contabo-vps-s-nvme-python

KVM VMware testing on Ubuntu 20.04 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 2109047-IB-CONTABOVP59
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
AMD EPYC 7282 16-Core
September 04
  3 Hours, 21 Minutes
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contabo-vps-s-nvme-pythonOpenBenchmarking.orgPhoronix Test Suite 10.6.1AMD EPYC 7282 16-Core (4 Cores)QEMU Standard PC (i440FX + PIIX 1996) (rel-1.14.0-0-g155821a1990b-prebuilt.qemu.org BIOS)Intel 440FX 82441FX PMC1 x 8192 MB RAM QEMU54GB QEMU HDDVMware SVGA IIRed Hat Virtio deviceUbuntu 20.045.4.0-81-generic (x86_64)1.0.2GCC 9.3.0ext4KVM VMwareProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelVulkanCompilerFile-SystemSystem LayerContabo-vps-s-nvme-python BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,brig,d,fortran,objc,obj-c++,gm2 --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-targets=nvptx-none=/build/gcc-9-HskZEa/gcc-9-9.3.0/debian/tmp-nvptx/usr,hsa --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - CPU Microcode: 0x830104d- Python 3.8.10- 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 usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Full AMD retpoline IBPB: conditional IBRS_FW STIBP: disabled RSB filling + srbds: Not affected + tsx_async_abort: Not affected

contabo-vps-s-nvme-pythonnumpy: cython-bench: N-Queenspybench: 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 Changepointmlpack: scikit_svmscikit-learn: AMD EPYC 7282 16-Core234.4636.006127135651815716616328.369734.715825314.368.2658209.73470.90838.793493.507104.83933.56165.020OpenBenchmarking.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 BenchmarkAMD EPYC 7282 16-Core50100150200250SE +/- 2.30, N = 3234.46

Cython Benchmark

Cython provides a superset of Python that is geared to deliver C-like levels of performance. This test profile makes use of Cython's bundled benchmark tests and runs an N-Queens sample test as a simple benchmark to the system's Cython performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterCython Benchmark 0.29.21Test: N-QueensAMD EPYC 7282 16-Core816243240SE +/- 0.31, N = 1536.01

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 7282 16-Core30060090012001500SE +/- 4.33, N = 31271

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 7282 16-Core80160240320400SE +/- 2.19, N = 3356

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3AMD EPYC 7282 16-Core110220330440550SE +/- 4.04, N = 3518

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosAMD EPYC 7282 16-Core306090120150SE +/- 1.76, N = 3157

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatAMD EPYC 7282 16-Core4080120160200SE +/- 1.33, N = 3166

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodyAMD EPYC 7282 16-Core4080120160200SE +/- 1.78, N = 5163

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibAMD EPYC 7282 16-Core714212835SE +/- 0.20, N = 328.3

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraceAMD EPYC 7282 16-Core150300450600750SE +/- 7.13, N = 3697

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsAMD EPYC 7282 16-Core816243240SE +/- 0.09, N = 334.7

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesAMD EPYC 7282 16-Core306090120150SE +/- 1.53, N = 3158

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileAMD EPYC 7282 16-Core60120180240300SE +/- 2.03, N = 3253

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupAMD EPYC 7282 16-Core48121620SE +/- 0.09, N = 314.3

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateAMD EPYC 7282 16-Core1530456075SE +/- 0.52, N = 1568.2

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonAMD EPYC 7282 16-Core140280420560700SE +/- 5.76, N = 7658

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 7282 16-Core50100150200250SE +/- 2.73, N = 12209.73

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropyAMD EPYC 7282 16-Core1632486480SE +/- 0.52, N = 370.91

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianAMD EPYC 7282 16-Core918273645SE +/- 0.29, N = 338.79

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineAMD EPYC 7282 16-Core110220330440550SE +/- 5.77, N = 4493.51

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointAMD EPYC 7282 16-Core20406080100SE +/- 0.95, N = 15104.84

Mlpack Benchmark

Mlpack benchmark scripts for machine learning libraries Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmAMD EPYC 7282 16-Core816243240SE +/- 0.40, N = 1533.56

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 0.22.1AMD EPYC 7282 16-Core4080120160200SE +/- 2.04, N = 3165.02