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Gelieza K
2023-08-14 15:34:38 +02:00
parent b55aef53a7
commit d2bad0df61
47 changed files with 220987 additions and 1 deletions
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20
notebooks/jacobi_method.ipynb
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@@ -938,6 +938,26 @@
"source": [
"# TODO"
]
},
{
"cell_type": "markdown",
"id": "6d3430ad",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d72ff47",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

20
notebooks/julia_async.ipynb
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@@ -754,6 +754,26 @@
" sleep(i)\n",
"end"
]
},
{
"cell_type": "markdown",
"id": "a5d3730b",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9863011",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

20
notebooks/julia_basics.ipynb
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@@ -1585,6 +1585,26 @@
"source": [
"# Implement here"
]
},
{
"cell_type": "markdown",
"id": "357e0490",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8d92f25",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

20
notebooks/julia_distributed.ipynb
View File

@@ -1295,6 +1295,26 @@
"\n",
"We have seen the basics of distributed computing in Julia. The programming model is essentially an extension of tasks and channels to parallel computations on multiple machines. The low-level functions are `remotecall` and `RemoteChannel`, but there are other functions and macros like `pmap` and `@distributed` that simplify the implementation of parallel algorithms."
]
},
{
"cell_type": "markdown",
"id": "9a49ad48",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e36ae43",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

18
notebooks/julia_tutorial.ipynb
View File

@@ -485,6 +485,24 @@
"\n",
"If you want to interact with the Julia community on discourse, sign in at https://discourse.julialang.org/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

20
notebooks/matrix_matrix.ipynb
View File

@@ -1108,6 +1108,26 @@
"println(\"Optimal speedup = \", P)\n",
"println(\"Efficiency = \", 100*(T1/TP)/P, \"%\")"
]
},
{
"cell_type": "markdown",
"id": "8e171362",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "86b7b044",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

14
notebooks/mpi_tutorial.ipynb
View File

@@ -374,10 +374,22 @@
"In this example, the root processor generates random data and then scatters it to all processes using MPI.Scatter. Each process calculates the average of its local data, and then the local averages are gathered using MPI.Gather. The root processor computes the global average of all sub-averages and prints it."
]
},
{
"cell_type": "markdown",
"id": "5e8f6e6a",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fcf34823",
"id": "c9364808",
"metadata": {},
"outputs": [],
"source": []

20
notebooks/solutions.ipynb
View File

@@ -186,6 +186,26 @@
" jacobi_mpi(n,niters)\n",
"end"
]
},
{
"cell_type": "markdown",
"id": "47d88e7a",
"metadata": {},
"source": [
"# License\n",
"\n",
"TODO: replace link to website\n",
"\n",
"This notebook is part of the course [Programming Large Scale Parallel Systems](http://localhost:8000/) at Vrije Universiteit Amsterdam and may be used under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "968304a6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {