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https://github.com/fverdugo/XM_40017.git
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Add exercise solution for measuring TSP search overhead
This commit is contained in:
Gelieza K
parent
7899998863
commit
5c03b6cf33
@ -291,6 +291,194 @@
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"rmprocs(workers())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "19641daf",
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"metadata": {},
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"source": [
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"## TSP Exercise: Measure search overhead"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f00557a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"## TSP serial \n",
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"function tsp_serial(connections,city)\n",
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" num_cities = length(connections)\n",
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" path=zeros(Int,num_cities)\n",
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" hops = 1\n",
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" path[hops] = city\n",
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" min_path = zeros(Int, num_cities)\n",
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" current_distance = 0\n",
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" min_distance = typemax(Int)\n",
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" # Collect search time \n",
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" search_time = @elapsed min_path, min_distance = tsp_serial_impl(connections,hops,path,current_distance, min_path, min_distance)\n",
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" (;path=min_path,distance=min_distance, search_time)\n",
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"end"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "30784da2",
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"metadata": {},
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"outputs": [],
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"source": [
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"## TSP distributed\n",
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"@everywhere function tsp_dist_impl(wait_time, connections,hops,path,current_distance,min_dist_chnl, max_hops,jobs_chnl,ftr_result)\n",
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" num_cities = length(connections)\n",
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" if hops == num_cities\n",
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" min_distance = fetch(min_dist_chnl)\n",
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" if current_distance < min_distance\n",
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" take!(min_dist_chnl)\n",
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" # Collect wait time to substract from overall search time \n",
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" if ftr_result !== nothing\n",
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" wait_time += @elapsed @spawnat 1 begin\n",
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" result = fetch(ftr_result)\n",
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" result.path .= path\n",
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" result.min_distance_ref[] = current_distance\n",
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" end |> wait\n",
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" end\n",
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" put!(min_dist_chnl, current_distance)\n",
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" end\n",
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" elseif hops <= max_hops\n",
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" current_city = path[hops]\n",
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" next_hops = hops + 1\n",
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" for (next_city,distance_increment) in connections[current_city]\n",
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" if !visited(next_city,hops,path)\n",
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" path[next_hops] = next_city\n",
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" next_distance = current_distance + distance_increment\n",
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" # Collect wait time because fetch may block\n",
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" wait_time += @elapsed min_distance = fetch(min_dist_chnl)\n",
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" if next_distance < min_distance\n",
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" tsp_dist_impl(wait_time, connections,next_hops,path,next_distance,min_dist_chnl,max_hops,jobs_chnl,ftr_result)\n",
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" end\n",
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" end\n",
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" end \n",
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" else\n",
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" # Collect communication time and add to wait time\n",
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" wait_time += @elapsed if jobs_chnl !== nothing \n",
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" path_copy = copy(path) \n",
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" put!(jobs_chnl,(;hops,path=path_copy,current_distance))\n",
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" end\n",
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" end\n",
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" # Return wait time\n",
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" wait_time\n",
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"end\n",
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"\n",
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"function tsp_dist(connections,city)\n",
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" max_hops = 2\n",
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" num_cities = length(connections)\n",
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" path=zeros(Int,num_cities)\n",
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" result_path=zeros(Int, num_cities)\n",
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" wait_time = 0\n",
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" search_time = 0\n",
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" hops = 1\n",
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" path[hops] = city\n",
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" current_distance = 0\n",
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" min_distance = typemax(Int)\n",
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" jobs_chnl = RemoteChannel(()->Channel{Any}(10))\n",
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" min_dist_chnl = RemoteChannel(()->Channel{Int}(1))\n",
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" put!(min_dist_chnl, min_distance)\n",
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" ftr_result = @spawnat 1 (;path=result_path,min_distance_ref=Ref(min_distance))\n",
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" @async begin\n",
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" # Collect search time from master process\n",
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" search_time += @elapsed wait_time += tsp_dist_impl(wait_time,connections,hops,path,current_distance,min_dist_chnl,max_hops,jobs_chnl,nothing)\n",
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" for w in workers()\n",
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" put!(jobs_chnl,nothing)\n",
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" end\n",
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" end\n",
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" @sync for w in workers()\n",
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" @spawnat w begin\n",
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" path = zeros(Int, num_cities)\n",
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" max_hops = typemax(Int)\n",
