{ "info": { "author": "Stijn De Weirdt", "author_email": "stijn.deweirdt@ugent.be", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: GNU General Public License v2 (GPLv2)" ], "description": "Description\n===========\n\nmympingpong is a mpi4py based random pair pingpong network stress test.\n\nInstallation\n============\n\nWe recommend using [EasyBuild][eb_url] to automatically install `mympingpong`\nand it's dependencies.\n\nThe required steps involve building a patched mpi4py\nand a parallel enabled h5py.\nInstructions on manual installing these can be found in\nmanual/install_insructions.\n\n\nUsage\n========\n\nAfter successfull installation, simply submit a job that runs\n```\nmympirun mympingpong -f output_dir -i nr_iterations -n nr_tests_per_rank\n```\n\nDependencies\n============\n\n(including but not limited to)\n\n - numpy >= 1.8.2\n - vsc-base >= 1.8.6\n - matplotlib >= 1.3.1\n - h5py >= 2.5.0\n\nExamples\n=======\n\nThe end result of a `mympingponganalysis` is a visual representation of the\npingpong Round Trip Time (RTT) of all the pairs. This can give insight in the architecture and/or topology\n(or any issues with it).\n\n## Example output\n![](/result_images/example.png)\n\neach plot graph up to 5 graphs\n - largest plot: each datapoint is the average pingpong RTT between pairs (x,y), where x and y are the MPI ranks.\n - histogram of all pingpong RTT\n - heatmap of number of pingpongs ran on the (x,y) pair\n - heatmap of the standard deviation in the data from running tests on the (x,y) pair\n - if a mask as been used, a histogram of all pingpong RTT in the mask interval\n\n### Every MPI rank on a unique node\n![](/result_images/stdev.png)\n\nThe result of running pingpong on 128 nodes, with each rank pinned to core 0.\nThe latency graph clearly shows which ranks are located on the same switch.\nThe standard deviation graph show that something fishy is going on with ranks 32-48.\n\n### Every MPI rank on a unique core\n![](/result_images/cores.png)\n\nThe result of running pingpong on 4 nodes with 16 cores per node.\nIn this example the NUMA nodes are visible. Inter node communication is clearly slower then intra node, but only by a factor of 3-5.\nThe histogram shows 3 regions:shared L2 cache, on die and inter-die.\nOn the latency graph the switch is also visible as a greenish shade.\n\n### Oversubscribing\n![](/result_images/oversubscribe.png)\n\nThe result of running 32 ranks per node on 4 nodes with 16 cores per node\n\nUsing PingPong to its fullest potential\n======================\n\nYou should always take care to have enough samples per pair. In other words, \nthe -n parameter should be high enough to ensure every rank has a consistent result. \nA quick way to see if consistent results are achieved, is when the pair samples graph has a deep red color\n\nKnowing that there is a problem might be useful, but you're more than like also going to want to know where the problem is located. \nInformation on what rank is pinned to what core on which node is present in the outputfile, but this data is not plotted with mympingponganalysis. 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