Python Benchmark Results

Raspberry Pi 4 S3569505 Python Benchmark Test

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s3569505
November 21 2020
  10 Hours, 52 Minutes
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Python Benchmark ResultsOpenBenchmarking.orgPhoronix Test SuiteARMv7 Cortex-A72 @ 1.50GHz (4 Cores)BCM2711 Raspberry Pi 4 Model B Rev 1.14096MB2 x 4GB Cruzer Blade + 16GB SL16Gvc4drmfbBenQ XL2411ZRaspbian 105.4.75-v7l-s3569505+ (armv7l)LXDE 0.10.0X Server 1.20.4modesetting 1.20.43.3 Mesa 19.3.2 (LLVM 9.0.1 128 bits)GCC 8.3.0ext41870x1002ProcessorMotherboardMemoryDiskGraphicsMonitorOSKernelDesktopDisplay ServerDisplay DriverOpenGLCompilerFile-SystemScreen ResolutionPython Benchmark Results PerformanceSystem Logs- --build=arm-linux-gnueabihf --disable-libitm --disable-libquadmath --disable-libquadmath-support --disable-sjlj-exceptions --disable-werror --enable-bootstrap --enable-checking=release --enable-clocale=gnu --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++ --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-nls --enable-objc-gc=auto --enable-plugin --enable-shared --enable-threads=posix --host=arm-linux-gnueabihf --program-prefix=arm-linux-gnueabihf- --target=arm-linux-gnueabihf --with-arch=armv6 --with-default-libstdcxx-abi=new --with-float=hard --with-fpu=vfp --with-gcc-major-version-only --with-target-system-zlib -v - Scaling Governor: cpufreq-dt ondemand- Python 2.7.16 + Python 3.7.3

Python Benchmark Resultsnumpy: pybench: 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 Changepointscikit-learn: s356950526.4652721.371.796335956091472.7714256998243.53672.835438.051335.477168.5161942.963936.07278.724OpenBenchmarking.org

Numpy Benchmark

This is a test to obtain the general Numpy performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgScore, More Is BetterNumpy Benchmarks3569505612182430SE +/- 0.05, N = 326.46

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 Timess356950511002200330044005500SE +/- 7.51, N = 35272

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: gos35695050.30830.61660.92491.23321.5415SE +/- 0.00, N = 31.37

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3s35695050.40280.80561.20841.61122.014SE +/- 0.00, N = 31.79

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaoss3569505140280420560700SE +/- 0.58, N = 3633

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floats3569505130260390520650595

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodys3569505130260390520650SE +/- 2.08, N = 3609

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibs3569505306090120150147

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraces35695050.62331.24661.86992.49323.1165SE +/- 0.00, N = 32.77

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadss3569505306090120150142

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaess3569505120240360480600SE +/- 0.67, N = 3569

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compiles35695052004006008001000SE +/- 0.67, N = 3982

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startups35695051020304050SE +/- 0.03, N = 343.5

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templates356950580160240320400SE +/- 0.33, N = 3367

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythons35695050.63681.27361.91042.54723.184SE +/- 0.00, N = 32.83

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: EXPoSEs356950512002400360048006000SE +/- 15.08, N = 35438.05

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative Entropys356950570140210280350SE +/- 0.85, N = 3335.48

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed Gaussians35695054080120160200SE +/- 0.50, N = 3168.52

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko Skylines3569505400800120016002000SE +/- 5.32, N = 31942.96

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian Changepoints35695052004006008001000SE +/- 0.68, N = 3936.07

Scikit-Learn

Scikit-learn is a Python module for machine learning Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 0.22.1s356950520406080100SE +/- 0.03, N = 378.72