sssyDIuRewp3NITjlJt85qDElJW6/A9JdZ3ifqYQ7m0mlB3VAB5G82zoZpLvsogJEcTuvfibPcFC/x03dSMcRmc5QNFCXySMcRxQUSHZPxGVk4cWVlrgdwZhgl6Z7NmEs8Fdv9EiiIF2q1iRJ3P6RLXogERl1bWcG0sSTiYJ0qdoRkWNtYqHZIqIiWc9K53sRCcQSXKQmClJEjA2HJInbp+hBkyeh5LVwkYbpUjVD1xWvBimRi6UH8rVCCZlumK9hulTNKCNxO1HCHctuEUrIdLtUs7pFLdUVr17nDeeBEyTcpamEKwkbqu1OQzZUM4rRUnWsp62XuK0GmUdY6xWLU2gd13N6OGHdqCdxWxUTCGu9ktKlak2SxO3fcM2Oq0S6+kn5tV6ng/rpUrXmZpKzi3rQYFpep0TQBnS/nhhXLel09hbyhH/8FUzj0R7OqAzgJNYkbE9HDIwXx7VHeQl/QdHES4HEbaVcwDvM5PN0sKd3pIooSZX8//bOO8yuqtz/nzVJSBBEiEpRioKiVEWlWBCwXQGvXgtJIHRUOlIltMyehB7pEHqTlgS8V6XZrhT1iiA/FQSVokgRA0oAE0lIMuv3x7tnsme9a5/Z58w5e++ZvJ/nyaOsfc4+a2bO2mu97fsmdAE/IZSuHcMjEYnbovd8LJVSHuiV6WogPjB0VkY26ND7ECoYFaebW0n4GKEc86iKFM2MjpIe4H+KHFizPBqRuC2Eg8c9kbWgIwztZGVEkjF09rS8FhzclsrFrhRc6uSe8SZ0/5ZeJJ2pVRLk7xseBN9Ii3/jAnQBXwrGXkmjG03jwKcpZR8Kr3lYsdXvqlEKK6CbnP4bXJj+2AwHodU3QZ4DSk2uTbyK9LPJ8vc0StMCblEaKdokuNArBpUrniXTHA+gnUKPRyRuC+KeBf8xREgiS6f+DoAfhxi3obLXg63/3tw94LdBREKyNO9gb5GtEc9XC9a5YRhDZD6y/sKeOoZ4fD2NFNwMw+gEk5C1F/ZaaidfRRSTQmO3LNZHfsb5FX2+YSzP/AVZf1tX19DQMAzDMIzllYlI34k3DPZCwzBGLmaIGIZhGIZhGIZROuXUiBiGUTZvgqYV0AbjDHQ9hWEYRiO2Q9SELqaYYtg2iBR1IZljwzCGN2aIGMbIZCy6uHCo5HbmNgzDyOFMYCukIfHB5CugvQWpGdkn/e//QTpdG4YxgrHULMMYmXhETaed/5aU+hMYhjES+BLS7HMz4BeI4tPqmesO6bnyKNLT4P8Bn8GMEMNYLrCIiGGMTF7EikANw6ie54D9EXnpU5HGxjsDi9LrP0VkXJ9EGuTOQfewMQxjhGIREcMwDMMwOs0jSD+xbZEmcmul429HDJCNgNmYEWIYyxXlRUQSNiXW8G6q6q5ZjGlsTS9vDEafJlGdytuLNO4LPc1PkfBEC/fqwrEDPmi2NYZHOKGlDrxFP3c0sH3kykMkvNDC/VZBcoAHshK/5BgWNH2/opzGatGmlvDzlppaJqxN2K3UsZRu7sY2xxGJh1HIWggb3j3kaH4teHLWAtznOtivwEsjKtXwDvhFKw3vvBwONwqGlwJ3uw6uBQ/rIp3Jsyx2cE+L99sI3XH+ZUfLzXSLfq7e7+AlJ2lHrdxvK+S7leUZB39q5X4VsgKwMbBeZuxNSHH6nUhUZITh30O04Z27v8X7bQG8ORj8O7jft3a/wp/7IXTDu+fA/aHF+20HjAkGHwP3dGv3K/SZDnneh02K/wDuuRbutyLS+DXk1+Bebv5+hT93JeDDkQv3g2uhmaJfHQi7qHvgbnBFBCaGRHmGSBdj6eU2pIhW6GUx09iOqbnFa3ESPksvtzMwojOfUdGNuN1sCFwdjM3lFLZo2nhwHIfn5GD0cUZHD9ftI2EJCV8ADgmu3E/CtiRNd3a/Gt1J+HaO4X9bnmMRNuBVHuU44BPBlUuAA5u61wxWYgE/RDbJZXi6GVpn56oYTfxgOhQep7MdwkvHwVIPnwcOCy7d72FbR9Nr4RJg12DsDuBzrc6xIK8CU4BPBuOXIWkxhfEwDrgdeF9wKXGdXwv/Aq5g4EEVD/s7+VkKkxo1PwfGDxzmi3TYEEH2pu8DK2bGlnjYzkFTXbW9PN9+xMDD03xgyyHPsjy6kJSsacjf5ZfAPxBD8TpgMhItmQmckl4bKSwFvsNAQ7IX/E7gftjcrfyWyHd6hczgIuAjQ5xjEVYHbmOg0+YV8B8A9+fmbuW/DlwaDD4HvH8oExwc58FvC/QEFx6V3637d5M3PBf4ejD2M/SZpN38GxF++K9g/DvAV5q7lR8N/DfaoDofXGfPcCnlpWZN5UHgm8HoGHqZRTJgo2hMwprIwTec+4GcVIJ3KOEa4NpgdA0WcwNzlJWdzzS2xZMEowvpYiLH8q+hTbIA4zkaeDAY3QqY3tR9Eg5BGyHPsAJ70ekowgSWMpo90ZvWASTs1tS9FnAhoRECd7MxpwxhhlUyHtns2/nvP0r9CcrjaOC+YGwrUE6Chng4CG2EPAvs1ckoAoCDXuQw9/fg0te9jDfDRWgj5B6a/H20goN5SFfvxcGl83wThxQvntZZoPaWcxx8b2izHBwHD6H3u9HALK+92bl4WAO4Ae3BPcjBH4c2y9L4FLLXXAOsDBwOfAw5eIKsvw8ixuHhSMfl00FlPAxT3BPA14LBLuA68G8rfh+/KpK6tkJw4WhwLUXamsPdAZwdDL5J5uTDOTXAbwqcEwwuBfYEV4YBejLw42BsY+CC5m7jd0EbIS8Bu4PrsLCL84gK3VPBhS+DP6jJm52KNkIeQpqNlkK5NSIJ5yOWV5Z1kYN9mBoRe38X8lAOQ96XkXB9O6ZYiJU4GFH4yLIDf2BKofcnjKeX6wgjUo7DW05Va5bDWMQoJgKvBFeOIeHzhe6RsDla630JXezK8SV5zk/kORx7IgexLJcynfcUukfCJGDvYPQFYDITCuneG8MYJ4feyei1cLQXL+2geFEE+lYwvASY5Ery7jqYi0ifhmvhEk+xteCl2/W+wfALwG6uWA+IIePEKOwOhscBc3zxw+mZ6NSFB4Djhji9wji4ELgpGF4HuMYX2O+87M/Xo/e7K9zwktI+AjFsr0NSX89Df0d/hxgneyHe3kOB1UqcY4dxc4DLg8G3AjeCL+DA9A64CnhncOE2xHFQFlPQEb0PIT2mCuBXQsQIwvT2bnA/HerkiuF6gd1BZbDsC37PYvfwG6D/nh7Yp7OpZVncPOR5HUbtz5YoVRH8jogjIMt8YAK45tPbW8QBWyMP/qfQX/L2k7Aqkic78LMch9E9iEWaMB04MRh9BNiKhGZDakNjGpvQy/0MXFC9wKdJaLSgHAnfBXXYv5mECe2e5qD0sAueOcHoS8AWJOQvqISVEQ/WwAOO45t0M6Pd0xyUhDPQHsiHgK0b1otM590s5UEGHnB6gR1J+FHb56mZD6wEbIAUcLaLN6AfMEPlv4EO5yEP4HLgq8ia73hkyktI++ZgeB6whYO/Nnjfysgh973BpSmu8AbdPjycBsop8hCwTaN6EQ/vQjzXQQoJOzpKWQvZuXQhKW1hFG6W01Gn8L07A7cy8LD/MvABJ9720khrdx4E1g8uHe7kQN7ovT3A1GD4EWArR8f3u0mIEXUXQ08z2QCJTD0QjP8YiZasjqj89TEeOdx2+ju3PlKTsgBZwx3Gj0Miy2Fkbxq40PAO3/sNJA0oyzNyL/dSu2ZYDL8O8BsGRvY88GVw/zPIe69FpJqz3AV8uoxahGAu2wM/YWC0cQGwZeO6Fz8WMcbCw/6Z4EqLImTmcwzaIfwE8MHG9SJ+beTv+Jbgwu7gbmjnDHP4C/AOZF8q2RABmMaW9EbyHLv4aJrCpelhBzw/Rn9ptiJR0YlySPgq2iqWPMckxwvawzF49aV5knF8kCnKI1sOCRcDBwSjkueY5PSOkAhUmPJxJwk7U0VhtxTg340OMV5AovL/+96TtzGcTMJJ7Z5iDp0yREYCpRoiAF68i2Fo+z7g406nC/W95zrEw5blB8DOTnt+O46XSOtdiIc5y0VO14X1vWccsrluEVw61cEJ7Z/l4HjxGP8GXWy+j5M0n9h7YockkIhOGJ0oBS+H6l8wcL9bjHynwpTAvvdsjz4kLQS2TtO+Ok07DZE88gyRsijZEAHw74ao4+uz4MJ0ob73fBD5/ozNDC4GtgfXVL1R+/CfQ2qglLEPLsfY93sTq6+FLcB1TpynIX4aqL3+98BW4HKcNv5CpD4jy/3AtuCarSlsA94hzT/D6P0ccBNz3jMKMfTDtX0puPAs2Cn6DZFq5HunRkPkY+llNqfzJvX6U1gDH8mTdRxcmRECkHAFOkT+duDbaRpZ+PqtIsXpi+hiYmVGCMB4DgeVErYtqBoWoYf90UbIc4inoxp1KTGYJqHTYA4lUTUsfZyPNkJ+hi5kM5YfjkQrG21DzlrwkvsdGiFzkcNy6UYIgJOUsF3Ra+Fgnx9NOBdthNxH3jOgBJwcTndDp4Rd7LXCS58BdhPaCLmwKiMEIFXoCj2lY4AbPHq/83Iwv5F4XUgZRojRMdzj6LqCNAXPr6Vf79+IfBfGBheOq84IAXC3IftnllWBWfF6Eb9h5PW9wB7VGSGAPN/CguxYDUuK/0+0o+plYGI1Rgik9SL7oKP2E8CHtUl9TEMbIb9H0ihLp7o+IgnnAN8NRjdgYRBhSOhiMdexTHO8j1l0q6LxKjgIXTSo8+4kJU0XmjmOyY0ClcVhLAImIMo7WY4j4TMDRhI2xatF2gvsmRsFKotECoPRxtCVTA+ifZIGFy5SKTTLiwIZIx4nCjQT0Wthig/ShLw0YQvTJXqByU4XjZeKkyL5mGNgpg/ShDzsglbWmofUt0SjQGXh4F60Y2AccKPXeeaxosuH0SmbVXAe4rXMsj6iENaP7y9iVvvdbKe9ycOd6cT3nRGOm4X+W64O3BCpF7kELWd9J7povAqOAaV4GhH58OOQupCwvuvk/ChQWbg8kY/9wQfOVr8uEonNRoHSonH3VMemWAiXJ/JxPvjA2ep3QDtGFiB1IU1LvbeDKhsaesZGq/53ST3ufZwIfDp4zeOsqLwK1ZAwny4moHOvT2Fa/6bYV2j2juA1t9HNhR2eYTGkD0pc2SNBlD1m0FdotmLwuu5B6mLKI+EOtDdjVZYymyQ1AhPeRV6hWaO6GGO5wJG/FjyyFjzkFV32OO1hqwQnB5azguFVgdk+dYh4SQmMroVGdTElcwpa5WYTMh5WH3P+pEWXrfRRaTeu/8Ci9ruv+IFS48dD4PwR2ex67Hft5V6kJmvRYC8cgRyEjm7twIBMEX8gKPXH1MHgatDXyi1GDr9hjcrR4LNpQheiVfjuRbzyNcDNRYwRJfKR9oABfJ4K3/mD18WUhYtFsFMj0KdGoF+DnGhr6/1ghk61ndWPYx5dESvOcy7T2IJpbIcu1lsITChF4rYoU3kYx1HB6Gh6uYlTeTMJ30C067M8wwrsTZ0a5SXMAa4MRiVNYA6jWMBMdJOzu9iY00qZX1HWYgraU7MlcCrnMxaJTIXNwb5FwvfLmJ5Rf5wYGVGVm7QJ4kXE5J5LqmVpguOIq9yc7ulfC2F60FllSNwWJU1xi6nc7OdhD9+XDquVqA6sk8St60/hUCo353j4gJd02LBoeSEw0S13UYORjluIGBmh6EAP+E+A3wztREhTLkuRuC2IexodTXXA1eDXAz8R2C+4nqZcll2c3gj3U3TB98rIIX5FRHQkVOF7kBIlbgtyOhD2pnk3cBn4PBW+q8F9u4zJ5VGtIQIwlV+hlbDG0csseqOW2zdI+G05k2uCbi4GpT61Dq/zfbRyzmK62KU0idtmWIVDkXSGLNvxKD9Bq138nTHsWjuJ2/1ZjDzk5wVXjuQlfohWu7iPtaopyDVqzTfQXsvtkCLivYLxucCuZUncFiWtF4mthcORgvqweeqvEK98rUi73O+B9lrORFJ8Q+WXyx0lSroXxElRa/isGYt4W29CNxk+wun6PWNE4B5Bi0d0IUb1LcQyD3A/L2NmzeFuQUsIr4b8DKEzR9K4W+pi3nFOQppFZtkcUe87PBh/BakLqVk0z6W/X/4WXJiEROo/FYw/ikhlV0r1hghAwgxQ3ugNSdMgMswhaa67bsnsB6qp4kfQdSHHpQZY/TiS1xAZ0zDitH3w37049uQE5pYyr2ZJeAqdI++Qg2SWlxnFbqnxYhj9pCk9xdYC7Fl1XUgeaYpVbC1sH7z0ZcSYquVaSFPewojTykiEJ8sj6INDnTgLHXF6N1od7GYnNQLGiMVdjRgeWd6Orgu5iwqkwJvgKLTIx4fQdSFngvtBOVNqFpcn8rE9Otp6ELgny5hV87gXiIt8bB/8dxqVcwvKmFUj6mGI9DeCaZif/yQ6b7teJH2NYBr0rYA76a5FoVk+CY/hVLg15GS6Vd52vUi4DRrW4Hgc+3JSub0FjOGDK5aff2rZfTaaxUnjs1C1JmS/svtstEBC4xqcBUhdSLl9pZrALdvvGtXg1H+/M9rFQUCj/Py51C6VKcTliXxkuR+dal8zXJ7IR5aLwd1Y0oRaxN3D4DU4h4L7XRmzGYy6GCKQ8BJd7E48tUFUnZJhkCeb8BD5eYPPsgJ7UKe6kDy6uQlyVcnuYeO6FJoNwniOQXtq+jifbqVkYxgDcJI2k6dYNJzknr+JSMnGuMBJw8pa4/pTO3L7ThziqFDSvSBOUuUa7XcTHRVKuhsl4hYgqTMxB+ZSpC6kltHWgbgn0P3I+ngJ2CUtcK85Liby0cfvEIn34cDJkCsidCO4K3KulU59DBGAXt6OrgkB+BsrqZSnOrNOzvhDHK8UJuqJ9EFZO+fqvbWrC8ljAW9A9xXo4+4SZ2IMU1JJ1dy1kNZhDAfegK6l6OPuEucxVMYTb0D3OtL4bbjwNuL73fPoFF9jZLMGYQq38E90zWadyTv7PIb0GhsurJsz/stUaGA4MA4RG4pxd4nzGJT6GCIiqXppztV3soDaWG8N6WFHUApafexET7yzce1wnAB8MufqCSQd67bbThyLuBJYL+f6lSS5DxzD6OM4tIR4/zWfv07qxpVoCfE+rvD566Q2pLLJs9GFvCAHue/4+LVakcom59U7vgOGyX5ntAG/BpJ9EDuPrQ58O+2eXXP8VkhvmBjbUPu0rD78AUiKfYz9wYcKqHXlfKQ5Y4xzwIcKqJVRD0MkX1I1yyQSpdpULxLWxkdlJJfhmUGiVJvqRcLH8UpGMosoeyS53tV60MNhaNnkLOOB60mUUo1hAJBKqiYNXtKFdMgOJRFrhRdllC81eMlqSJ+Uuq+FC9GyyVk2Q8tw1gqfdlUn0lU9w0QPe5czI6M6+iVVI13V+9mR2qcD+dWINWweyIngQ9WmmuE3pXGzyLQnnH9HOfNpFT8BLZucJe2D5WvhtKmHIfIS30JLqv6RmFRjwnvLmVSTyGH2RnTqQ5ivLEZX0tDoqg4xLmKyyeHP8XZ0l9H6MI0P4pXKyOtIs7os2zJsPDVGmXj610J4OA/XwhrAVb6ma8HLs3VGMJy3FpIy5tQKXuR79w6Gn0dLEx/iGxtdVfMtYOtgLLjZVp0AACAASURBVLbfXeh13yZjZHEiWlL1CXSvmVPTiEMN8WnfEBVtDZ+TqTSxz0sXqhi/MnHZ5PDnWBW4IW1yWEP8u9ER1V50T6XBjK7SqN4QSfgccHAwOo/RfBZpzpJlJeBmzq5l6D1BNvIsDyON9G4NxmOdveuAQ9I3QhnJ2azINkieZ5ad6amhpyZhZXq5ATH6shwL7IyWYz2BntzUG2M5JDUqrkDXhsxBDpFhDn+ss3fleKmluBG9Fo4DdkKvheN8fhpaZXiRtw17FfQVrsdUbq708M4y5tYMHj6H1u2fh3x/QmnilYCbvdT2GCMO/3G0E2wB8AVgSjC+AnAz+LCzdx34BjLnLE8irQuuC8bXAmaBj9VGVc1FwHuCsR8gMsSh4M1HqE1n+Cy+rydRKJt8GvJcD6WJDwC/Wxkza0S1hsjJrIPkRmY9iR7HPpzIX5Eus2GDmU15tR5WXD897IB+cIiUb8K/WYH90IVaE0j4ainzK0rCMcDng9EngK9zLP+ii90QRZdleE5jmuo4WjWXoB8ot5NwXipN/I3gWhee6zilYXjcWL44Cr25PgF8zZEr032qlw2qTlyMXgt3AOfkSBN3Adf7xqkipeKl6HI2enOd7uAnqTTxBcG1VYHZvnGqSKl4KeS9hnC/g30dPIU4s0KVm02Ac0qYnlEq/q3EMw8OAfcocC7SqDPLuuTXFVWE/xDaYbwY2B3cK8CBaE/8DoiCX43w+6AbNs8F9gH3GnFp4mPB/2cZs2uCs4g1bIaeVJp4L7TT5hLwYd+aUqnOELmUMSxhFpKnn+VsutNmTwl5DWYOIKFyKw6AU1gDzw3oB8qBJOkCPJ4XiTeYuYBpvK/zkyxAQqzQbBFdGdnkqTyINrjG0MssEvV3rIaEA4DJweizrJBZgN3EmkitwWJuYE5UxcZYjvASxQy9032Sqq8COKIy3aOBWT5fpa1UvBgZuwfDzwJ7pb0s+qSJQ5nu1YEbfVzRqQrOBbYIxu5l4PPqaFBNYrdEJCwrJ629uQn93TjPpQfOjDRxuN993etnmjFs8XmZB7PAXSP/13lgX8RAzfJl8Ad1dn5F8W+EaObBMeDuk//rFiBOm9eC10wD/7FOz7AYfkPgvGCwF5i8TDbZPYHu65P+HX3YeLsi/JeJZRfBpGWyye4OtGMj/Tv6ypw21Rkiz3Ma2nv4AHD8gJGEPCvuUhLVfbRcErpYzHVo7+EVJFwfvPZenDroj6OXOZyhPH3lkoj3EN0B/gim8pvgtedRV09NEs15XEIXkziefw4YXSnaRGoHHs3tAWMsB3hy1gIc5YLwvBNVkrAPzTrANVXXi/i4J30J0jk9POgejF4L26OdDqXjYRdQzVVfBHZzGcdO2g1+Mrr3xtFeR3mr4FTgo8GYcuw4iZzHUs0u8TqyZQxPjgVCT3okOunSQyRh742zwYeGeRVcgu4Afzuqcap7GJ22mhrmvmKnjR+HpNuGZ7AecEHjVDcHMSCzpJGtqlPNfOwMljZOdWHj1CnAL4OxWGSrNKoxRHqiKhAvM4qJJKpICxJiVtzKwA0klYbeT0TnUz8CKvVH8EwHfhKMbshrnNv+qRUmVYFQhWa30M3Fkdd7xuZ4anqUNV4eM/pUIFT90ElMjfQWOIY8T00PCTXx1BgVcCW6tuBWYGbO6/dFdyP/HFQn0+37FVFUbUG306muOPrXQtiNvMdTnUx3KnEb1tJ5pAO86kng4t3IHXC1z+8L0HF8vH5oPmJMLQpf7+BOpKA9y8rAnDRNzRi2+K3RtQULgYngwnotwP0KlILlWETxqELBG38gqKyUZ4C90mhOgJuJRASzrA1cW7E08YWgslLuQUfE+zgEaWqYZTvkLFgRfgzkZBfhvqdf7xYjBm7Y0+5w8GE6cimUb4jkSdw6DuIktaEvY62aWXEJsUKzhcBuJGpD73tPL2PYHQi7pO5LD3u0f5IFiEvcPo32Qi7jOOYhOZMDPTWesyqTJl7ATLTCzE9pJOWZ8Huc6vkinpq6SxMbbSdH4vZpYG+nPdQAOHgZeaiHDpQZXufqlsVFaInbu0CpyPXj4Pdo59Ao4Fqf3wSxYzSQuD3TafGPfhzcjPYMjkekiUv3WnpJv4lJuu/vtPhHluOB/wvGNqdCr6UxVPxqyIExVFs6AtxvIm/o4wzgh8HYu6gsC8Fvhu48nqbRu39G3tDHgcCfg7GdyXPcdpyoxO0LwG7gcho2u4VI5DU8400FX1U/qdNB1enq7KIBuKfR9YGp+pkvvZ9UuYaI5N9fi97YZtKtrOWB7E++FZfwX22bYxFOJa/Q7GASHmr43hOYi2M3QqlGz8WlSxPHJW4X08UkkkE6wCfcjzbEqpEm7iGv0GwyiZLEHIhEfXI8NfWUYzXaj5dDXrgWlgCTnH7mDMDB/cBJwfBYxINd6lrw8ozcKxh+AZjsdI3aAJw0lL0xGF4b+HYFqWYxidvYMyfGN9Bey5jjqKP4frlStd9d6vTveQAu/e6hv3uH+ca9kYxa4vMzD3CXNH6v60Wkq/8WXJiYFlmXiM/LPDgRnM48GIB7BXFghk6bM8Fv064ZFsPHlEvT37MLf88B7hHgsGCwC6mzKLmflN8JOCIYTH/PTmcXDcB9B1TWS9oPplxp4nINkUeZhg71P8wqBWUvk6inXhZ4ktsxuN04XucKdKHZHBKuKnSHbu5Ce+plgZclTZwvcTuFqSrylHePMxDFmizlemoSNsRHC812J1GRpzjjop6anejh8DbM0Kg5qcRtbHM9zukobB4z0J76WGpRx0glbsO11wvs7qTfRhH2R3vqS22oliNx+zJiFA6yuYKTyPQEtDTxiSVLE/eg97tY5CmK6091GRCNc0jPmne0Y4JGaRwOymHaOPNgAC5P8GYm+M2HOrkmuBiUw/SH6D5FObhfoz31afTTN2rw2UZ8XgPtM8D9qNg93JUQ1AFLP6mr0yaVJeDzHKYHgsvPLhrIEUAYjduakvtJlWeIiMRtWAgsuclHqjz9fBJuIW7FzeJSFfJsPz18k7jEbZibPBgngapd2IxXiy7oIXMpMVnPpCmpSM8K7EvMU5Ow75BmV4SEvEKz6SSqFiefKcQ9NZ4zaihNbLSfmNzznej0g1zS1K2oTLen8zLdae1AbC2c4uDHRe+TShNrmW44zevwf9vJkbgFkbgturmSpj1pmW5J0eq419JLsf9xwfACYILTaR25pNLEYf+UPmnimjZUMwaSK3E7CVzjzIMBuFjtwjikWLqEXjN+X1Ap5HOBvdOoTVHOBsLahfXRTfg6xdnotNlYLc5gxKSJP0sp/aR8nwqfzi7CNc4uGoBbhDhtQmniKeA/M5QZNkM5hkiexK3LSNw2w/gcK+75DltxCVvhVaHZIuTgHf4hB7tXX+g9zKk8mB52bX2ShT77AHShWZ46WWOO58W0v0joqbmIhE57ai5AF5rdy8ZKnWxwEn6NPjjUS5rYaDupxG0ojfocsGdeXUgejnyZbq+/p+3mPOD9wdjPaKHplouoOSGH3lleF0S2jVTidhZa4vZ8p9XJBsWRI9PdYWlin8ofE0nddVqdrAhHoxuqbUUtG6oZA/FvIq7Cdyy4otHWLD1AoObEJtBpwRu/CbpXj2Qe9EvcFsWlak6Eak5fAV8wQtQq/stAKH8cSNwWxfX1kwod6aeADxXy2s00UKI6MXWyAjjpFTeQLuC6sqSJR5fxISxmPXSo52W6mdPS/Q5jEdP5MktVmL2XhBWiylvt4S2EKQOOx+lWm0QxEp4lYSfCA4RvnMs9RByOBfggJNzFL5mqZD2LMZV7SPg8ugt154pcz+CNvMYDSFHWMsZwKxNa/P0lnEMPL+JVis7aDFInYAw/0rqH19DpEfdFJG4L4eBeL9Kc6wSXOrYW0tSyB9E/x21prUErnIc4ScpcC32pBtm+Jr3o7szNcDA68gxikAySC94ysf3uFScH0qZxsMjDl4HQQ9nrYWxMecuoDeuha89eQ6f1FMT1gt8DLf+L9PWIKW+1hTXQtQjPgiueeTAANy9tBhhGWRdKPU1MeastjEI/J38L7qnWbuceBr8zkhabpYNpZn4FJJU8/Dl+nDZfbAE3G7xHoq1Z1qZzz8kBbI14/gqHvQ3DaBvzkfW3ftUTqSGXI7+bE6qeiGEsZ0xC1l7Y6X0ksT7yM86veiKGsRzyF2T9bV1dQ0PDMAzDMAzDMJZbzBAxDMMwDMMwDKN0zBAxDMMwDMMwDKN0zBAxDMMwDMMwDKN0zBAxDMMwDMMwDKN0zBAxDMMwDMMwDKN0zBAxDMMwDMMwDKN0ymloCHAKa7FYNceaT8ILtbhfUU5mPZYEHXPH8U+m8EpL90t4B6FBOJT7FfvM0cC6kSt/I2Fh5fcrygxWYgFrRK48RUJv2+6X9OtdGyOMtJt37Lv7vNMdc4dyv785OrcWPKwE8bXgaH4ttPt+TXzuasi/LIsdPNPi/cajm3S1fL8mPndN4A3B8HxHa/uTh7XQzSVbvp9RJn51pOloltfAPd/G+/27+Q7nTX/uOsCYYPBlcC02OG33/Qp9pgPeGbkwF9yCFu7XBbyjffcr/LnjgFjX86fBtdDEtt33a47yIiJL+ADwBPBk5t+fSFpo5DaNzVg84D5yL9iwbfPNYwn7BJ/7JAv5BYnadAanh4lIU5fs/e5jISu1b8IREpYCZxH+HK13MD4zcq/ZdPDAAsACHHC7+mzHMU3fK6GLBXxX3Uua6ZkRMnJZCnyL9q2F0yP3mkM536HbIp99bLM38bIv/E/kXid20ghJeTPwm+Bz/+qjnaQb48UA+TX65/hC22abT2y/e8K3sEd52DRyr8eQcaP+rA/8kYF/vz+D36L5W/n1gD8E93oCaUzdaT6PXku/Bf/m5m/lPwE8Fdzrd8Dq7ZlqHs4DJ6J/jtvAj2r0zhyOjdzrh0Ar92qGpcANkc8+vcX7XRW51/np53Sc8gyRbm4Hzg1GVwVmk7BC4fskrEwvc9DeoZNI+PnQJlmIacBPgrFNkD9acRLeheeyYNTj2I+Evw1hfkXwjGVf5EGQ5SskHNTUnRJ2Ag4PRl8BJpLwesszLPbZ8+liAqHn2nMy0/hok3ebCnwqGHsUOLT1CRp1x4mBsC/iEMjyZQ8HN3MvDzsCRwbD84HJDha1PsvBcbAAmAD8O7g0zcPHmrzdicCng7HHgG+0OL3CODlUfU0Pc6WHtxe9j5f3XIX2ft4KXDSkSRbAwR3A2cHwG4EbPYwtep80MjUHHV2Z6kZ21/MRhLsP6A4GxwGzwb+x+H38GGAWEuXLcja47w1lhsVwFwE3BYPrANemkYaC+DWQQ3R4/jwQ3B+HMsOCHIQYPVm2R5yOTeC3BnqCwYXABHCvtji3grjFwGRQmTNHgm/S0eIPBnYNBp8B9koNt45Tbo3IWhwL/DIY/RDNWXEzgfcGYz9FvPKdJ6GXMewOhGHV/ehhj0L3OJ+xyOaySnDldLq5deiTLMBxzAMmgjIWzibhA4XukbA2cC2y6S/DcSAJf27DLAdnKg+jD3+j6WUWCW8pdI9pbIccvrIspIvdSNTBzhhhOHgZmIReC2d5iq0FD2sD3yZcC7C/k2htx3Hwe+CIYHg0cJOn2Frw8HHEKM+yEJjg4F9Dn+XgOHk2hk6atyKH+KKexsOALwZjTwN7u/IinFOA/wvGPgic0cQ9ZgIbBWN3NXkPo3rOQDzlWd6N/p43YgawTTD2AHD8EObVLAeC2tt3RtZbAXwXYoSsGVy4HNz1Q51cMdxC5BAf7u3d4D9Z7B5+NSTrI0wtOwLcb4Y6w2K4PwNfDQeBq9PIWQH8Zsj3KssSYFdw/xzqDItSriGyP4uRDT/MATychP8a9P0JXwV12J8LTG6pJqBVTmAujsmEYSvPxUxXm4bmJc4FwrDsr1hLeU06S8L9wEnB6FgkShUaSeF7RyPemfCAM5Nu5TXpLAmXIA+3LHEjSb93dXq5EX3AOZipymtijFAc3I/2iI0FZnvtMBhAWhdyE3otXOLgxvbNcnCcHGzCDX1t4Nt+kLXg08M+ei0c6rQHsdN8A0nRyvJxtGdZ4cW5FR7UFwOTnN57OoaTDX0SEG7oh3ltJCk87APsGQzPRSJspaRMGO3C9SJnl+eCC5PA7zP4+/3n0If9l4GJ4DqbeTAAJ9kOOsI7A/yHC9wgAcLD/iPorIoO4x4BDgkGUyPJh0ZSgE8P+4SH/VvAXdKuGRbD3YI4K7KkRpIPjaQAvzJEs4tOBPeLds2wCOWrZiU8DezFQK+UhNGTaNGPMI1NgPOC0V5gdxI6XKQVoZu70JGclVjKHM5Wf9hlJHwFOCAYncdoJqaGWrkkzAC+H4y+C7h8kHeeDCr96WFW4eh2Ta1JDkB7nnciUR7iZSR0IbUAYZHWbBKuau/0jGHAWUCY4lBkLUxDpz/9Hh2pK4sDkZz0LDsCR+W9ITVSrkSnP81xcEV7pzc4aWH/RCBMcTjB67Sxfjy8CfFUhulPU5yOxnectCh+byL7nY8XzYK8eBPgwmC4F9jD6Wi8MSxwLwK7oY3ImeDfl/8+vw5wDdqRsB+4MKW0BNyv0bVnadqYD9PGMvjt0dGbNKXUVZB54K5GothZ0rQx3+hsfBS6zuxJdHSiLI4E/l8wFksbC7kYnV30A3SEpONUI9+bcBv6IStWXKxeZAYrpXUhA/NkHdNIVL1GmUxF5+luyqsqN1iYzgboTd3j2IcT+WsH5lcEj3jews+fkEagNAmfBVUQPh+YwJHNKw21hST9fK10dDoJH8l51xTgM8HYE8DX2zw7YxjgGqwFn/Od8PAJ4JvB8AIklamSteDIXQunee086ONYdEF4rF6jNBw8jv69dwHXpypSMS4GJYByB3BOm6dXGCciAmEN4arALI/e77zUD9yArguZ7uDHnZmlUQ7uXvQBcRxwI/iI4I3vi7aGBeEXgPvvTsywIOcjghZZ1iU31cyvTjzaegi4R9s9uSY4CCn+zxJ7pqf4rYBTgsHFwO5ptKgC3CLiTpsp4HNEPvzXgd2DwbnAPmn0rlSq6yOyFkcB9wWjWwHT1WsXcBGwcTB6DxtxcmcmVxBJB5sMKiJzAD1MHjByKWNYyvWIxy7L2XQrL2y5JLyEpBCEEZkLSHh/8No1kbCkLjRLlBe2XBIeQj9AxFNzavAgT9gGCRNnWUQXE0jUgjaWExz9tVPhWjjPB+mUvt97pjbXA53e3ErFwcOgopN99SID1oIX79m04LWLgIlOb26l4iS6EUYnVydSL5KKC4RFl88Ce5VYF5LHMeiITOxQA5JqEXrI7yG2NxrDkVPQBuXGaOcswGlo58FDtKCG115crshHWvycwXch6aKh82AWuGs6NMGCuDyRj+nggyi3XxVJRw+dB0enggQV4nJFPsAHUW6/Kdoxk55lOy0BHac6Q2R/FjOK3dFV/8eQ8Pn+/+phVySVK8sLwG5MqEGerKSF7UMoa+m5hOm8p/+/n+csqi80yyfhPvTBfBwwhzN4Y/qavEKzK0hUXno1JFxITNnj9UxoO2E88kAZmEPpOIKpKi/dWM5w8Ct0wfY4pF7kjdAvcRtbC1e51qV/24qTA21Yo7IOcG1fvYjvzydWRZdHOh3ur4qDiavc9D87PeQWXTr4R0dnVwBHbn3kUT6T5uHFCA5rBl4EdrO6kJGCk5RynWK3D/hMTZCPpVOm0U5XTebBAFxao6JFPsBnRT5OQKdTxqKdFeF+j65RSSNRPq3783kqfLcBF3R4ggVxuSIfy6SJfZ4KXwLufzs8wVyq7ax+UjSvTgqBEtZlOu/Gc2lwXYq+Oi9xW5wkmle3MkuZQ8I4Ej6HLox6mVElSNw2x+nElD1e6/9y9yBhyyyPUIKsZ5MchFb2+Bw9HEK/qkSk0Kybi8uYnDEsOAOUgl1W5aYbXXRZR7nnAxDp3Sw7IwXTeWvhO04XQFaG65fEVKpdiYdPemnuFiu6nOooRdK9EE5Uu8LDlwOu9rCej6soeWA/R432O6MNOHGmxutFNgKfp8J3ALhSVPiK4WLOVBG8wa8CPiYw0SdxW4oKXzHc5eSIfKRGSEyFL63/KkfitiB5Ih99jrWYCt/dwKmdnVZjqjVEABJuAXUAHA9cz1K+Q+qBzHA6CT8qZW7NcSIQKg1sjljRsQfKfpykwprVIqlme6I3vUkkXExeoVndJG4TXqYrIsfqmUHC5ZCJuAlPMq6yQjOjhqSpPPsRUbnxcAla7vnfSF1IrdZCKrk7Ga1ycyZy6A2LLv9CdUWXuTgxpkLVoC7k2Xoduujyh9RQ4tbBd9B9TFZDomu3oBXaznDaIDZGBO5udGreSki0PqbCdxm4UB2yDpyNRAayvAtZm7G6kMPB/baMiTXJQWinzY6IWEnYHmIxolhWmsRtMdxCJD11fnDhRPAXE1fh2xVcpdHW6g0RgPEcgU4D2BYJt2e5j7VU+lA9SPqlGsM0gF2RjSbLhSRUWWiWT8ILdEU9NQegvy+HkFBloVk+U3kAnUc7FjlcZpFCsykqRdBYznFpSgyS4pNlf/RaONRJdLB2OOkuHtZOrYA2OBYj0rAvlzKxJnGiHHRtMPw2UNLvc5F+IaUXXRbkKPR+91HEcZUlliJojCx60A2SNyeuwpevAFkpziMH3KeCC19Aq/DdDC7McqkJ7l/AV9AiH/uh60JOAFe6Cl8x3J/Qz/YutFprKildTV1IlnoYIoeRV/WfZR6jmVSJxG1REimMpHFh5EOskqfIUBOmcg+6eDVkFgnXlDCb1kk4D63sMRDHMWl9jGEoHMRUbkJmO11QXSucqNwM5vw4tgqJ2yY5GBo6P3qB3Z0WEKkNTqJTE9D1kVnmIX1P6rvfGW2gv16k0fe1Qonborh5xAVvsjxJhSp8xXAP00DmPOVO4FslTGYIuJjIR8ip4GqhwlcPQwQgaSgVWbXEbXES7oAc+V6Yz6gKJW6b42TypSIfZ8W6FJo1JE/Zo4/b6FaymoYRcirkpoMOJ7nn/chfC7cD55Y4l5Zw/Yey3BS4aU57mGuHa3wo88C+TnuYjRGJm0u8XqSPg8FVqsJXDPcrdIPkPlJnc1USt83gLia/Ee1zwJ41qwvJIyby0cfPGNzBVhr1MUQAunie+GL8ExupHMT64nINpts4STXdqydSL5JXIHkZx6rC0bryb/K6KTvOpnpZT6PmpCk+Ya1IH5dXLXHbBP9Gd/nu45waSNwWZR5ikIS8jtTvDBf+Rny/exyrC1ne+AfxaMLfEVW74ULe2efn4B4sdSZDI+/scwO4ylX4CvI6+c/7C8GFKceVUR9D5DRWo5fr0IVNAO/l0VxLu14kbI7PLZKcSKLymetJwiS0bPKyq4kqDq0rZwIfjF7xXEaiikMNYwA5kqp9TPVahaSunA58KOfapV4XSteOVDb5WkSWMmQFRIEqFAapHX5ZgXpsv9sQrTRkjFj8SoixMS5ycU1qnwbUh9+AfEfAJ8HvW+ZsWicqm9zHEeA/XOZshsAJaJXTPs4HH0rPV0ZdDBHHoqiMZJaTSPhUWRNqiSRXRrIP0aJOeEdpc2qF6VEZySyiRX127s9ZD0Q2OVTayfIuRBHDMKJ4+Y4MuhZ8/pqvBV4ke0Ot/CwbMDzWwlRouA/sCBxZ0lxaooFscpYTvO69YIxMLqKxM+Ng8GGjzprhU8le1bA5y0XgQ0GGmuHfTlzltA9pkIwPBYhqht+WxkIXawDXpM0mK6cWkyDhKLSMZJhOI1KNCauXM6mWuAwyTQyF8OdYDbiehNHlTKlJEsaxlFvQssnhz7EZr9bYU3My6yGe0+wDxSNpHVkm0KOUtAwDLx7Km9GRgnAtbEp+XVjleFgXvblG14KvoXRvHx52QMsmL0BLE5+WdouvK0dQcL/zcmAwRix+L3TmwTx0muRM8GEzvToxA515EH6nxwE3gA+b6dUEPwqpDQllk8OfY11Q/e1qhH8LIv8cnjHDn+M/gGNKmdIgVG+ITGNLtJb2Irr4FLpD8VrATWmH73qR8HVEqjfLs4xmc3SDmY9So0KhgHPRMpL3MJoPoKWJD6JH/czVkzCaJdyA9KPJci5dfJEwL9tzIYn6mQ3jbOD9wdi9EF0LB3gpOK0VXjajG9Fr4XxE9jasUbjAw/vKmFszeEnFuh6dynQQWqZ7DBKlCn/myvGwJXBaMLwIiX58OxhfE7jJx9O3jGGPfze6K3cvsAtwXjC+KtIkMJSRrQE+1rB5HvJdD7t1b0p9RTESpPlfloeRNhJh7csu4EM53BrgHXAlWjZ5NrA9Wpr4ZPAfLWFiDan2QJ+wKr3MRms0H8lUfoNsMn8Mrn0CrYlfLdPYBDgnGF0C7MqJPIeovIQFrVNI+EwZ0ytMD7sgPRKySC8FUSzT0sSemUynbp6aUxBjL8uvgSmpNPHJwbVxwI0k1NRTY5SNFz35A4Phl4A9nGxKe6K9lpd4ye+vE9PRa+FBRKr3XrRM9zjkEB9GRCsjrQu5DukZkuUaJ4f389Ey3evSOKWudHz/YVLtd8c46StyEBCqI+2ANrSMYY8fh6Rxh+tsGrj/Rc44oax8zGlbMX4dpL9PGG3dB9yfkWaqoTTx18DvXs78iuK3B44LBucjssl/Q+oEQzGBc8BvUcLkmuEYdMPmVNnRPYyOgIwGbgL/5jIml0fVkYUrQR1iv0PCTAAS5tPFBLQVN51panOthhmsRC9zIDjEOqaS8HNApImdkviUzTVRm2s1TGcDvMoR9zj2I0kVJESaOPRmrMpSZpOozbUaEj4LHB2MSnfppL/T+jS0xOcmaC+UsRzi46F3D+zj4GkAJ1ryYTrWG4EbvD5oVoKX0HvotJmPNC3sS2eKyXRvSL28lsciP0uWx0nrv9wyme6ngtd82YuEZV2Yid7vbgMuhAHSxOF+1+N1gztjeHMeOtqacZK5xcghPpS7PQp8mNZXEX400gU+PMSeA+578n/dXEToDRy4CQAAEMpJREFUI2wuejH4MI29IvzqxDvAHwQudYS7X4Fqpj0OiVLVxGnjt0IcT1nSnkUudYS7i5C0rSzrANem0ZRKqM4Q6eFQ4EvB6NOEmvxTeRgXseJ6mcWpagGUzwIuBDYORu9iI84cMNLNbKRAMYssgDkVh94vZQxLuR5daHYm3YGM5FocS9xTc2rnJliQhDWR33H4vT6AhMcyr+tlTLSJ1FdJqJmnxigT31+MqNJ6vuXg+8HYcegGgB9C1KkqxfcXI+q14FgmIe76u+uqtbCvl/FKSWs9wjTWhcAExzIJ8bQbfMxreZaHyr2WqUEUprE+g3SA74+sOemgHRbbj0ZStMLcdWNY4iegew9J5gEukyrp/oyu2UqFDnwjoYOyOA34SDD2a1Rkwf0ArfyVCvv4mFJYifguJOVzreDCVeDC0oDT0f2kBhP2KQm/KrJv6ewiXFgacBC6n9TOwKEdmtygVGOIxCVuF9PFJJJIz4duLkJ+yVnW5nVVjFwuInG7dzA6lzFMZkJEH34VDgYeCka341FO6MwEC/I8M4BtgtH7iakuSGf7mKfmSHpUAWZ5SN3QDUhedZZLSCLNiU4g31MzfKSJjfZzBhDKMz6ALpIm7Xo9CV0EeLinOpnuNJUpthYuczI+AAd5DdVmeqpbC6nE7WzEOMxyuIPfhq938WfWWCTVrDJpYi855jOC4SXAri6i8+9EAjV8Zq0NXDscpImNRviYOp0H9ktTgALcLejo7GrAdWlEoiKiEreSeYB7PfKGE4BfBGObQ26rg7I4Aa1O9yjRQ7nrc9o8H1yYlIoOVMlVxLKLcDP1S12f0yb8O30LfCUiH+UbInkSt47jmKq8i1kOJGbF9TSUZ+0ccYnbXhx7cIL6ogpH8hpd7IbuCtxND5/swCwHp4ed0RK38kVN1BdVSIh7ajxXp2pVVZCgNbN/zyoNpDwTfoA+IMj3s+7SxEbb8bATWuL2ZWCi0w9tAFwsipvKdHsqk+meCup58gii1hTFwV0QRHHTtVCFNHEDidubXWPFmjPQzQAHk2DuGJ5cSfcTnD6YZTkAMlFcIfb9NIYNfizyXQiN4jPANWpgeTja8N6WynrN5ErcHgAu/M6mOKmZ1Yb3YeC/2O4ZFiMqcbsQiUyFZ7QU9wJxp81F4CvqJ+UPA8LfYWxfyuAeAOUAH4OomjWSYO4IVURELkZL3N5J9yDylwkv0xWx4jwzSJQ3v7OIxO1sdKHZyXSrfOuBTOURnNpMuvDckKYWlcfJrIOPSNw69iVR+dYDSbgF3bxoNZYwi0uVB7OzJGwPHB+MLmAUEzhS5VuHnIg+EGzGq+pQZoxgfOpxRm+u+zntABmAg+8AoedpNWCW1978juJhO+IStxOcdoCETIW0rm0ZMW9+GcQkbp8EvtboTa7fu6w6I0/0UkdSNjPRUaVYqsoAXL93WRnAZ3gdsTOGBzMQxb0s8cyDAbiFSO3Qv4ILx4MvudeMlzYKOk3wYnA682AA7hkkgyQU+biqfGliPx5JyQqjSoeC+13j97q70ano0lutdGliH4sqpZF6p7OLBnIW8L1grJJ+UuUaIiJxG+bgP0tcgUYzlTwr7npOb9hIp92ci847vpeNlQJNnG4uRxZBljUQD2A5oXeRuL0JXWh2Pt1KgSaPI9DSxNvwfImemoT8QrOTlAJN7P1LGB311BxComqYjBFIKnE7C725XuDgvwve5kj0WojVN3QMT+5aOMRJukFDXJ/Sn5YmPtjr+oaOkSNxuxjY3emUUIXrz7fXXkuvpck7RtqTJayzmYuIHoQpoQrXp/Q3kDGIgVs7aWKjEbkStxPTwvRBcI+jPdxpfYMP6xs6yTRimQf5ncgD3G1oyeK0vqEsaWLfF21dN7gwB9wVBW+SAD8NxkruJ+X7oq1hnc1x4BplF6W4VN0sKk3cIJrSfsozROISt73AXiRq48sn4Sx00egGLCzJiotL3L4E7BGtC8nnQDJFoymfpUcpPnWKmMTtQ+iNL5+EhYxiItpTcxw9JXQFTsgrNLuWRGny53Miz+Ai0sRwZQ2liY32E5O4fZgmZFNdvzqJkuk+1tN5me4GEreznRStF8KJYyi2FmZ6nYPcdgaRuA1FMnJx5Mt0+1DhsAP4uApfL6JYFgoDNOJctNey5g3VjIH4VJVISdzuC+6p4vdxs9C9ZlZH0mlKELzxO6DPB6nSmxss8yDLNxG56iwxxadOEZO4HTTaOhDXizjQw7Pr/uDL6id1CbHsoqaMITcPcZYsCS6cB760flLlGCJ5ErfQTaKsysHos+KeDsZ3SSMunSNP4hb2IVHzaUzSp1HNwuBup5IoJYr2Epe4nc8oJpAE8xmMk4h7ajzXc4oyENrN8ehCs8dZsQX1h25uJ+apqZM0sdF2fLwv0XwklamZzRXXr9c+gC7gOq8NhHYzBW3wxOYzKA7uIKehWgnSxDGJ29uRXiHNMg3dUG0TOixN7Pv6Eun9bprT82mIW7bfhV7Lr3ipIzFqTb/EbaShqPtuCzfM6zVT3IHYEj41eKISt4NnHgzA5TltjgH/n63OsBhFJG6L4p4jns1zKfgO95Py+yOpm1nS+bjBs4sG4H5GTj+psqSJy1Fd+DcfR3KPs/nH/6RVydeEl0jYFfHcZdmYhHFNH6aL0sunEG9dlt+RqAhNMRIeImEf5EGSZXvg/1q65+Cf2YVo0ochyFs5SUVoit5zFj2sh2f9AeNL+AQRlZ62cDpvYiFrERahdnEhx6oITTHG801eYimS75m959bAz1qbqFFX0oLoHdBr4TanG6kWwslhfV2kQDrLJ9DpmG3Bi+z229EF2Rc5vdkX5Vi5dbAW6Nxa8LAREl3N/hy9SGF3k5urSBN7OShMJUh59bCe04f7drE9Er3JRnDmoSM0hXAwz4s62z7BpY08rNiswWyUyoeR1KXfZ8YWoJvnFcQtAD8Rneb1NikydoOmLrbIp9EiEH8GVzzzYADuSfCTgdDw2Br87WnEoRN8Ch0hvgtcGKEpiLsT/CFAGD3YAS020Sb8OORZGT7vrwVXPLtoIKcgghphqv7HkChLx9kaecg3LMg0DKMjzEfW3/qDvXA55HLkd1OtvLVhLH9MQtZesxkLw4n1kZ9xftUTMYzlkL8g62/rqjurG4ZhGIZhGIaxHJJNzXorOu2oCP+P6pvSGEbVnIJOxynC2HZPZAQykdbUjr6FNCM0jOWVdWlNfnmddk+kxoyltbPPE1i01jCORctSF+Gt2f/oS81q9d/trc3dMEYU9zO0dWSpWZq+1KxW/1XUKMswasPmDG0NLQ+pWa3+u7/8KRtG7bidoa2jrUcjeVph4VMzPDWE9xrGSOFMpBdMq7RaZDaSuQHdUbgZhvJewxgJPMfQ9vdn2zWRGvIPhva7mduuiRjGMGYmorTYKlafbhiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRiGYRhV4KqegDEEElbG8VFgGzxvBcYD44AFwN+BhxnFLziJJ6ucpmEYhmEYhmGEmCEyHEnYBpgC7AyMLvCOR4CrgCtIeLWTUzMMY/nFw9XAZjmX/wl81oEvcUqGYQw7/I7A9AIv7AVeAV4FngAeBn4I7sUOTs5oM0UOsUZdSFgV2ej/q8l3bgKcBXwE+Eq7p2UYhuFhPWBPoKvBy7YF7i1nRoZhDFPGAx9s8b294GcDU8A93cY5GR2i0YZh1InpbAT8muaNkCz29zYMo1PsweDPmL3KmIhhGMstXcCuwK/Av6fqyRiDYwfT4cAprMFS7gQ2GOSVzwC/AR4F/tHxeRmGYSxj9wKv+YqHN3R8JoZhLO+sCVxe9SSMwbHUrPrjWMx3kLSHGK8AZwLfJuHZAVcS3gV8FjgQ2LiTkzQMY/nFw4eBIt7HVYAvAjd0dkaGYYxA/g50Z/77DUjq+WRgxcjrt5WoiPtTGZMzWsMMkbrTw5fwfDTn6pOM4j9yVbESngAuBC4kYRLk3scwDGMo5KVcLQVGRV5rhohhGM3yMrjL9LC/Evg5+lkD8CHADJEaY4ZI3fFMzbmyAPhcYWnehFnArHZNyzAMA8CLZPiEyKU/Ao8Bnw/GP+lhHSeppIZhGEPE3Qf+D8CmkYvjy56N0RxWI1JnEtYHNs+5eh4JfyxzOoZhGBE+D6wWGb8JmB0Z7wJ26+iMDMNY3hiTM/58qbMwmsYMkXrzmdwro4mEJw3DMEonLy1rFvA9JHobsm/npmMYxvKF/wzw7siFJcCvSp6M0SSWmlVv3pcz/hdO5K+lzsQwDCPAwxrEHSYPOknLwsPt6NStDT1s6eCBTs/RMIwRw1vBn57579HAhsBOxB3rV4GzFNCaY4ZIvXlLzvjjpc7CMAwjzu7E95FsPdps4jUke2GGiGEYxXkzcGyB13ngeuCQzk7HaAeWmlVv8oqsXil1FoZhGHH2jIx54ObMf99B/Jm1q4exHZmVYRjLM3cBCbjFVU/EGBwzROrNaznjtnkbhlEpHrYgLqbxC8ey1FEHC5FakZDxwM4dmp5hGMsvnwD+AH7/qidiDI4ZIvUmrzv66qXOwjAMQxOLhkBcJjymngX5he6GYRghC4CfBP8eAV6PvHYF4GLwXyhvekYrmCFSb57LGX8fCSuUOhPDMIwUL3UhkyKXlgK3RMZ/BLwYGd8pLXg3DMMYjGfAfTr4tymimHV75PUOOBu8nXVrjBWr15t7gOMj4yvi+A/g1pLnYxiGAbAjsGZk/FVgmo+/J5Zq2mfQnNeuiRmGsbzhngY/GXgBlJN2fWAz4HelT8sohBkidWYVfsarvAasqK55ppBwOwm95U/MMIzlnLyUqtWAr7dwLzNEDMMYAu4V8I8R767+LswQqS0WrqozR/IacHXO1Y8AUwvfK2E00/hoO6ZlGMbyi5ci88+18ZZb+HjRu2EYRjOskjP+1lJnYTSFGSJ1ZzSnIqozMbpJOJ8kd/GJAdLDLsBv6OWoTkzRMIzlikm0X7lvjzbfzzCM5Qr/PmCdnIux+jSjJriqJ2AUoIf98VzS4BX/AL4L/BLJkRyHYy08WwKfAtZKX/c/JHyps5M1DGMk4+E+YOvIpeeARYO83QHvjIz/HVjHwZIhTs8wjGGPn4w0JAz5I7iNMq8bDbwd+AyQAG+L3Qx4h9SRGHXEakSGA91cSsJ7gcNzXvEW4KvpPyGnWtQwDKNVPGxI3AhZALzHyf8Odo9HgY2C4TWBTwN3DnmShmGMVN4LvtnTzU/NCKk3lpo1XEg4AtifuF62YRhGGeydM/7dIkZIypyccespYhhGO5kHHFz1JIzGmCEynEi4DNgW0eRvlhdx5m00DKM1vOwXu+dcvqmJW+W99gseVm1uVoZhGFEeArYH96eqJ2I0xmpEhisJHwB2AbYDtkSn2XngKeCXOL6L51aS3KJ3wzCMhnipN/tx5NJLwFquiWith98A749c2t/BZS1O0TCMEUFujchgPI3Uys4BvgvO2hsMA8wQGQnMYRSP8mZG8WY8b8DxMivwAsfyr6qnZhjGyMCL6EWsGPRfDh5r8l7rIbVtIf904kAxDGO5xY8nLmoRoxd4BXgRnJ15hiH/H8Phg89jllwPAAAAAElFTkSuQmCC" + "fig_matmul_intro_q_3.png": { + "image/png": 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" } }, "cell_type": "markdown", - "id": "639dffce", + "id": "0c38af09", "metadata": {}, "source": [ - "
\n", - "\n", + "
\n", + "\n", "
" ] }, { "cell_type": "markdown", - "id": "b5973043", + "id": "db099c49", "metadata": {}, "source": [ "### Data dependencies" @@ -954,227 +822,218 @@ }, { "cell_type": "markdown", - "id": "a69b4b9e", + "id": "b5290fcf", "metadata": {}, "source": [ "
\n", - "Question: Which are the data dependencies of the computations done by the worker in charge of computing entry C[i,j] ? \n", - "
\n", - "\n", - " a) column A[:,i] and row B[j,:]\n", - " b) row A[i,:] and column B[:,j]\n", - " c) the whole matrices A and B\n", - " d) row A[i,:] and the whole matrix B" + "Question: Which are the data dependencies of the computations done by the worker in charge of computing the range of rows C[rows,:] ? \n", + "
\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "dbf21585", + "id": "ac898985", "metadata": {}, "outputs": [], "source": [ - "answer = \"x\"\n", - "alg_1_deps_check(answer)" + "# TODO multiple choice and checker" ] }, { "cell_type": "markdown", - "id": "9126a56b", + "id": "4f8dbc8c", "metadata": {}, "source": [ - "### Complexity" + "### Implementation\n", + "\n", + "These are the main steps of the implementation of algorithm 2:\n", + "\n", + "1. The worker receives the corresponding rows A[rows,:] and matrix B from the master process\n", + "2. The worker computes the product of A[rows,:] times B\n", + "3. The worker sends back the result of C[rows,:] to the master process" + ] + }, + { + "attachments": { + "fig_matmul_machines_3.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "188ce727", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
" ] }, { "cell_type": "markdown", - "id": "56a7e26e", + "id": "e5e4eafa", "metadata": {}, "source": [ + "The implementation of this variant is let as an exercise (see below)." + ] + }, + { + "cell_type": "markdown", + "id": "dce52b5b", + "metadata": {}, + "source": [ + "### Communication overhead\n", + "\n", + "Let us analyze the (theoretical) communication overhead for algorithm 3.\n", + "\n", "
\n", - "Question: Which is the complexity of the communication and computations done by a worker?\n", - "
\n", - "\n", - " a) O(N) communication and O(N^2) computation\n", - " b) O(N^2) communication and O(N) computation\n", - " c) O(N^3) communication and O(N) computation\n", - " d) O(N) communication and O(N) computation" + "Question: Which is the complexity of the communication and computations done by a worker in algorithm 3?\n", + "" ] }, { "cell_type": "code", "execution_count": null, - "id": "c068b2c2", + "id": "d6dbbf50", "metadata": {}, "outputs": [], "source": [ - "answer = \"x\"\n", - "alg_1_complex_check(answer)" + "# TODO multiple choice and checker function" ] }, { "cell_type": "markdown", - "id": "e16d6ee8", + "id": "ba449065", "metadata": {}, "source": [ - "### Parallel efficiency\n", - "\n", - "The *speedup* represents how faster a parallel algorithm runs with respect to the serial one\n", - "\n", - "$$\\text{speedup} = \\dfrac{\\text{time serial algorithm}}{\\text{time parallel algorithm}}$$\n", - "\n", - "If we run an optimal parallel algorithm with P processes we expect it to run P times faster than the sequential implementation. I.e., the *optimal* speedup of a parallel algorithm on P processes is equal to P,\n", - "\n", - "$$\\text{optimal speedup} = P.$$\n", - "\n", - "However, the *observed* speedup would be lower in practice. The closer the observed speedup is to the optimal one, the more efficient will be the parallel algorithm. To quantify how close (or how far) a parallel algorithm is from an optimal one, the parallel efficiency is defined:\n", - "\n", - "$$\\text{efficiency} = \\dfrac{\\text{speedup}}{\\text{optimal speedup}} = \\dfrac{\\text{speedup}}{P}.$$\n", - "\n", - "\n", - "An optimal parallel algorithm will have efficiency equal to 1. A real parallel algorithm will usually have efficiency less than 1.\n", - "\n" + "In this case, the ratio between communication and computation is O(P/N). If the matrix size N is much larger than the number of workers P, then the communication overhead O(P/N) would be negligible. This opens the door to an scalable implementation." ] }, { "cell_type": "markdown", - "id": "5bdb9e58", + "id": "706cb6ea", "metadata": {}, "source": [ - "### Efficiency of algorithm 1\n", + "## Summary\n", "\n", - "To determine the (theoretical) efficiency of algorithm 1, we need to estimate the time of the serial algorithm and the time of the parallel one. Remember that when we say that an algorithm has complexity O(X), this is equivalent to say that the cost (e.g. the time) of running the algorithm is proportional to X (for X large enough). In other words, the time of the algorithm can be written as C*X for a suitable constant C.\n", + "The table below compares the three parallel algorithms. \n", "\n", - "Using the computational complexities of the sequential and parallel algorithm 1 we can model the run times as follows\n", + "
\n", "\n", - "- The time of the sequential algorithm is Cm*N^3\n", - "- The time of the parallel algorithm in each worker is (Cn + Cw)*N\n", + "| Algorithm | Parallelism
(#workers) | Communication
per worker | Computation
per worker | Ratio communication/
computation |\n", + "|---|---|---|---|---|\n", + "| 1 | N² | 2N + 1 | N | O(1) |\n", + "| 2 | N | 2N + N² | N² | O(1) |\n", + "| 3 | P | N² + 2N²/P | N³/P | O(P/N) |\n", "\n", - "where\n", "\n", - "- Cn is a constant related the network throughput (the smaller Cn the faster the network).\n", - "- Cm and Cw are constants related with the FLOPS in the master and workers respectively (the smaller Cm and Cw the faster the computations)." + "- Matrix-matrix multiplication is trivially parallelizable (all entries in the result matrix can be computed in parallel, at least in theory)\n", + "- However, we cannot exploit all the potential parallelism in a distributed system due to communication overhead\n", + "- We need a sufficiently large grain size to obtain a near optimal speedup\n" ] }, { "cell_type": "markdown", - "id": "961fb287", + "id": "8a1048b3", "metadata": {}, "source": [ - "
\n", - "Question: For which values of Cn, Cm, Cm the parallel algorithm 1 achieves the optimal efficiency? Assume that the time of the parallel algorithm is mainly the time spent in the workers. Since all the workers run in parallel, the time of the parallel algorithm can be approximated as the time in a worker.\n", - "
\n", + "## Exercises" + ] + }, + { + "cell_type": "markdown", + "id": "3e3a0c49", + "metadata": {}, + "source": [ + "### Implementation of algorithm 3\n", "\n", - " a) Cm == Cn + Cw\n", - " b) Cm*N^2 == Cn + Cw\n", - " c) Cm == (Cn + Cw)*N\n", - " d) Cm == (Cn + Cw)*P\n" + "Implement algorithm 3 in the function below. For simplicity, assume that the number of rows of C is a multiple of the number of workers.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "f8527cba", + "id": "c2941c87", "metadata": {}, "outputs": [], "source": [ - "answer = \"x\"\n", - "alg_1_time_check(answer)" - ] - }, - { - "cell_type": "markdown", - "id": "fd92d631", - "metadata": {}, - "source": [ - "## Implementation of algorithm 1" - ] - }, - { - "cell_type": "markdown", - "id": "8452d659", - "metadata": {}, - "source": [ - "The following cells contain an implementation of the parallel algorihtm 1 using Julia's Distribtued module. Take a look and try to understand it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d8a1383", - "metadata": {}, - "outputs": [], - "source": [ - "using Distributed" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5842410", - "metadata": {}, - "outputs": [], - "source": [ - "if procs() == workers()\n", - " addprocs(4)\n", + "# TODO move solution to a separate notebook and remove part of the implementation here\n", + "function matmul_dist_3!