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" while true\n",
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" job = take!(jobs_chnl)\n",
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" if job == nothing\n",
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" break\n",
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" end\n",
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" hops = job.hops\n",
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" path = job.path \n",
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" current_distance = job.current_distance\n",
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" min_distance = fetch(min_dist_chnl)\n",
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" if current_distance < min_distance\n",
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" # Collect search time from worker processes \n",
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" search_time += @elapsed wait_time += tsp_dist_impl(wait_time,connections,hops,path,current_distance,min_dist_chnl,max_hops,nothing,ftr_result)\n",
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" end\n",
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" end\n",
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" end\n",
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" end \n",
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" result = fetch(ftr_result)\n",
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" (;path = result.path, distance = result.min_distance_ref[], search_time, wait_time)\n",
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"end\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "694de934",
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"metadata": {},
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"outputs": [],
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"source": [
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"using Distributed\n",
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"using RandomMatrix\n",
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"using Plots\n",
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"\n",
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"function generate_rand_connections(city_range, distance_range)\n",
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" # generate random connections matrix \n",
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" n_cities = rand(city_range)\n",
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" matrix = randTriangular(distance_range, n_cities; Diag=false)\n",
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"\n",
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" connections = Array{Array{Tuple{Int64,Int64},1},1}(undef, n_cities)\n",
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" for i in 1:n_cities\n",
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" connections[i] = Array{Tuple{Int64,Int64},1}(undef, n_cities)\n",
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" end\n",
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" for i in 1:n_cities\n",
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" for j in i:n_cities\n",
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" distance = matrix[i,j]\n",
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" connections[i][j] = (j,distance)\n",
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" connections[j][i] = (i,distance)\n",
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" end\n",
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" end\n",
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" return connections\n",
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"end\n",
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"\n",
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"# Run once so compile times are not measured\n",
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"distance_range = 1:100\n",
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"connections = generate_rand_connections(4:4, distance_range)\n",
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"tsp_dist(connections,1)\n",
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"tsp_serial(connections,1)\n",
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"\n",
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"# Measure runtimes of serial and parallel algorithm\n",
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"n_it = 5\n",
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"city_ranges = [4:4, 6:6, 8:8, 10:10]\n",
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"search_overhead = zeros(Float64, length(city_ranges), n_it )\n",
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"for (i, n) in enumerate(city_ranges)\n",
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" for k in 1:n_it\n",
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" connections = generate_rand_connections(n, distance_range)\n",
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" @show n, k\n",
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" path_dist, distance_dist, search_time_dist, wait_time_dist = tsp_dist(connections,1)\n",
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" path_serial, distance_serial, search_time_serial = tsp_serial(connections,1)\n",
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" # Compute search overhead as difference between distributed program and serial program\n",
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" # (without time spent communicating or waiting)\n",
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" search_overhead[i, k] = search_time_dist - wait_time_dist - search_time_serial\n",
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" end\n",
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"end\n",
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"\n",
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"min_search_oh = minimum(search_overhead, dims=2)\n",
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"city_sizes = [4,6,8,10]\n",
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"plot(city_sizes, min_search_oh, yaxis=:log, seriestype=:scatter,legend=false)\n",
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"plot!(city_sizes, min_search_oh, yaxis=:log, legend=false)\n",
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"\n",
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"xlabel!(\"Number of cities\")\n",
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"ylabel!(\"Search overhead (s)\")\n",
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"title!(\"Minimum search overhead for different problem sizes\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "47d88e7a",
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@ -314,7 +502,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Julia 1.9.0",
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"display_name": "Julia 1.9.1",
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"language": "julia",
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"name": "julia-1.9"
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},
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@ -322,7 +510,7 @@
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "1.9.0"
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"version": "1.9.1"
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}
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},
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"nbformat": 4,
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@ -1,117 +0,0 @@
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using Distributed
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if procs() == workers()
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addprocs(4)
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end
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@everywhere function visited(city,hops,path)
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for i = 1:hops
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if path[i] == city
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return true
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end
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end
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return false
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end
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# solution [1, 4, 5, 2, 3, 6], distance = 222
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connections = [
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[(1,0),(4,39),(5,76), (6,78),(3,94),(2,97)],
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[(2,0),(5,25),(4,58),(3,62),(1,97),(6,109)],
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[(3,0),(6,58),(2,62),(4,68),(5,70),(1,94)],
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[(4,0),(5,38),(1,39),(2,58),(3,68),(6,78)],
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[(5,0),(2,25),(4,38),(3,70),(1,76),(6,104)],
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[(6,0),(3,58),(1,78),(4,78),(5,104),(2,109)]
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]
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# Shortest route with start 1: 1-3-2-4 (distance: 7)
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con2 = [
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[(1,0), (2,2), (3,3), (4,4)],
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[(2,0), (4,1), (1,2), (3,3)],
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[(3,0), (1,3), (2,3), (4,10)],
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[(4,0), (2,1), (1,4), (3,10)]
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]
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## TSP distributed
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@everywhere function tsp_dist_impl(connections,hops,path,current_distance,min_dist_chnl, max_hops,jobs_chnl,ftr_result)
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num_cities = length(connections)
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if hops == num_cities
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min_distance = fetch(min_dist_chnl)
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if current_distance < min_distance
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take!(min_dist_chnl)
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# Wait until results are written to future
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if ftr_result !== nothing
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@spawnat 1 begin
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result = fetch(ftr_result)
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result.path .= path
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result.min_distance_ref[] = current_distance
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end |> wait
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end
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# Unblock waiting processes
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put!(min_dist_chnl, current_distance)
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end
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elseif hops <= max_hops
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current_city = path[hops]
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next_hops = hops + 1
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for (next_city,distance_increment) in connections[current_city]
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if !visited(next_city,hops,path)
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path[next_hops] = next_city
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next_distance = current_distance + distance_increment
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min_distance = fetch(min_dist_chnl)
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if next_distance < min_distance
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tsp_dist_impl(connections,next_hops,path,next_distance,min_dist_chnl,max_hops,jobs_chnl,ftr_result)
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end
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end
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end
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else
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if jobs_chnl !== nothing
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# Allocate new memory so paths are not overwritten in queue
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path_copy = copy(path)
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put!(jobs_chnl,(;hops,path=path_copy,current_distance))
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end
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end
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end
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function tsp_dist(connections,city)
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max_hops = 2
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num_cities = length(connections)
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path=zeros(Int,num_cities)
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result_path=zeros(Int, num_cities)
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hops = 1
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path[hops] = city
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current_distance = 0
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min_distance = typemax(Int)
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jobs_chnl = RemoteChannel(()->Channel{Any}(10))
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# Initialize min distance channel with Intmax
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min_dist_chnl = RemoteChannel(()->Channel{Int}(1))
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put!(min_dist_chnl, min_distance)
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# Future to store overall result
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ftr_result = @spawnat 1 (;path=result_path,min_distance_ref=Ref(min_distance))
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@async begin
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tsp_dist_impl(connections,hops,path,current_distance,min_dist_chnl,max_hops,jobs_chnl,nothing)
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for w in workers()
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put!(jobs_chnl,nothing)
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end
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end
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@sync for w in workers()
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@spawnat w begin
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path = zeros(Int, num_cities)
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max_hops = typemax(Int)
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jobs_channel = nothing
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while true
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job = take!(jobs_chnl)
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if job == nothing
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break
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end
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hops = job.hops
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path = job.path
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current_distance = job.current_distance
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tsp_dist_impl(connections,hops,path,current_distance,min_dist_chnl,max_hops,jobs_channel,ftr_result)
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end
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end
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end
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result = fetch(ftr_result)
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(;path = result.path, distance = result.min_distance_ref[])
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end
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city = 1
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tsp_dist(con2,city)
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