epyc last

AMD EPYC 7343 16-Core testing with a Supermicro H12SSL-i v1.02 (2.4 BIOS) and astdrmfb on AlmaLinux 9.1 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 2304307-NE-EPYCLAST283
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a
April 30 2023
  7 Hours, 46 Minutes
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April 30 2023
  2 Hours, 34 Minutes
c
April 30 2023
  2 Hours, 34 Minutes
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April 30 2023
  2 Hours, 34 Minutes
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epyc lastOpenBenchmarking.orgPhoronix Test SuiteAMD EPYC 7343 16-Core @ 3.20GHz (16 Cores / 32 Threads)Supermicro H12SSL-i v1.02 (2.4 BIOS)8 x 64 GB DDR4-3200MT/s Samsung M393A8G40AB2-CWE2 x 1920GB SAMSUNG MZQL21T9HCJR-00A07astdrmfbDELL E207WFPAlmaLinux 9.15.14.0-162.12.1.el9_1.x86_64 (x86_64)GCC 11.3.1 20220421ext41680x1050ProcessorMotherboardMemoryDiskGraphicsMonitorOSKernelCompilerFile-SystemScreen ResolutionEpyc Last BenchmarksSystem Logs- Transparent Huge Pages: always- --build=x86_64-redhat-linux --disable-libunwind-exceptions --enable-__cxa_atexit --enable-bootstrap --enable-cet --enable-checking=release --enable-gnu-indirect-function --enable-gnu-unique-object --enable-host-bind-now --enable-host-pie --enable-initfini-array --enable-languages=c,c++,fortran,lto --enable-link-serialization=1 --enable-multilib --enable-offload-targets=nvptx-none --enable-plugin --enable-shared --enable-threads=posix --mandir=/usr/share/man --with-arch_32=x86-64 --with-arch_64=x86-64-v2 --with-build-config=bootstrap-lto --with-gcc-major-version-only --with-linker-hash-style=gnu --with-tune=generic --without-cuda-driver --without-isl - NONE / relatime,rw,stripe=32 / raid1 nvme1n1p3[0] nvme0n1p3[1] Block Size: 4096 - Scaling Governor: acpi-cpufreq performance (Boost: Enabled) - CPU Microcode: 0xa001173 - Python 3.9.14- itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Retpolines IBPB: conditional IBRS_FW STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

abcdResult OverviewPhoronix Test Suite100%104%109%113%118%SQLiteInfluxDBQuantLibIntel TensorFlowSVT-AV1