(C,A,B)\n", + " m = size(C,1)\n", + " n = size(C,2)\n", + " l = size(A,2)\n", + " @assert size(A,1) == m\n", + " @assert size(B,2) == n\n", + " @assert size(B,1) == l\n", + " @assert mod(m,nworkers()) == 0\n", + " # Implement here\n", + " nrows_w = div(m,nworkers())\n", + " @sync for (i,w) in enumerate(workers())\n", + " rows_w = (1:nrows_w) .+ (i-1)*nrows_w\n", + " Aw = A[rows_w,:]\n", + " ftr = @spawnat w begin\n", + " Cw = similar(Aw,nrows_w,n)\n", + " matmul_seq!(Cw,Aw,B)\n", + " Cw\n", + " end\n", + " @async C[rows_w,:] = fetch(ftr)\n", + " end\n", + " C\n", "end" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "2aec1209", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist_1!(C, A, B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", - " z = zero(eltype(C))\n", - " @assert nworkers() == m*n\n", - " iw = 0 \n", - " @sync for j in 1:l\n", - " for i in 1:m\n", - " Ai = A[i,:]\n", - " Bj = B[:,j]\n", - " iw += 1\n", - " w = workers()[iw]\n", - " ftr = @spawnat w begin\n", - " Cij = z\n", - " for k in 1:n\n", - " Cij += Ai[k]*Bj[k]\n", - " end\n", - " Cij\n", - " end\n", - " @async C[i,j] = fetch(ftr)\n", - " end\n", - " end\n", - " C\n", - " end" - ] - }, { "cell_type": "markdown", - "id": "847ade81", + "id": "9810107f", "metadata": {}, "source": [ - "You can execute the following cells to test this implementation." + "Use test-driven development to implement the algorithm. Use this test:" ] }, { "cell_type": "code", "execution_count": null, - "id": "b920bde0", + "id": "d2df240f", "metadata": {}, "outputs": [], "source": [ "using Test\n", - "N = 2\n", + "P = nworkers()\n", + "load = 100\n", + "N = load*P\n", "A = rand(N,N)\n", "B = rand(N,N)\n", "C = similar(A)\n", - "@test matmul_dist_1!(C,A,B) ≈ A*B" + "@test matmul_dist_3!(C,A,B) ≈ A*B" + ] + }, + { + "cell_type": "markdown", + "id": "968d8237", + "metadata": {}, + "source": [ + "Measure the performance of your implementation by running next cell. Do you get close to the optimal speedup?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0d43911", + "metadata": {}, + "outputs": [], + "source": [ + "P = nworkers()\n", + "load = 100\n", + "N = load*P\n", + "A = rand(N,N)\n", + "B = rand(N,N)\n", + "C = similar(A)\n", + "T1 = @belapsed matmul_seq!(C,A,B)\n", + "C = similar(A)\n", + "TP = @belapsed matmul_dist_3!(C,A,B)\n", + "println(\"Speedup = \", T1/TP)\n", + "println(\"Optimal speedup = \", P)\n", + "println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")" ] }, { @@ -1196,25 +1055,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "023b20d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "matmul_dist_1_v2! (generic function with 1 method)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "function matmul_dist_1_v2!(C, A, B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", + " m = size(C,1)\n", + " n = size(C,2)\n", + " l = size(A,2)\n", + " @assert size(A,1) == m\n", + " @assert size(B,2) == n\n", + " @assert size(B,1) == l\n", " z = zero(eltype(C))\n", - " @sync for j in 1:l\n", + " @sync for j in 1:n\n", " for i in 1:m\n", " Ai = A[i,:]\n", " Bj = B[:,j]\n", - " # Note the :any\n", " ftr = @spawnat :any begin\n", " Cij = z\n", - " for k in 1:n\n", - " Cij += Ai[k]*Bj[k]\n", + " for k in 1:l\n", + " @inbounds Cij += Ai[k]*Bj[k]\n", " end\n", " Cij\n", " end\n", @@ -1222,7 +1094,7 @@ " end\n", " end\n", " C\n", - " end" + "end" ] }, { @@ -1235,13 +1107,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "c1d3595b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "using Test\n", - "N = 40\n", + "N = 50\n", "A = rand(N,N)\n", "B = rand(N,N)\n", "C = similar(A)\n", @@ -1250,1092 +1133,46 @@ }, { "cell_type": "markdown", - "id": "9f6424c8", + "id": "b49ee366", "metadata": {}, "source": [ - "Note that each worker will process on average N^2/P entries instead of a single one as in the original implementation. This invalidates our complexity analysis." - ] - }, - { - "cell_type": "markdown", - "id": "75f30a9c", - "metadata": {}, - "source": [ - "
\n", - "Question: Which is the computational complexity of the work done in a worker for this variation of algorithm 1?\n", - "
\n", - "\n", - " a) O(N/P) communication and O(N^2) computation\n", - " b) O(N^2) communication and O(N/P) computation\n", - " c) O(N^3/P) communication and O(N^3/P) computation\n", - " d) O(N/P) communication and O(N/P) computation" + "Run the next cell to check the performance of this implementation. Note that we are far away from the optimal speed up. Why? To answer this question compute the theoretical communication over computation ratio for this implementation and reason about the obtained result. Hint: the number of times a worker is spawned in this implementation is N^3/P on average." ] }, { "cell_type": "code", - "execution_count": null, - "id": "76a18834", + "execution_count": 18, + "id": "9a4c526c", "metadata": {}, - "outputs": [], - "source": [ - "answer = \"x\"\n", - "alg_1_v2_complex_check(answer)" - ] - }, - { - "cell_type": "markdown", - "id": "8bf21c75", - "metadata": {}, - "source": [ - "
\n", - "Question: For which values of Cn, Cm, Cm the this variation of the parallel algorithm 1 achieves the optimal efficiency?\n", - "
\n", - "\n", - " a) Cm == Cn + Cw\n", - " b) Cm*N^2 == Cn + Cw\n", - " c) Cm == (Cn + Cw)*N\n", - " d) Cm == (Cn + Cw)*P\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6de8d00f", - "metadata": {}, - "outputs": [], - "source": [ - "answer = \"x\"\n", - "alg_1_v2_time_check(answer)" - ] - }, - { - "cell_type": "markdown", - "id": "3e0d32fc", - "metadata": {}, - "source": [ - "## Parallel algorithm 2\n", - "\n", - "Each worker computes a row of `C`. We need `P=N` workers.\n" - ] - }, - { - "attachments": { - "fig_matmul_intro_q_2.png": { - "image/png": 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" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Speedup = 0.0012612337176382187\n", + "Optimal speedup = 4\n", + "Efficiency = 0.03153084294095547%\n" + ] } - }, - "cell_type": "markdown", - "id": "9bc5cb16", - "metadata": {}, + ], "source": [ - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c9c40141", - "metadata": {}, - "source": [ - "### Data dependencies" - ] - }, - { - "cell_type": "markdown", - "id": "b5c89c45", - "metadata": {}, - "source": [ - "### Complexity" - ] - }, - { - "cell_type": "markdown", - "id": "caafe52f", - "metadata": {}, - "source": [ - "### Efficiency" - ] - }, - { - "cell_type": "markdown", - "id": "78c8c975", - "metadata": {}, - "source": [ - "### Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "710edd44", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist_2!(C, A, B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", - " z = zero(eltype(C))\n", - " @assert nworkers() == m\n", - " iw = 0\n", - " @sync for i in 1:m\n", - " Ai = A[i,:]\n", - " iw += 1\n", - " w = workers()[iw]\n", - " ftr = @spawnat w begin\n", - " Ci = fill(z,l)\n", - " for j in 1:n\n", - " for k in 1:n\n", - " Ci[j] += Ai[k]*B[k,j]\n", - " end\n", - " end\n", - " Ci\n", - " end\n", - " @async C[i,:] = fetch(ftr)\n", - " end\n", - " C\n", - " end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2378bba", - "metadata": {}, - "outputs": [], - "source": [ - "using Test\n", - "N = 4\n", + "N = 100\n", "A = rand(N,N)\n", "B = rand(N,N)\n", "C = similar(A)\n", - "@test matmul_dist_2!(C,A,B) ≈ A*B" - ] - }, - { - "cell_type": "markdown", - "id": "30e6cb67", - "metadata": {}, - "source": [ - "## Parallel algorithm 3" - ] - }, - { - "cell_type": "markdown", - "id": "d294e05a", - "metadata": {}, - "source": [ - "Each worker computes N/P consecutive rows of `C`." - ] - }, - { - "attachments": { - "fig_matmul_intro_q_3.png": { - "image/png": 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" - } - }, - "cell_type": "markdown", - "id": "47868612", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0a04b344", - "metadata": {}, - "source": [ - "### Data dependencies" - ] - }, - { - "cell_type": "markdown", - "id": "019bd08a", - "metadata": {}, - "source": [ - "### Complexity" - ] - }, - { - "cell_type": "markdown", - "id": "93c30b29", - "metadata": {}, - "source": [ - "### Efficiency" - ] - }, - { - "cell_type": "markdown", - "id": "4587a17e", - "metadata": {}, - "source": [ - "### Implementation\n", - "\n", - "The implementation of this algorithm is let as an exercise (see below). The following function is a helper to compute which subset of rows will be processed by a given worker." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bbf0055", - "metadata": {}, - "outputs": [], - "source": [ - "function local_rows(i,N)\n", - " p = i\n", - " P = nworkers()\n", - " l = N ÷ P\n", - " offset = l * (p-1)\n", - " rem = N % P\n", - " if rem >= (P-p+1)\n", - " l = l + 1\n", - " offset = offset + p - (P-rem) - 1\n", - " end\n", - " start = 1+offset\n", - " stop = l+offset\n", - " start:stop\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "768c4c8d", - "metadata": {}, - "source": [ - "Run the following cell to understand what function `local_rows` does." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79627008", - "metadata": {}, - "outputs": [], - "source": [ - "N = 12\n", - "for i in 1:nworkers()\n", - " rows = local_rows(i,N)\n", - " @show rows\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "d67bfd59", - "metadata": {}, - "source": [ - "Implement the parallel algorithm 3 in the function below. Test it with the provided test." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efa65ae4", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist_3!(C, A, B)\n", - " ## Implement your code here\n", - " C\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b07001dc", - "metadata": {}, - "outputs": [], - "source": [ - "using Test\n", - "N = 4\n", - "A = rand(N,N)\n", - "B = rand(N,N)\n", + "P = nworkers()\n", + "T1 = @belapsed matmul_seq!(C,A,B)\n", "C = similar(A)\n", - "@test matmul_dist_3!(C,A,B) ≈ A*B" - ] - }, - { - "cell_type": "markdown", - "id": "5e6a89dd", - "metadata": {}, - "source": [ - "## Efficiency\n", - "\n", - "The usual bottleneck in distributed computations is the overhead associated with the communication between processes.\n", - "\n", - "As we can see, the distributed function is much slower than the handwritten serial version. The additional runtime is due to the communication between the master process and the worker processes. \n", - "But how can we analyze the efficiency of this algorithm in general? In order to determine the efficiency, we have to compare how much computation time we can save to how much communication overhead there is in addition to the serial program. \n", - "\n", - "### Exercise 4\n", - "Determine the complexity of the serial algorithm and compare it with the complexity of the coordinator's work in the parallel algorithm. How much compute time does the coordinator save by offloading work to nodes? " + "TP = @belapsed matmul_dist_1_v2!(C,A,B)\n", + "println(\"Speedup = \", T1/TP)\n", + "println(\"Optimal speedup = \", P)\n", + "println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "c800dd33", - "metadata": {}, - "outputs": [], - "source": [ - "a = \"O(√N)\"\n", - "b = \"O(N)\"\n", - "c = \"O(N²)\"\n", - "d = \"O(N³)\"\n", - "\n", - "compute_time_serial = #TODO\n", - "compute_time_coordinator = #TODO\n", - "compute_time_saved = #TODO\n", - "\n", - "@test (compute_time_serial, compute_time_coordinator, compute_time_saved) == solution_mm_par_04()" - ] - }, - { - "cell_type": "markdown", - "id": "a98a10b9", - "metadata": {}, - "source": [ - "The complexity of the sequential algorithm is $O(N^3)$. The coordinator loops over the number of workers (= the number of elements in $C$, i.e. $N^2$). The computation of the dot-product is outsourced to the workers. So the complexity of the coordinator's work is $O(N^2)$. Thus, the coordinator saves $\\frac{O(N^2)}{O(N^3)} = O(N)$ compute time." - ] - }, - { - "cell_type": "markdown", - "id": "1ca84016", - "metadata": {}, - "source": [ - "### Exercise 5\n", - "How much communication overhead is there between the coordinator and the workers? In order to determine this, evaluate the size of the data that is passed between coordinator and workers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50543fef", - "metadata": {}, - "outputs": [], - "source": [ - "a = \"O(log(N))\"\n", - "b = \"O(√N)\"\n", - "c = \"O(N)\"\n", - "d = \"O(N²)\"\n", - "\n", - "comm_overhead = #TODO\n", - "@test comm_overhead == solution_mm_par_05()" - ] - }, - { - "cell_type": "markdown", - "id": "432fbe4f", - "metadata": {}, - "source": [ - "The coordinator sends 2 arrays of length `n` to each worker. After the worker is done computing, it sends 1 float back to the coordinator. So the complexity of the communication overhead is $O(2N +1) = O(N)$.\n", - "```julia\n", - "@async C[i,j] = @fetchfrom w dot(Ai, Bj) # <------- Send 2 Arrays of length n to workers, Receive 1 number\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "9ee43181", - "metadata": {}, - "source": [ - "In conclusion, we found out that the computation/communication ratio is $O(N)/O(N) = O(1)$, so algorithm 1 provides no gain in efficiency. \n", - "\n", - "\n", - "# Parallel Algorithm 2\n", - "Another idea to parallelize the matrix multiplication is to let each processor compute one _row_ ($N$ elements) of $C$. This approach requires only $N$ workers. \n", - "\n", - "## Data Dependencies" - ] - }, - { - "cell_type": "markdown", - "id": "dc2d1264", - "metadata": {}, - "source": [ - "Each worker requires the entire $B$ matrix and one row of $A$ as an input. Each processor can re-use the row of A $N$-times to compute the $N$-elements in its row. \n", - "![Algorithm2](images/MM_par_algorithm_2.png)\n" - ] - }, - { - "cell_type": "markdown", - "id": "4415d376", - "metadata": {}, - "source": [ - "The image below depicts the structure of Parallel Algorithm 2. \n", - "The coordinator sends the $i$-th row of $A$ and the entire matrix B to worker node $i$. The worker nodes compute the whole row and send the result back to the coordinator.\n", - "\n", - "![Structure of algorithm 2](images/MM_par_2_structure.png)\n" - ] - }, - { - "cell_type": "markdown", - "id": "3ace3b41", - "metadata": {}, - "source": [ - "Let's first remove all unnecessary workers. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8547d6c1", - "metadata": {}, - "outputs": [], - "source": [ - "rmprocs(6:26)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13a3ab88", - "metadata": {}, - "outputs": [], - "source": [ - "workers()" - ] - }, - { - "cell_type": "markdown", - "id": "8b3ccbe4", - "metadata": {}, - "source": [ - "### Exercise 6\n", - "Provide the code for Parallel Algorithm 2. Test your algorithm using Julia's `@test` macro. You can view the solution to this exercise at the bottom of the notebook. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "68d34ae0", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist2!(C, A, B)\n", - " n = size(A,1)\n", - " @assert size(A,2) == n\n", - " @assert size(B,1) == n\n", - " @assert size(B,2) == n\n", - " @assert size(C,1) == n\n", - " @assert size(C,2) == n\n", - " @assert nworkers() == n\n", - " \n", - " # TODO: finish the code for algorithm 2\n", - " \n", - " C\n", - "end \n", - "\n", - "# TODO: test your code" - ] - }, - { - "cell_type": "markdown", - "id": "2e9e7902", - "metadata": {}, - "source": [ - "## Efficiency\n", - "\n", - "### Exercise 7\n", - "How efficient is Parallel Algorithm 2? Answer the following questions to determine the computation/communication ratio: \n", - "1. How much compute time does the coordinator save by offloading work to nodes?\n", - "2. How much communication overhead is there between the coordinator and the workers?