{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"../images/ipython_logo.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Back to the main [Index](../Index.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parallel Computing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"IPython includes an architecture and library for interactive parallel computing. The enables Python functions, along with their arguments, to be run in parallel a multicore CPU, cluster or cloud using a simple Python API."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tutorials"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Data Publication API](Data%20Publication%20API.ipynb) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Monitoring an MPI Simulation - 1](Monitoring%20an%20MPI%20Simulation%20-%201.ipynb)\n",
"* [Monitoring an MPI Simulation - 2](Monitoring%20an%20MPI%20Simulation%20-%202.ipynb)\n",
"* [Parallel Decorator and map](Parallel%20Decorator%20and%20map.ipynb)\n",
"* [Parallel Magics](Parallel%20Magics.ipynb)\n",
"* [Using Dill](Using%20Dill.ipynb)\n",
"* [Using MPI with IPython Parallel](Using%20MPI%20with%20IPython%20Parallel.ipynb)\n",
"* [Monte Carlo Options](Monte%20Carlo%20Options.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Non-notebook examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This directory also contains examples that are regular Python (`.py`) files."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"customresults.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/customresults.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"dagdeps.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/dagdeps.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"dependencies.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/dependencies.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"fetchparse.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/fetchparse.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"iopubwatcher.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/iopubwatcher.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"itermapresult.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/itermapresult.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"nwmerge.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/nwmerge.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"phistogram.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/phistogram.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"task_profiler.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/task_profiler.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"throughput.py
"
],
"text/plain": [
"/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/throughput.py"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run ../utils/list_pyfiles.ipy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More substantial examples can be found in subdirectories:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"daVinci Word Count/
\n",
" pwordfreq.py
\n",
" wordfreq.py
"
],
"text/plain": [
"daVinci Word Count/\n",
" pwordfreq.py\n",
" wordfreq.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"interengine/
\n",
" bintree.py
\n",
" bintree_script.py
\n",
" communicator.py
\n",
" interengine.py
"
],
"text/plain": [
"interengine/\n",
" bintree.py\n",
" bintree_script.py\n",
" communicator.py\n",
" interengine.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"pi/
\n",
" parallelpi.py
\n",
" pidigits.py
"
],
"text/plain": [
"pi/\n",
" parallelpi.py\n",
" pidigits.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"rmt/
\n",
" rmt.ipy
\n",
" rmt.ipynb
\n",
" rmtkernel.py
"
],
"text/plain": [
"rmt/\n",
" rmt.ipy\n",
" rmt.ipynb\n",
" rmtkernel.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"wave2D/
\n",
" communicator.py
\n",
" parallelwave-mpi.py
\n",
" parallelwave.py
\n",
" RectPartitioner.py
\n",
" wavesolver.py
"
],
"text/plain": [
"wave2D/\n",
" communicator.py\n",
" parallelwave-mpi.py\n",
" parallelwave.py\n",
" RectPartitioner.py\n",
" wavesolver.py"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"workflow/
\n",
" client.py
\n",
" job_wrapper.py
\n",
" wmanager.py
"
],
"text/plain": [
"workflow/\n",
" client.py\n",
" job_wrapper.py\n",
" wmanager.py"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run ../utils/list_subdirs.ipy"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 0
}