ionos-vps-s-python

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 2108226-IB-IONOSVPSS02
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
Intel Xeon E5-2660 v4
August 22
  17 Hours, 58 Minutes
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ionos-vps-s-pythonOpenBenchmarking.orgPhoronix Test Suite 10.6.1Intel Xeon E5-2660 v4 (1 Core)Intel 440BX (6.00 BIOS)Intel 440BX/ZX/DX1 x 512 MB DRAM11GB Virtual diskVMware SVGA IIVMware VMXNET3Ubuntu 20.045.4.0-81-generic (x86_64)1.0.2GCC 9.3.0ext41176x885VMwareProcessorMotherboardChipsetMemoryDiskGraphicsNetworkOSKernelVulkanCompilerFile-SystemScreen ResolutionSystem LayerIonos-vps-s-python BenchmarksSystem Logs- Transparent Huge Pages: never- --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: 0xb000038- 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: Not affected

ionos-vps-s-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 Changepointscikit-learn: Intel Xeon E5-2660 v4102.9758.546214761492525828529553.61.1458.325242323.712284.241275.352411.797232.2832704.032771.24040372.949OpenBenchmarking.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 BenchmarkIntel Xeon E5-2660 v420406080100SE +/- 1.04, N = 3102.97

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 E5-2660 v41326395265SE +/- 0.10, N = 358.55

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 E5-2660 v45001000150020002500SE +/- 8.01, N = 32147

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 E5-2660 v4130260390520650SE +/- 4.73, N = 3614

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3Intel Xeon E5-2660 v42004006008001000SE +/- 5.49, N = 3925

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosIntel Xeon E5-2660 v460120180240300SE +/- 2.16, N = 15258

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatIntel Xeon E5-2660 v460120180240300285

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodyIntel Xeon E5-2660 v460120180240300SE +/- 2.08, N = 3295

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibIntel Xeon E5-2660 v41224364860SE +/- 0.19, N = 353.6

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraceIntel Xeon E5-2660 v40.25650.5130.76951.0261.2825SE +/- 0.01, N = 31.14

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsIntel Xeon E5-2660 v41326395265SE +/- 0.69, N = 458.3

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesIntel Xeon E5-2660 v460120180240300SE +/- 1.76, N = 3252

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileIntel Xeon E5-2660 v490180270360450SE +/- 3.71, N = 3423

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupIntel Xeon E5-2660 v4612182430SE +/- 0.03, N = 323.7

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateIntel Xeon E5-2660 v4306090120150SE +/- 1.23, N = 6122

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonIntel Xeon E5-2660 v420406080100SE +/- 83.16, N = 1284.24

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 E5-2660 v430060090012001500SE +/- 14.31, N = 41275.35

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropyIntel Xeon E5-2660 v490180270360450SE +/- 3.78, N = 3411.80

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianIntel Xeon E5-2660 v450100150200250SE +/- 0.79, N = 3232.28

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineIntel Xeon E5-2660 v46001200180024003000SE +/- 29.77, N = 32704.03

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointIntel Xeon E5-2660 v4170340510680850SE +/- 3.85, N = 3771.24

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 E5-2660 v49K18K27K36K45K40372.95