openSUSE TW x86-64-v3

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x86-64-v3
March 05 2023
  1 Hour, 36 Minutes
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March 05 2023
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x86-64-v3

Kernel Notes: Transparent Huge Pages: always
Processor Notes: Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xf4
Python Notes: Python 3.10.9
Security Notes: itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected

Default

Processor: Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads), Motherboard: Logic Supply RXM-181 (Z01-0002A026 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 256GB TS256GMTS800 + 15GB Ultra USB 3.0, Graphics: Intel UHD 630 CML GT2 31GB, Audio: Realtek ALC233, Monitor: DELL P2415Q, Network: Intel I219-LM + Intel I210

OS: openSUSE 20230303, Kernel: 6.2.1-1-default (x86_64), Desktop: KDE Plasma 5.27.2, Display Server: X Server 1.21.1.7, OpenGL: 4.6 Mesa 23.0.0, Compiler: GCC 12.2.1 20230124 [revision 193f7e62815b4089dfaed4c2bd34fd4f10209e27], File-System: btrfs, Screen Resolution: 1920x1080

openSUSE TW x86-64-v3OpenBenchmarking.orgPhoronix Test SuiteIntel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads)Logic Supply RXM-181 (Z01-0002A026 BIOS)Intel Comet Lake PCH32GB256GB TS256GMTS800 + 15GB Ultra USB 3.0Intel UHD 630 CML GT2 31GBRealtek ALC233DELL P2415QIntel I219-LM + Intel I210openSUSE 202303036.2.1-1-default (x86_64)KDE Plasma 5.27.2X Server 1.21.1.74.6 Mesa 23.0.0GCC 12.2.1 20230124 [revision 193f7e62815b4089dfaed4c2bd34fd4f10209e27]btrfs1920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionOpenSUSE TW X86-64-v3 BenchmarksSystem Logs- Transparent Huge Pages: always- Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xf4- Python 3.10.9- itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected

x86-64-v3 vs. Default ComparisonPhoronix Test SuiteBaseline+0.7%+0.7%+1.4%+1.4%+2.1%+2.1%+2.8%+2.8%2.1%Relative Entropy2.8%Earthgecko Skyline2.3%raytraceNumenta Anomaly BenchmarkNumenta Anomaly BenchmarkPyPerformancex86-64-v3Default

openSUSE TW x86-64-v3numenta-nab: Relative Entropynumenta-nab: Earthgecko Skylinepyperformance: raytracepyperformance: python_startuppyperformance: django_templatenumenta-nab: Bayesian Changepointpyperformance: 2to3numenta-nab: KNN CADpyperformance: gonumpy: system-libxml2: 400 KBsystem-libxml2: 3 MBpyperformance: regex_compilesystem-libxml2: 950 KBpyperformance: pickle_pure_pythonsystem-libxml2: 700 KBpybench: Total For Average Test Timesnumenta-nab: Contextual Anomaly Detector OSEsystem-libxml2: 112 MBsystem-libxml2: 1 MBsystem-libxml2: 2 MBnumenta-nab: Windowed Gaussianpyperformance: crypto_pyaespyperformance: json_loadspyperformance: pathlibpyperformance: nbodypyperformance: floatpyperformance: chaossystem-libxml2: 100 KBx86-64-v3Default32.665191.33154712.454.662.204352378.107243373.792872175194414483302122075.55165671449146415.07712826.319.61651161187933.576195.71853612.253.861.458348373.829241376.882892160193416481303121675.75965846450146615.08412826.319.616511611879OpenBenchmarking.org

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 time-series 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: Relative EntropyDefaultx86-64-v3816243240SE +/- 0.32, N = 6SE +/- 0.45, N = 333.5832.67

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineDefaultx86-64-v34080120160200SE +/- 0.76, N = 3SE +/- 2.23, N = 3195.72191.33

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: raytraceDefaultx86-64-v3120240360480600SE +/- 1.53, N = 3SE +/- 2.52, N = 3536547

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: python_startupDefaultx86-64-v33691215SE +/- 0.03, N = 3SE +/- 0.17, N = 312.212.4

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: django_templateDefaultx86-64-v31224364860SE +/- 0.15, N = 3SE +/- 0.17, N = 353.854.6

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 time-series 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: Bayesian ChangepointDefaultx86-64-v31428425670SE +/- 0.65, N = 15SE +/- 0.70, N = 1561.4662.20

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: 2to3Defaultx86-64-v380160240320400SE +/- 0.88, N = 3SE +/- 0.67, N = 3348352

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 time-series 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: KNN CADDefaultx86-64-v380160240320400SE +/- 1.44, N = 3SE +/- 1.99, N = 3373.83378.11

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: goDefaultx86-64-v350100150200250SE +/- 0.33, N = 3SE +/- 0.33, N = 3241243

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 BenchmarkDefaultx86-64-v380160240320400SE +/- 1.37, N = 3SE +/- 1.11, N = 3376.88373.79