epyc lastsqlite: 2sqlite: 4sqlite: 8sqlite: 16sqlite: 32quantlib: svt-av1: Preset 4 - Bosphorus 4Ksvt-av1: Preset 8 - Bosphorus 4Ksvt-av1: Preset 12 - Bosphorus 4Ksvt-av1: Preset 13 - Bosphorus 4Ksvt-av1: Preset 4 - Bosphorus 1080psvt-av1: Preset 8 - Bosphorus 1080psvt-av1: Preset 12 - Bosphorus 1080psvt-av1: Preset 13 - Bosphorus 1080pintel-tensorflow: resnet50_fp32_pretrained_model - 1intel-tensorflow: resnet50_fp32_pretrained_model - 1intel-tensorflow: resnet50_int8_pretrained_model - 1intel-tensorflow: resnet50_int8_pretrained_model - 1intel-tensorflow: resnet50_fp32_pretrained_model - 16intel-tensorflow: resnet50_fp32_pretrained_model - 32intel-tensorflow: resnet50_fp32_pretrained_model - 64intel-tensorflow: resnet50_fp32_pretrained_model - 96intel-tensorflow: resnet50_int8_pretrained_model - 16intel-tensorflow: resnet50_int8_pretrained_model - 32intel-tensorflow: resnet50_int8_pretrained_model - 64intel-tensorflow: resnet50_int8_pretrained_model - 96intel-tensorflow: resnet50_fp32_pretrained_model - 256intel-tensorflow: resnet50_fp32_pretrained_model - 512intel-tensorflow: resnet50_fp32_pretrained_model - 960intel-tensorflow: resnet50_int8_pretrained_model - 256intel-tensorflow: resnet50_int8_pretrained_model - 512intel-tensorflow: resnet50_int8_pretrained_model - 960intel-tensorflow: inceptionv4_fp32_pretrained_model - 1intel-tensorflow: inceptionv4_fp32_pretrained_model - 1intel-tensorflow: inceptionv4_int8_pretrained_model - 1intel-tensorflow: inceptionv4_int8_pretrained_model - 1intel-tensorflow: mobilenetv1_fp32_pretrained_model - 1intel-tensorflow: mobilenetv1_int8_pretrained_model - 1intel-tensorflow: inceptionv4_fp32_pretrained_model - 16intel-tensorflow: inceptionv4_fp32_pretrained_model - 32intel-tensorflow: inceptionv4_fp32_pretrained_model - 64intel-tensorflow: inceptionv4_fp32_pretrained_model - 96intel-tensorflow: inceptionv4_int8_pretrained_model - 16intel-tensorflow: inceptionv4_int8_pretrained_model - 32intel-tensorflow: inceptionv4_int8_pretrained_model - 64intel-tensorflow: inceptionv4_int8_pretrained_model - 96intel-tensorflow: mobilenetv1_fp32_pretrained_model - 16intel-tensorflow: mobilenetv1_fp32_pretrained_model - 32intel-tensorflow: mobilenetv1_fp32_pretrained_model - 64intel-tensorflow: mobilenetv1_fp32_pretrained_model - 96intel-tensorflow: mobilenetv1_int8_pretrained_model - 16intel-tensorflow: mobilenetv1_int8_pretrained_model - 32intel-tensorflow: mobilenetv1_int8_pretrained_model - 64intel-tensorflow: mobilenetv1_int8_pretrained_model - 96intel-tensorflow: inceptionv4_fp32_pretrained_model - 256intel-tensorflow: inceptionv4_fp32_pretrained_model - 512intel-tensorflow: inceptionv4_fp32_pretrained_model - 960intel-tensorflow: inceptionv4_int8_pretrained_model - 256intel-tensorflow: inceptionv4_int8_pretrained_model - 512intel-tensorflow: inceptionv4_int8_pretrained_model - 960intel-tensorflow: mobilenetv1_fp32_pretrained_model - 256intel-tensorflow: mobilenetv1_fp32_pretrained_model - 512intel-tensorflow: mobilenetv1_fp32_pretrained_model - 960intel-tensorflow: mobilenetv1_int8_pretrained_model - 256intel-tensorflow: mobilenetv1_int8_pretrained_model - 512intel-tensorflow: mobilenetv1_int8_pretrained_model - 960influxdb: 4 - 10000 - 2,5000,1 - 10000influxdb: 64 - 10000 - 2,5000,1 - 10000abcd2.1503.1785.0658.47611.3213202.13.76652.582174.519160.5019.02795.925547.498548.00779.28312.613221.8554.508168.744174.040170.509169.726346.017356.317365.095373.126167.968168.371168.721382.093383.615391.68032.2330.81769.0414.4361045.591933.3753.2053.1052.4451.83113.31117.00118.48118.25932.19981.32998.43990.352003.492056.182112.332083.5551.9251.7651.76119.18119.90120.741001.61976.58983.762090.972170.092133.181547894.41602099.92.0412.9383.8566.20911.8113206.13.78251.982175.683160.6649.06495.707542.028542.62579.77612.535221.7694.509171.632174.303170.516169.649347.926361.362364.043373.723167.998168.641169.729380.652385.546392.0733.1630.47569.1614.4151048.21932.4753.4752.9152.4051.72111.63117.81118.93119.097759734929.58984.41997.73986.722002.092110.192120.772081.8752.0751.8751.78119.18119.61120.941000.28974.84982.712106.542179.092128.11545780.81593776.42.1062.9043.9667.19811.7433200.73.79152.523172.696160.1399.12196.329535.678545.86479.98812.502216.3694.622171.049174.255170.603169.971348.365357.714365.003372.933168.055168.788170.093381.064385.5392.38332.0630.64769.0114.4261046.791933.5852.9953.1151.7251.90113.63116.93117.61119.82933.76982.66999.93988.1519892037.862063.782087.3952.0651.7751.59119.33119.91120.741001.43976.22982.462028.822161.982137.21552035.61599391.92.0392.7123.7616.07511.5983192.73.78452.572174.836159.5219.2895.648539.588547.43280.21812.466217.5934.596169.333172.269170.261169.31344.868357.035365.305372.928168.16168.365169.338380.015384.26391.38331.8630.72369.0714.3771046.41934.3253.2253.2751.9752.00113.36117.89119.83118.23931.22981.61999.92988.721984.382033.572091.662071.1852.0451.7651.62119.45120.56120.571001.3974.69984.362091.62171.792132.2915607581600346.9OpenBenchmarking.org

SQLite

This is a simple benchmark of SQLite. At present this test profile just measures the time to perform a pre-defined number of insertions on an indexed database with a variable number of concurrent repetitions -- up to the maximum number of CPU threads available. Learn more via the OpenBenchmarking.org test page.

