pythontests

AMD EPYC 7302P 16-Core testing with a Supermicro H11SSL-i v2.00 (2.1 BIOS) and ASPEED on CentOS 7.8.2003 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2010082-NE-PYTHONTES43
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pythontestsOpenBenchmarking.orgPhoronix Test Suite 10.2.0AMD EPYC 7302P 16-Core @ 3.00GHz (16 Cores / 32 Threads)Supermicro H11SSL-i v2.00 (2.1 BIOS)AMD Starship/Matisse4 x 64 GB DDR4-3200MT/s 36ASF8G72PZ-3G2E12 x 1600GB SAMSUNG MZPLL1T6HAJQ-00005 + 2 x 960GB Micron_5300_MTFD + 4 x 10001GB HGST HUH721010ALASPEED2 x Mellanox MT27710 + 2 x Intel I210CentOS 7.8.20033.10.0-1127.19.1.el7.x86_64 (x86_64)GCC 4.8.5 20150623xfs1024x768ProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelCompilerFile-SystemScreen ResolutionPythontests PerformanceSystem Logs- Scaling Governor: acpi-cpufreq conservative - CPU Microcode: 0x8301034- Python 2.7.5 + Python 3.6.8- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl and seccomp + spectre_v1: Mitigation of Load fences usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Full retpoline IBPB + srbds: Not affected + tsx_async_abort: Not affected

pythontestspybench: Total For Average Test Timespyperformance: gopyperformance: 2to3pyperformance: chaospyperformance: floatpyperformance: nbodypyperformance: pathlibpyperformance: raytracepyperformance: json_loadspyperformance: crypto_pyaespyperformance: regex_compilepyperformance: python_startuppyperformance: django_templatepyperformance: pickle_pure_pythonnumenta-nab: EXPoSEnumenta-nab: Relative Entropynumenta-nab: Windowed Gaussiannumenta-nab: Earthgecko Skylinenumenta-nab: Bayesian ChangepointAMD EPYC 7302P 16-Core178445253022520820228.71.0436.419033114.81289191125.90024.25611.509115.82456.358OpenBenchmarking.org

PyBench

This test profile reports the total time of the different average timed test results from PyBench. PyBench reports average test times for different functions such as BuiltinFunctionCalls and NestedForLoops, with this total result providing a rough estimate as to Python's average performance on a given system. This test profile runs PyBench each time for 20 rounds. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyBench 2018-02-16Total For Average Test TimesAMD EPYC 7302P 16-Core400800120016002000SE +/- 3.00, N = 31784

PyPerformance

PyPerformance is the reference Python performance benchmark suite. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: goAMD EPYC 7302P 16-Core100200300400500SE +/- 1.53, N = 3452

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3AMD EPYC 7302P 16-Core110220330440550530

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosAMD EPYC 7302P 16-Core50100150200250225

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatAMD EPYC 7302P 16-Core50100150200250SE +/- 0.33, N = 3208

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodyAMD EPYC 7302P 16-Core4080120160200202

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibAMD EPYC 7302P 16-Core714212835SE +/- 0.03, N = 328.7

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraceAMD EPYC 7302P 16-Core0.2340.4680.7020.9361.17SE +/- 0.01, N = 31.04

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsAMD EPYC 7302P 16-Core816243240SE +/- 0.12, N = 336.4

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesAMD EPYC 7302P 16-Core4080120160200SE +/- 0.67, N = 3190

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileAMD EPYC 7302P 16-Core70140210280350SE +/- 1.15, N = 3331

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupAMD EPYC 7302P 16-Core48121620SE +/- 0.09, N = 314.8

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateAMD EPYC 7302P 16-Core306090120150128

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonAMD EPYC 7302P 16-Core2004006008001000SE +/- 1.20, N = 3919

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: EXPoSEAMD EPYC 7302P 16-Core2004006008001000SE +/- 5.54, N = 31125.90

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropyAMD EPYC 7302P 16-Core612182430SE +/- 0.08, N = 324.26

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianAMD EPYC 7302P 16-Core3691215SE +/- 0.09, N = 311.51

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineAMD EPYC 7302P 16-Core306090120150SE +/- 0.26, N = 3115.82

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointAMD EPYC 7302P 16-Core1326395265SE +/- 0.53, N = 356.36


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