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e0ee39a7", - "metadata": {}, - "outputs": [], - "source": [ - "a = \"O(1)\"\n", - "b = \"O(√N)\"\n", - "c = \"O(N)\"\n", - "d = \"O(N²)\"\n", - "e = \"O(N³)\"\n", - "\n", - "compute_time_saved = #TODO\n", - "comm_overhead = #TODO\n", - "comp_comm_ratio = #TODO\n", - "\n", - "@test (compute_time_saved, comm_overhead, comp_comm_ratio) == solution_mm_par_07()" - ] - }, - { - "cell_type": "markdown", - "id": "57092027", - "metadata": {}, - "source": [ - " 1. The coordinator saves $O(N^2)$ computations. \n", - " 2. The coordinator sends $N + N^2$ floats to each worker and receives $N$ floats from each worker. Thus, the communication overhead is $O(2N + N^2) = O(N^2)$. \n", - " \n", - "This results in a computation/communication ratio of $O(N^2)/O(N^2) = O(1)$. \n", - "\n", - "```julia\n", - "\n", - "@async C[i,:] = @fetchfrom w Ai*B # <----- Worker does N² computations/ Send N + N² floats, receive N floats \n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "d02d9321", - "metadata": {}, - "source": [ - "To conclude, our second algorithm is still inefficient. How can we design an algorithm that does more computation than communication? \n", - "\n", - "# Parallel Algorithm 3\n", - "\n", - "Think of how to design an efficient algorithm to solve matrix multiplication in parallel. Hint: Assume that we are dealing with large problem sizes, thus $N >> P$ (where $P$ is the number of processors). " - ] - }, - { - "cell_type": "markdown", - "id": "def4265c", - "metadata": {}, - "source": [ - "If $N >> P$, we can assign _many rows_ to one processor. Each processor computes $N/P$ rows of $C$. " - ] - }, - { - "cell_type": "markdown", - "id": "a6d4bab7", - "metadata": {}, - "source": [ - "## Data Dependencies" - ] - }, - { - "cell_type": "markdown", - "id": "7fd516bb", - "metadata": {}, - "source": [ - "Each processor needs the entire $B$ matrix and $N/P$ rows of $A$. \n", - "\n", - "![Alg3DataDependencies](images/MM_par_algorithm_3.png)" - ] - }, - { - "cell_type": "markdown", - "id": "3b67c60a", - "metadata": {}, - "source": [ - "Now let's start coding Algorithm 3. We'll use $P = 2$, so let's remove all other workers. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f6d154e", - "metadata": {}, - "outputs": [], - "source": [ - "rmprocs(4:5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7511e301", - "metadata": {}, - "outputs": [], - "source": [ - "workers()" - ] - }, - { - "cell_type": "markdown", - "id": "bfc5dddc", - "metadata": {}, - "source": [ - "### Exercise 8\n", - "Write a function that calculates the row indices for each partition of matrix $A$. The function should store the result in the input variable `indices`, which is of the type `Array{Int64}[]` (a list of arrays). You can add arrays to this variable by calling `push!(indices, my_array)`. The $i$-th entry of `indices` should be an array of row indices of matrix $A$ for the $i$-th worker. The function also receives the number of rows `n` and the number of processors `p` as input parameters. You can assume that `p` divides `n` without remainder, thus $n \\pmod p = 0$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4375aa9f", - "metadata": {}, - "outputs": [], - "source": [ - "function calculate_partition!(indices, n, p)\n", - " @assert mod(n,p) == 0\n", - " # TODO: Calculate the row indices of matrix A for each worker. \n", - " # Store arrays of row indices in the variable indices.\n", - " \n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57b766cb", - "metadata": {}, - "outputs": [], - "source": [ - "indices = Array{Int64}[]\n", - "n = 8\n", - "p = 2\n", - "calculate_partition!(indices, n, p)\n", - "@test indices == [[1,2,3,4], [5,6,7,8]]" - ] - }, - { - "cell_type": "markdown", - "id": "7a0ebd30", - "metadata": {}, - "source": [ - "### Exercise 9\n", - "Provide the code for Parallel Algorithm 3. Test your implementation using Julia macro `@test`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d7c0193", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist3!(C, A, B)\n", - " n = size(A,1)\n", - " p = nworkers()\n", - " @assert size(A,2) == n\n", - " @assert size(B,1) == n\n", - " @assert size(B,2) == n\n", - " @assert size(C,1) == n\n", - " @assert size(C,2) == n\n", - " @assert mod(n, p) == 0\n", - " indices = Array{Int64}[]\n", - " calculate_partition!(indices, n, p)\n", - " @sync for (i, w) in enumerate(workers())\n", - " # TODO: do matrix multiplication in parallel\n", - " end\n", - " C\n", - "end\n", - "\n", - "# TODO: test your solution" - ] - }, - { - "cell_type": "markdown", - "id": "cfc6bc65", - "metadata": {}, - "source": [ - "## Efficiency again\n", - "Let's have a look at the efficiency of Parallel Algorithm 3. \n", - "\n", - "### Exercise 10\n", - "How efficient is this parallel algorithm? Determine the computation/communication ratio like in Exercise 7. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a1efe1f", - "metadata": {}, - "outputs": [], - "source": [ - "a = \"O(1)\"\n", - "b = \"O(N/P)\"\n", - "c = \"O(N)\"\n", - "d = \"O(N²/P)\"\n", - "e = \"O(N²)\"\n", - "f = \"O(N³/P)\"\n", - "g = \"O(N³)\"\n", - "\n", - "compute_time_saved = #TODO\n", - "comm_overhead = #TODO\n", - "comp_comm_ratio = #TODO\n", - "\n", - "@test (compute_time_saved, comm_overhead, comp_comm_ratio) == solution_mm_par_10()" - ] - }, - { - "cell_type": "markdown", - "id": "f548576d", - "metadata": {}, - "source": [ - "- The workers now do $N/P \\times N \\times N$ computations, thus the complexity is $O(N³/P)$. \n", - "\n", - "- The coordinator sends the whole $B$ matrix ($N²$) and a part of the $A$ matrix ($N²/P$) and receives part of the $C$ matrix ($N²/P$). Hence, the complexity of the communication overhead is $O(N²/P)$. \n", - "- Finally, we obtain a computation/communication ratio of $O(N³/P) / O(N²/P) = O(N/P)$. \n", - "\n", - "\n", - "```julia\n", - "\n", - "@async C[rows_w,:] = @fetchfrom w Aw*B # <----- Send N² + N²/P floats, get N²/P floats. Worker does N/P * N * N multiplications/additions. \n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "5ca61b7f", - "metadata": {}, - "source": [ - "The table below compares the three parallel algorithms. \n", - "\n", - "
\n", - "\n", - "| Algorithm | Parallelism
(#jobs) | Communication
per job | Computation
per job | Ratio computation/
communication |\n", - "|---|---|---|---|---|\n", - "| 1 | N² | 2N + 1 | N | O(1) |\n", - "| 2 | N | N + N² | N² | O(1) |\n", - "| 3 | P | N²/P + N² + N²/P | N³/P | O(N/P) |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2eaaa1da", - "metadata": { - "tags": [ - "hide_cell" - ] - }, - "outputs": [], - "source": [ - "HTML(\"\"\"\n", - "\"\"\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "e5f0c0cb", - "metadata": {}, - "source": [ - "To conclude, only algorithm 3 provides an increase in efficiency, especially for large problem sizes. If $N >> P$, algorithm 3 will have a low communication overhead. \n", - "\n", - "## Scalability \n", - "The matrices we have looked at in the previous examples were of a very small size. In the previous paragraph, we have established that the efficiency for Algorithm 3 is better especially for large matrices. Let's examine this by running the algorithm for different matrix sizes. First we will construct three matrices of different sizes. Next, we run the parallel algorithm for different numbers of processors. We evaluate the performance against the handwritten sequential version `matmul_hand!()` presented at the top of the notebook. We measure the performance by calculating the _speedup_, which is defined as \n", - "\n", - "$$\n", - "S_p = \\frac{T_1}{T_p},\n", - "$$\n", - "\n", - "where $T_1$ denotes the runtimes of the sequential algorithm on one node, $T_p$ denotes the runtime of the parallel algorithm on $p$ nodes. The ideal speedup is $S_p = p$. We will look more into speedups and how to measure efficiency in a later section of this course. NB: the following cells take about 6 - 10 minutes of compute time. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c2b1423", - "metadata": {}, - "outputs": [], - "source": [ - "matrix_sizes = [84, 420, 2100]\n", - "\n", - "function initialize_matrices!