Threads / Copies: 1

a: The test run did not produce a result.

b: The test run did not produce a result.

c: The test run did not produce a result.

d: The test run did not produce a result.

OpenBenchmarking.orgSeconds, Fewer Is BetterSQLite 3.41.2Threads / Copies: 2abcd0.48380.96761.45141.93522.419SE +/- 0.004, N = 32.1502.0412.1062.0391. (CC) gcc options: -O2 -lz -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterSQLite 3.41.2Threads / Copies: 4abcd0.71511.43022.14532.86043.5755SE +/- 0.030, N = 153.1782.9382.9042.7121. (CC) gcc options: -O2 -lz -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterSQLite 3.41.2Threads / Copies: 8abcd1.13962.27923.41884.55845.698SE +/- 0.038, N = 35.0653.8563.9663.7611. (CC) gcc options: -O2 -lz -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterSQLite 3.41.2Threads / Copies: 16abcd246810SE +/- 0.163, N = 138.4766.2097.1986.0751. (CC) gcc options: -O2 -lz -lm

OpenBenchmarking.orgSeconds, Fewer Is BetterSQLite 3.41.2Threads / Copies: 32abcd3691215SE +/- 0.05, N = 311.3211.8111.7411.601. (CC) gcc options: -O2 -lz -lm

QuantLib

QuantLib is an open-source library/framework around quantitative finance for modeling, trading and risk management scenarios. QuantLib is written in C++ with Boost and its built-in benchmark used reports the QuantLib Benchmark Index benchmark score. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMFLOPS, More Is BetterQuantLib 1.30abcd7001400210028003500SE +/- 1.35, N = 33202.13206.13200.73192.71. (CXX) g++ options: -O3 -march=native -fPIE -pie

SVT-AV1

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 4 - Input: Bosphorus 4Kabcd0.8531.7062.5593.4124.265SE +/- 0.019, N = 33.7663.7823.7913.7841. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 8 - Input: Bosphorus 4Kabcd1224364860SE +/- 0.19, N = 352.5851.9852.5252.571. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 12 - Input: Bosphorus 4Kabcd4080120160200SE +/- 0.56, N = 3174.52175.68172.70174.841. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 13 - Input: Bosphorus 4Kabcd4080120160200SE +/- 0.85, N = 3160.50160.66160.14159.521. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 4 - Input: Bosphorus 1080pabcd3691215SE +/- 0.031, N = 39.0279.0649.1219.2801. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 8 - Input: Bosphorus 1080pabcd20406080100SE +/- 0.42, N = 395.9395.7196.3395.651. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 12 - Input: Bosphorus 1080pabcd120240360480600SE +/- 0.64, N = 3547.50542.03535.68539.591. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

OpenBenchmarking.orgFrames Per Second, More Is BetterSVT-AV1 1.5Encoder Mode: Preset 13 - Input: Bosphorus 1080pabcd120240360480600SE +/- 0.34, N = 3548.01542.63545.86547.431. (CXX) g++ options: -march=native -mno-avx -mavx2 -mavx512f -mavx512bw -mavx512dq

Intel TensorFlow

Intel optimized version of TensorFlow with benchmarks of Intel AI models and configurable batch sizes. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 1abcd20406080100SE +/- 0.09, N = 379.2879.7879.9980.22

OpenBenchmarking.orgms, Fewer Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 1abcd3691215SE +/- 0.01, N = 312.6112.5412.5012.47

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 1abcd50100150200250SE +/- 1.58, N = 3221.86221.77216.37217.59

OpenBenchmarking.orgms, Fewer Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 1abcd1.042.083.124.165.2SE +/- 0.032, N = 34.5084.5094.6224.596

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 16abcd4080120160200SE +/- 0.83, N = 3168.74171.63171.05169.33

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 32abcd4080120160200SE +/- 0.31, N = 3174.04174.30174.26172.27

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 64abcd4080120160200SE +/- 0.12, N = 3170.51170.52170.60170.26

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 96abcd4080120160200SE +/- 0.11, N = 3169.73169.65169.97169.31

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 16abcd80160240320400SE +/- 1.45, N = 3346.02347.93348.37344.87

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 32abcd80160240320400SE +/- 0.47, N = 3356.32361.36357.71357.04

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 64abcd80160240320400SE +/- 0.43, N = 3365.10364.04365.00365.31

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 96abcd80160240320400SE +/- 0.27, N = 3373.13373.72372.93372.93

