sia-3000mts-ddr4-furyan-07-data-science-bench

AMD Ryzen 7 5700G testing with a MSI MAG B550 TORPEDO (MS-7C91) v3.0 (H.53 BIOS) and AMD Cezanne 2GB 2x8GB 3000MT/s Manjaro 21.1.6

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Sia - 3000MT/s - furyan-07-data-science-bench
November 09 2021
  1 Hour, 13 Minutes
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sia-3000mts-ddr4-furyan-07-data-science-benchOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 7 5700G @ 3.80GHz (8 Cores / 16 Threads)MSI MAG B550 TORPEDO (MS-7C91) v3.0 (H.53 BIOS)AMD Renoir Root Complex14GB2000GB Samsung SSD 970 EVO Plus 2TBAMD Cezanne 2GB (2000/1500MHz)AMD Device 1637GNV34DBERealtek RTL8125 2.5GbE + Intel-AC 9260ManjaroLinux 21.1.65.13.19-2-MANJARO (x86_64)Xfce 4.16X Server 1.20.134.6 Mesa 21.2.3 (LLVM 12.0.1)1.2.182GCC 11.1.0 + Clang 12.0.1ext43440x1440ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLVulkanCompilerFile-SystemScreen ResolutionSia-3000mts-ddr4-furyan-07-data-science-bench BenchmarksSystem Logs- Transparent Huge Pages: madvise- --disable-libssp --disable-libstdcxx-pch --disable-libunwind-exceptions --disable-werror --enable-__cxa_atexit --enable-cet=auto --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-default-ssp --enable-gnu-indirect-function --enable-gnu-unique-object --enable-install-libiberty --enable-languages=c,c++,ada,fortran,go,lto,objc,obj-c++,d --enable-lto --enable-multilib --enable-plugin --enable-shared --enable-threads=posix --mandir=/usr/share/man --with-isl --with-linker-hash-style=gnu - Scaling Governor: acpi-cpufreq schedutil (Boost: Enabled) - CPU Microcode: 0xa50000c- Python 3.9.7- 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: always-on RSB filling + srbds: Not affected + tsx_async_abort: Not affected

sia-3000mts-ddr4-furyan-07-data-science-benchmrbayes: Primate Phylogeny Analysishmmer: Pfam Database Searchnumpy: 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_pythonmlpack: scikit_icamlpack: scikit_qdamlpack: scikit_svmmlpack: scikit_linearridgeregressionscikit-learn: Sia - 3000MT/s - furyan-07-data-science-bench91.68292.950488.2217.59876519827085.590.299.214.737118.782.91348.7537.733231.9362.3211.551.957.239OpenBenchmarking.org

Timed MrBayes Analysis

This test performs a bayesian analysis of a set of primate genome sequences in order to estimate their phylogeny. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed MrBayes Analysis 3.2.7Primate Phylogeny AnalysisSia - 3000MT/s - furyan-07-data-science-bench20406080100SE +/- 1.29, N = 1591.681. (CC) gcc options: -mmmx -msse -msse2 -msse3 -mssse3 -msse4.1 -msse4.2 -msse4a -msha -maes -mavx -mfma -mavx2 -mrdrnd -mbmi -mbmi2 -madx -mabm -O3 -std=c99 -pedantic -lm -lreadline

Timed HMMer Search

This test searches through the Pfam database of profile hidden markov models. The search finds the domain structure of Drosophila Sevenless protein. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterTimed HMMer Search 3.3.2Pfam Database SearchSia - 3000MT/s - furyan-07-data-science-bench20406080100SE +/- 0.04, N = 392.951. (CC) gcc options: -O3 -pthread -lhmmer -leasel -lm -lmpi

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 BenchmarkSia - 3000MT/s - furyan-07-data-science-bench110220330440550SE +/- 2.31, N = 3488.22

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-QueensSia - 3000MT/s - furyan-07-data-science-bench48121620SE +/- 0.01, N = 317.60

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 TimesSia - 3000MT/s - furyan-07-data-science-bench160320480640800SE +/- 0.67, N = 3765

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: goSia - 3000MT/s - furyan-07-data-science-bench4080120160200SE +/- 0.00, N = 3198

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: 2to3Sia - 3000MT/s - furyan-07-data-science-bench60120180240300SE +/- 0.58, N = 3270

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosSia - 3000MT/s - furyan-07-data-science-bench20406080100SE +/- 0.15, N = 385.5

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatSia - 3000MT/s - furyan-07-data-science-bench20406080100SE +/- 0.23, N = 390.2

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodySia - 3000MT/s - furyan-07-data-science-bench20406080100SE +/- 0.23, N = 399.2

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibSia - 3000MT/s - furyan-07-data-science-bench48121620SE +/- 0.00, N = 314.7

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: raytraceSia - 3000MT/s - furyan-07-data-science-bench80160240320400SE +/- 0.00, N = 3371

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsSia - 3000MT/s - furyan-07-data-science-bench510152025SE +/- 0.00, N = 318.7

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: crypto_pyaesSia - 3000MT/s - furyan-07-data-science-bench20406080100SE +/- 0.07, N = 382.9

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: regex_compileSia - 3000MT/s - furyan-07-data-science-bench306090120150SE +/- 0.00, N = 3134

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupSia - 3000MT/s - furyan-07-data-science-bench246810SE +/- 0.02, N = 38.75

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateSia - 3000MT/s - furyan-07-data-science-bench918273645SE +/- 0.03, N = 337.7

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pickle_pure_pythonSia - 3000MT/s - furyan-07-data-science-bench70140210280350SE +/- 0.00, N = 3332

Mlpack Benchmark

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

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_icaSia - 3000MT/s - furyan-07-data-science-bench714212835SE +/- 0.05, N = 331.93

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_qdaSia - 3000MT/s - furyan-07-data-science-bench1428425670SE +/- 0.12, N = 362.32

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_svmSia - 3000MT/s - furyan-07-data-science-bench3691215SE +/- 0.10, N = 311.55

OpenBenchmarking.orgSeconds, Fewer Is BetterMlpack BenchmarkBenchmark: scikit_linearridgeregressionSia - 3000MT/s - furyan-07-data-science-bench0.43880.87761.31641.75522.194SE +/- 0.02, N = 31.95

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 0.22.1Sia - 3000MT/s - furyan-07-data-science-bench246810SE +/- 0.026, N = 37.239