(array_A, array_B, array_C)\n", - " for n in matrix_sizes\n", - " A = rand(n, n)\n", - " B = rand(n, n)\n", - " C = rand(n, n)\n", - " push!(array_A, A)\n", - " push!(array_B, B)\n", - " push!(array_C, C)\n", - " end\n", - "end\n", - "\n", - "matrices_A = Matrix{Float64}[]\n", - "matrices_B = Matrix{Float64}[]\n", - "matrices_C = Matrix{Float64}[]\n", - "\n", - "initialize_matrices!(matrices_A, matrices_B, matrices_C)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b3feb47", - "metadata": {}, - "outputs": [], - "source": [ - "n_workers = [0, 1, 2, 4, 7]\n", - "rmprocs(workers())\n", - "runtimes = zeros(length(n_workers), length(matrix_sizes));\n", - "\n", - " \n", - "for (i,p) in enumerate(n_workers)\n", - " # Add sufficient worker processes\n", - " if p >= 1\n", - " addprocs(p)\n", - " end\n", - " for (j,n) in enumerate(matrix_sizes)\n", - " @show p, n\n", - " C = matrices_C[j]\n", - " A = matrices_A[j]\n", - " B = matrices_B[j]\n", - " if nprocs() == 1\n", - " # Run sequential algorithm if 0 workers\n", - " runtimes[i,j] = @belapsed matmul_hand!(C, A, B)\n", - " else\n", - " runtimes[i,j] = @belapsed matmul_dist3!(C, A, B)\n", - " end\n", - " @show runtimes[i,j]\n", - " end\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "153ca8a0", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot runtimes \n", - "using Plots\n", - "\n", - "function calculate_speedups!(speedup, runtimes, reference)\n", - " @assert length(reference) == size(runtimes,2)\n", - " @assert size(speedup,1) == size(runtimes,1)\n", - " @assert size(speedup,2) == size(runtimes,2)\n", - " for j in 1:size(runtimes,2)\n", - " for i in 1:size(runtimes,1)\n", - " Sₚ = reference[j] / runtimes[i,j] \n", - " speedup[i,j] = Sₚ\n", - " end\n", - " end\n", - "end\n", - "\n", - "speedups = zeros(length(n_workers)-1, length(matrix_sizes))\n", - "reference = runtimes[1,:]\n", - "calculate_speedups!(speedups, runtimes[2:end, :], reference)\n", - "\n", - "plot(n_workers[2:end], \n", - " [speedups[:,1], speedups[:,2], speedups[:,3]], \n", - " label=[\"N=$(matrix_sizes[1])\" \"N=$(matrix_sizes[2])\" \"N=$(matrix_sizes[3])\"],\n", - " xlabel=\"# workers\", ylabel=\"speedup\",\n", - " title=\"Speedups compared with sequential version matmul_hand!\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "39cd37f0", - "metadata": {}, - "source": [ - "As we can see, the speedups are higher for larger matrix sizes. We can also observe that the speedups decrease when using more processors. The speedups are superlinear, that is, much larger than the theoretical ideal speedup. The reason for this is that we compare to a very slow handwritten solution whilst using the optimized Julia built-in function ` * ` on the workers in the parallel computation. If we were to compare the performance with Julia's `A * B`, we would see that the parallel algorithm performs worse even for large matrix sizes, since this function is highly optimized. We will get more into the efficiency analysis of parallel algorithms later in the course.\n", - "\n", - "# Discussion\n", - "The first problem we have looked at, Matrix Multiplication, can be parallelized by sending parts of the input matrices to each worker and collecting the result at the end. This problem is **trivial to parallelize**, since the workers can do their computations independently from one another. In the following sections, we will look at other algorithms which require intermediate communication. \n", - "\n", - "Another key insight is that we can attain a better performance by chosing a **large grain size**, thus by dividing the matrix $A$ into larger chunks of data.\n", - "\n", - "Finally, the time saved by doing more computation than communication is increased in **large problem sizes**. \n" - ] - }, - { - "cell_type": "markdown", - "id": "0de5e8ac", - "metadata": {}, - "source": [ - "# Solution to Exercises\n", - "### Solution to Exercise 3\n", - "\n", - "The following cell shows the code for the first parallel algorithm with the correct macros." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e60911e", - "metadata": {}, - "outputs": [], - "source": [ - "@everywhere using LinearAlgebra \n", - "\n", - "function matmul_dist1!(C, A, B)\n", - " n = size(A,1)\n", - " @assert size(A,2) == n\n", - " @assert size(B,1) == n\n", - " @assert size(B,2) == n\n", - " @assert size(C,1) == n\n", - " @assert size(C,2) == n\n", - " @assert nworkers() == n^2 \n", - " # Let each worker compute one element \n", - " @sync for w in workers()\n", - " # Compute row and column index from worker id\n", - " i, j = index_from_wid(w)\n", - " Ai = A[i,:]\n", - " Bj = B[:,j]\n", - " # Do element computation in parallel\n", - " @async C[i,j] = @fetchfrom w dot(Ai, Bj)\n", - " end\n", - " C\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "4e5f3056", - "metadata": {}, - "source": [ - "### Solution to Exercise 6\n", - "The cell below contains the code for parallel algorithm 2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "72c86b2e", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist2!(C, A, B)\n", - " n = size(A,1)\n", - " @assert size(A,2) == n\n", - " @assert size(B,1) == n\n", - " @assert size(B,2) == n\n", - " @assert size(C,1) == n\n", - " @assert size(C,2) == n\n", - " @assert nworkers() == n\n", - " # Let each worker compute one row \n", - " @sync for w in workers()\n", - " # Compute row index from worker id \n", - " i = w - 1\n", - " # Make sure Ai is an array, not a vector\n", - " Ai = A[[i],:]\n", - " # Do row computation in parallel\n", - " @async C[i,:] = @fetchfrom w Ai*B\n", - " end\n", - " C\n", - "end\n", - "\n", - "# Test solution\n", - "C1 = matmul_dist2!(C, A, B)\n", - "@test C1 ≈ A * B" - ] - }, - { - "cell_type": "markdown", - "id": "21fa8bbc", - "metadata": {}, - "source": [ - "### Solution to Exercise 8" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ed7e697", - "metadata": {}, - "outputs": [], - "source": [ - "function calculate_partition!(indices, n, p)\n", - " @assert mod(n,p) == 0\n", - " nrows = div(n,p)\n", - " for i in 1:p\n", - " range =((i-1) * nrows + 1) : (i*nrows)\n", - " push!(indices, range)\n", - " end\n", - "end\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "666bd69c", - "metadata": {}, - "source": [ - "### Solution to Exercise 9\n", - "The following cell contains the code for parallel algorithm 3." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b40d341c", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist3!(C, A, B)\n", - " n = size(A,1)\n", - " p = nworkers()\n", - " @assert size(A,2) == n\n", - " @assert size(B,1) == n\n", - " @assert size(B,2) == n\n", - " @assert size(C,1) == n\n", - " @assert size(C,2) == n\n", - " @assert mod(n, p) == 0\n", - " indices = Array{Int64}[]\n", - " calculate_partition!(indices, n, p)\n", - " @sync for (i, w) in enumerate(workers())\n", - " # Get row indices of this partition\n", - " rows_w = indices[i]\n", - " Aw = A[rows_w,:]\n", - " # Do partition computation in parallel\n", - " @async C[rows_w,:] = @fetchfrom w Aw*B\n", - " end\n", - " C\n", - "end\n", - "\n", - "# Test solution\n", - "C1 = matmul_dist3!(C, A, B)\n", - "@test C1 ≈ A * B" - ] - }, - { - "cell_type": "markdown", - "id": "066b5483", - "metadata": {}, - "source": [ - "## Solutions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "161421d7", - "metadata": {}, - "outputs": [], - "source": [ - "function matmul_dist_1!(C, A, B)\n", - " m = size(A,1)\n", - " n = size(A,2)\n", - " l = size(B,2)\n", - " @assert size(B,1) == n\n", - " @assert size(C,1) == m\n", - " @assert size(C,2) == l\n", - " @sync for j in 1:l # Note the @sync!\n", - " for i in 1:m\n", - " Ai = A[i,:]\n", - " Bj = B[:,j]\n", - " # Compute this entry in any of the available workers \n", - " @async C[i,j] = fetch(@spawnat :any dot(Ai, Bj))\n", - " end\n", - " end\n", - " C\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1248015", + "id": "4f2d0d9b", "metadata": {}, "outputs": [], "source": []