System Libxml2 Parsing

This test measures the time to parse a random XML file with libxml2 via xmllint using the streaming API. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 400 KBDefaultx86-64-v360120180240300SE +/- 1.53, N = 3SE +/- 0.88, N = 3289287

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 3 MBDefaultx86-64-v35001000150020002500SE +/- 3.21, N = 3SE +/- 19.94, N = 321602175

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: regex_compileDefaultx86-64-v34080120160200SE +/- 0.00, N = 3SE +/- 0.00, N = 3193194

System Libxml2 Parsing

This test measures the time to parse a random XML file with libxml2 via xmllint using the streaming API. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 950 KBDefaultx86-64-v390180270360450SE +/- 1.20, N = 3SE +/- 0.67, N = 3416414

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: pickle_pure_pythonDefaultx86-64-v3100200300400500SE +/- 0.58, N = 3SE +/- 0.33, N = 3481483

System Libxml2 Parsing

This test measures the time to parse a random XML file with libxml2 via xmllint using the streaming API. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 700 KBDefaultx86-64-v370140210280350SE +/- 1.20, N = 3SE +/- 0.58, N = 3303302

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 TimesDefaultx86-64-v330060090012001500SE +/- 1.15, N = 3SE +/- 3.67, N = 312161220

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 time-series 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: Contextual Anomaly Detector OSEDefaultx86-64-v320406080100SE +/- 0.63, N = 3SE +/- 0.42, N = 375.7675.55

System Libxml2 Parsing

This test measures the time to parse a random XML file with libxml2 via xmllint using the streaming API. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 112 MBDefaultx86-64-v314K28K42K56K70KSE +/- 426.89, N = 3SE +/- 211.17, N = 36584665671

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 1 MBDefaultx86-64-v3100200300400500SE +/- 2.85, N = 3SE +/- 0.67, N = 3450449

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 2 MBDefaultx86-64-v330060090012001500SE +/- 11.39, N = 3SE +/- 6.89, N = 314661464

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 time-series 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: Windowed GaussianDefaultx86-64-v348121620SE +/- 0.13, N = 12SE +/- 0.10, N = 1315.0815.08

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: crypto_pyaesDefaultx86-64-v3306090120150SE +/- 0.00, N = 3SE +/- 0.00, N = 3128128

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: json_loadsDefaultx86-64-v3612182430SE +/- 0.07, N = 3SE +/- 0.03, N = 326.326.3

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: pathlibDefaultx86-64-v3510152025SE +/- 0.03, N = 3SE +/- 0.03, N = 319.619.6

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: nbodyDefaultx86-64-v34080120160200SE +/- 0.33, N = 3SE +/- 0.33, N = 3165165

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: floatDefaultx86-64-v3306090120150SE +/- 0.33, N = 3SE +/- 0.00, N = 3116116

OpenBenchmarking.orgMilliseconds, Fewer Is BetterPyPerformance 1.0.0Benchmark: chaosDefaultx86-64-v3306090120150SE +/- 0.00, N = 3SE +/- 0.00, N = 3118118

System Libxml2 Parsing

This test measures the time to parse a random XML file with libxml2 via xmllint using the streaming API. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterSystem Libxml2 ParsingFilesize: 100 KBDefaultx86-64-v320406080100SE +/- 0.33, N = 3SE +/- 0.00, N = 37979

x86-64-v3

Kernel Notes: Transparent Huge Pages: always
Processor Notes: Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xf4
Python Notes: Python 3.10.9
Security Notes: itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected

Testing initiated at 5 March 2023 13:30 by user .

Default

Processor: Intel Core i7-10700T @ 4.50GHz (8 Cores / 16 Threads), Motherboard: Logic Supply RXM-181 (Z01-0002A026 BIOS), Chipset: Intel Comet Lake PCH, Memory: 32GB, Disk: 256GB TS256GMTS800 + 15GB Ultra USB 3.0, Graphics: Intel UHD 630 CML GT2 31GB, Audio: Realtek ALC233, Monitor: DELL P2415Q, Network: Intel I219-LM + Intel I210

OS: openSUSE 20230303, Kernel: 6.2.1-1-default (x86_64), Desktop: KDE Plasma 5.27.2, Display Server: X Server 1.21.1.7, OpenGL: 4.6 Mesa 23.0.0, Compiler: GCC 12.2.1 20230124 [revision 193f7e62815b4089dfaed4c2bd34fd4f10209e27], File-System: btrfs, Screen Resolution: 1920x1080

Kernel Notes: Transparent Huge Pages: always
Processor Notes: Scaling Governor: intel_pstate powersave (EPP: balance_performance) - CPU Microcode: 0xf4
Python Notes: Python 3.10.9
Security Notes: itlb_multihit: KVM: Mitigation of VMX disabled + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Mitigation of Clear buffers; SMT vulnerable + retbleed: Mitigation of Enhanced IBRS + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced IBRS IBPB: conditional RSB filling PBRSB-eIBRS: SW sequence + srbds: Mitigation of Microcode + tsx_async_abort: Not affected

Testing initiated at 5 March 2023 16:44 by user .