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 256abcd4080120160200SE +/- 0.12, N = 3167.97168.00168.06168.16

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 512abcd4080120160200SE +/- 0.21, N = 3168.37168.64168.79168.37

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_fp32_pretrained_model - Batch Size: 960abcd4080120160200SE +/- 0.30, N = 3168.72169.73170.09169.34

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 256abcd80160240320400SE +/- 0.54, N = 3382.09380.65381.06380.02

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 512abcd80160240320400SE +/- 0.21, N = 3383.62385.55385.50384.26

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: resnet50_int8_pretrained_model - Batch Size: 960abcd90180270360450SE +/- 0.57, N = 3391.68392.07392.38391.38

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 1abcd816243240SE +/- 0.26, N = 332.2333.1632.0631.86

OpenBenchmarking.orgms, Fewer Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 1abcd714212835SE +/- 0.10, N = 330.8230.4830.6530.72

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 1abcd1530456075SE +/- 0.07, N = 369.0469.1669.0169.07

OpenBenchmarking.orgms, Fewer Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 1abcd48121620SE +/- 0.03, N = 314.4414.4214.4314.38

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 1abcd2004006008001000SE +/- 0.98, N = 31045.591048.201046.791046.40

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 1abcd400800120016002000SE +/- 0.61, N = 31933.371932.471933.581934.32

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 16abcd1224364860SE +/- 0.07, N = 353.2053.4752.9953.22

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 32abcd1224364860SE +/- 0.09, N = 353.1052.9153.1153.27

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 64abcd1224364860SE +/- 0.12, N = 352.4452.4051.7251.97

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 96abcd1224364860SE +/- 0.07, N = 351.8351.7251.9052.00

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 16abcd306090120150SE +/- 0.30, N = 3113.31111.63113.63113.36

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 32abcd306090120150SE +/- 0.81, N = 3117.00117.81116.93117.89

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 64abcd306090120150SE +/- 0.54, N = 3118.48118.93117.61119.83

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 96abcd306090120150SE +/- 0.42, N = 3118.25119.10119.82118.23

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 16abcd2004006008001000SE +/- 0.39, N = 3932.19929.58933.76931.22

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 32abcd2004006008001000SE +/- 1.14, N = 3981.32984.41982.66981.61

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 64abcd2004006008001000SE +/- 0.55, N = 3998.43997.73999.93999.92

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 96abcd2004006008001000SE +/- 1.26, N = 3990.35986.72988.15988.72

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 16abcd400800120016002000SE +/- 14.27, N = 32003.492002.091989.001984.38

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 32abcd5001000150020002500SE +/- 19.33, N = 72056.182110.192037.862033.57

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 64abcd5001000150020002500SE +/- 10.67, N = 32112.332120.772063.782091.66

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 96abcd400800120016002000SE +/- 2.02, N = 32083.552081.872087.392071.18

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 256abcd1224364860SE +/- 0.02, N = 351.9252.0752.0652.04

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 512abcd1224364860SE +/- 0.05, N = 351.7651.8751.7751.76

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_fp32_pretrained_model - Batch Size: 960abcd1224364860SE +/- 0.11, N = 351.7651.7851.5951.62

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 256abcd306090120150SE +/- 0.27, N = 3119.18119.18119.33119.45

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 512abcd306090120150SE +/- 0.14, N = 3119.90119.61119.91120.56

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: inceptionv4_int8_pretrained_model - Batch Size: 960abcd306090120150SE +/- 0.16, N = 3120.74120.94120.74120.57

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 256abcd2004006008001000SE +/- 0.11, N = 31001.611000.281001.431001.30

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 512abcd2004006008001000SE +/- 0.75, N = 3976.58974.84976.22974.69

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_fp32_pretrained_model - Batch Size: 960abcd2004006008001000SE +/- 0.13, N = 3983.76982.71982.46984.36

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 256abcd5001000150020002500SE +/- 14.60, N = 32090.972106.542028.822091.60

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 512abcd5001000150020002500SE +/- 2.76, N = 32170.092179.092161.982171.79

OpenBenchmarking.orgimages/sec, More Is BetterIntel TensorFlow 2.12Model: mobilenetv1_int8_pretrained_model - Batch Size: 960abcd5001000150020002500SE +/- 2.58, N = 32133.182128.102137.202132.29

InfluxDB

This is a benchmark of the InfluxDB open-source time-series database optimized for fast, high-availability storage for IoT and other use-cases. The InfluxDB test profile makes use of InfluxDB Inch for facilitating the benchmarks. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgval/sec, More Is BetterInfluxDB 1.8.2Concurrent Streams: 4 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000abcd300K600K900K1200K1500KSE +/- 5338.36, N = 31547894.41545780.81552035.61560758.0

