contabo-vps-s-ssd-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 2109079-IB-CONTABOVP02
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
Intel Xeon E5-2630 v4
September 06
  7 Hours, 18 Minutes
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contabo-vps-s-ssd-pythonOpenBenchmarking.orgPhoronix Test Suite 10.6.1Intel Xeon E5-2630 v4 (4 Cores)QEMU Standard PC (i440FX + PIIX 1996) (rel-1.12.1-0-ga5cab58e9a3f-prebuilt.qemu.org BIOS)Intel 440FX 82441FX PMC1 x 8192 MB RAM QEMU215GB 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-ssd-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: 0x1- Python 3.8.10- itlb_multihit: KVM: Vulnerable + l1tf: Mitigation of PTE Inversion + mds: Mitigation of Clear buffers; SMT Host state unknown + meltdown: Mitigation of PTI + 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 generic retpoline IBPB: conditional IBRS_FW STIBP: disabled RSB filling + srbds: Not affected + tsx_async_abort: Mitigation of Clear buffers; SMT Host state unknown

contabo-vps-s-ssd-pythoncython-bench: N-Queensmlpack: scikit_svmnumenta-nab: EXPoSEnumenta-nab: Relative Entropynumenta-nab: Windowed Gaussiannumenta-nab: Earthgecko Skylinenumenta-nab: Bayesian Changepointnumpy: 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_pythonscikit-learn: Intel Xeon E5-2630 v456.53247.83485.470151.49682.0871237.124288.258128.72197865186228630632352.91.4278.833754034.61681.48242.733OpenBenchmarking.org

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-QueensIntel Xeon E5-2630 v41326395265SE +/- 0.36, N = 356.53

Mlpack Benchmark

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

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmIntel Xeon E5-2630 v41122334455SE +/- 0.17, N = 347.83

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: EXPoSEIntel Xeon E5-2630 v4110220330440550SE +/- 4.30, N = 9485.47

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropyIntel Xeon E5-2630 v4306090120150SE +/- 2.03, N = 12151.50

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianIntel Xeon E5-2630 v420406080100SE +/- 0.87, N = 482.09

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineIntel Xeon E5-2630 v430060090012001500SE +/- 9.05, N = 31237.12

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointIntel Xeon E5-2630 v460120180240300SE +/- 2.41, N = 9288.26

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 BenchmarkIntel Xeon E5-2630 v4306090120150SE +/- 1.29, N = 3128.72

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 TimesIntel Xeon E5-2630 v4400800120016002000SE +/- 11.72, N = 31978

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: goIntel Xeon E5-2630 v4140280420560700SE +/- 6.23, N = 3651

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3Intel Xeon E5-2630 v42004006008001000SE +/- 10.41, N = 3862

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosIntel Xeon E5-2630 v460120180240300SE +/- 4.31, N = 12286

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatIntel Xeon E5-2630 v470140210280350SE +/- 2.84, N = 15306

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodyIntel Xeon E5-2630 v470140210280350SE +/- 5.46, N = 12323

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibIntel Xeon E5-2630 v41224364860SE +/- 0.74, N = 352.9

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraceIntel Xeon E5-2630 v40.31950.6390.95851.2781.5975SE +/- 0.06, N = 71.42

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsIntel Xeon E5-2630 v420406080100SE +/- 0.77, N = 1578.8

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesIntel Xeon E5-2630 v470140210280350SE +/- 3.62, N = 15337

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileIntel Xeon E5-2630 v4120240360480600SE +/- 7.06, N = 3540

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupIntel Xeon E5-2630 v4816243240SE +/- 0.69, N = 1234.6

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateIntel Xeon E5-2630 v44080120160200SE +/- 2.69, N = 12168

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonIntel Xeon E5-2630 v40.3330.6660.9991.3321.665SE +/- 0.01, N = 61.48

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 0.22.1Intel Xeon E5-2630 v450100150200250SE +/- 1.98, N = 9242.73