OpenBenchmarking.orgval/sec, More Is BetterInfluxDB 1.8.2Concurrent Streams: 64 - Batch Size: 10000 - Tags: 2,5000,1 - Points Per Series: 10000abcd300K600K900K1200K1500KSE +/- 3918.66, N = 31602099.91593776.41599391.91600346.9

68 Results Shown

SQLite:
  2
  4
  8
  16
  32
QuantLib
SVT-AV1:
  Preset 4 - Bosphorus 4K
  Preset 8 - Bosphorus 4K
  Preset 12 - Bosphorus 4K
  Preset 13 - Bosphorus 4K
  Preset 4 - Bosphorus 1080p
  Preset 8 - Bosphorus 1080p
  Preset 12 - Bosphorus 1080p
  Preset 13 - Bosphorus 1080p
Intel TensorFlow:
  resnet50_fp32_pretrained_model - 1:
    images/sec
    ms
  resnet50_int8_pretrained_model - 1:
    images/sec
    ms
  resnet50_fp32_pretrained_model - 16:
    images/sec
  resnet50_fp32_pretrained_model - 32:
    images/sec
  resnet50_fp32_pretrained_model - 64:
    images/sec
  resnet50_fp32_pretrained_model - 96:
    images/sec
  resnet50_int8_pretrained_model - 16:
    images/sec
  resnet50_int8_pretrained_model - 32:
    images/sec
  resnet50_int8_pretrained_model - 64:
    images/sec
  resnet50_int8_pretrained_model - 96:
    images/sec
  resnet50_fp32_pretrained_model - 256:
    images/sec
  resnet50_fp32_pretrained_model - 512:
    images/sec
  resnet50_fp32_pretrained_model - 960:
    images/sec
  resnet50_int8_pretrained_model - 256:
    images/sec
  resnet50_int8_pretrained_model - 512:
    images/sec
  resnet50_int8_pretrained_model - 960:
    images/sec
  inceptionv4_fp32_pretrained_model - 1:
    images/sec
    ms
  inceptionv4_int8_pretrained_model - 1:
    images/sec
    ms
  mobilenetv1_fp32_pretrained_model - 1:
    images/sec
  mobilenetv1_int8_pretrained_model - 1:
    images/sec
  inceptionv4_fp32_pretrained_model - 16:
    images/sec
  inceptionv4_fp32_pretrained_model - 32:
    images/sec
  inceptionv4_fp32_pretrained_model - 64:
    images/sec
  inceptionv4_fp32_pretrained_model - 96:
    images/sec
  inceptionv4_int8_pretrained_model - 16:
    images/sec
  inceptionv4_int8_pretrained_model - 32:
    images/sec
  inceptionv4_int8_pretrained_model - 64:
    images/sec
  inceptionv4_int8_pretrained_model - 96:
    images/sec
  mobilenetv1_fp32_pretrained_model - 16:
    images/sec
  mobilenetv1_fp32_pretrained_model - 32:
    images/sec
  mobilenetv1_fp32_pretrained_model - 64:
    images/sec
  mobilenetv1_fp32_pretrained_model - 96:
    images/sec
  mobilenetv1_int8_pretrained_model - 16:
    images/sec
  mobilenetv1_int8_pretrained_model - 32:
    images/sec
  mobilenetv1_int8_pretrained_model - 64:
    images/sec
  mobilenetv1_int8_pretrained_model - 96:
    images/sec
  inceptionv4_fp32_pretrained_model - 256:
    images/sec
  inceptionv4_fp32_pretrained_model - 512:
    images/sec
  inceptionv4_fp32_pretrained_model - 960:
    images/sec
  inceptionv4_int8_pretrained_model - 256:
    images/sec
  inceptionv4_int8_pretrained_model - 512:
    images/sec
  inceptionv4_int8_pretrained_model - 960:
    images/sec
  mobilenetv1_fp32_pretrained_model - 256:
    images/sec
  mobilenetv1_fp32_pretrained_model - 512:
    images/sec
  mobilenetv1_fp32_pretrained_model - 960:
    images/sec
  mobilenetv1_int8_pretrained_model - 256:
    images/sec
  mobilenetv1_int8_pretrained_model - 512:
    images/sec
  mobilenetv1_int8_pretrained_model - 960:
    images/sec
InfluxDB:
  4 - 10000 - 2,5000,1 - 10000
  64 - 10000 - 2,5000,1 - 10000