3700X

AMD Ryzen 7 3700X 8-Core testing with a Gigabyte A320M-S2H-CF (F52a BIOS) and HIS AMD Radeon HD 7750/8740 / R7 250E 1GB 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 2009023-FI-3700X498660
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R1
September 02 2020
  32 Minutes
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September 02 2020
  31 Minutes
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September 02 2020
  31 Minutes
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3700XOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 7 3700X 8-Core @ 3.60GHz (8 Cores / 16 Threads)Gigabyte A320M-S2H-CF (F52a BIOS)AMD Starship/Matisse8GB240GB TOSHIBA RC100HIS AMD Radeon HD 7750/8740 / R7 250E 1GBAMD Oland/Hainan/CapeVA2431Realtek RTL8111/8168/8411Ubuntu 20.045.9.0-050900rc1daily20200817-generic (x86_64) 20200816GNOME Shell 3.36.4X Server 1.20.8modesetting 1.20.84.5 Mesa 20.0.8 (LLVM 10.0.0)GCC 9.3.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLCompilerFile-SystemScreen Resolution3700X BenchmarksSystem Logs- Scaling Governor: acpi-cpufreq ondemand - CPU Microcode: 0x8701021- 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 STIBP: conditional RSB filling + srbds: Not affected + tsx_async_abort: Not affected

R1R2R3Result OverviewPhoronix Test Suite100%100%100%101%101%TensorFlow LiteNAMDTensorFlow LiteTensorFlow LiteTensorFlow LiteTensorFlow LiteTensorFlow LiteNASNet MobileATPase Simulation - 327,506 AtomsMobilenet FloatI.R.VSqueezeNetInception V4Mobilenet Quant

3700Xnamd: ATPase Simulation - 327,506 Atomstensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2R1R2R32.26358190706275807316606512823513105124991632.26056190663275602716720312806613096724968372.2571119056227577031666111281281310122496437OpenBenchmarking.org

NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD was developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsR3R2R10.50931.01861.52792.03722.5465SE +/- 0.00810, N = 3SE +/- 0.00600, N = 3SE +/- 0.00629, N = 32.257112.260562.26358
OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsR3R2R1246810Min: 2.24 / Avg: 2.26 / Max: 2.27Min: 2.25 / Avg: 2.26 / Max: 2.27Min: 2.25 / Avg: 2.26 / Max: 2.27

TensorFlow Lite

This is a benchmark of the TensorFlow Lite implementation. The current Linux support is limited to running on CPUs. This test profile is measuring the average inference time. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetR3R2R140K80K120K160K200KSE +/- 138.40, N = 3SE +/- 88.37, N = 3SE +/- 94.23, N = 3190562190663190706
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetR3R2R130K60K90K120K150KMin: 190301 / Avg: 190562.33 / Max: 190772Min: 190513 / Avg: 190663.33 / Max: 190819Min: 190535 / Avg: 190706.33 / Max: 190860

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4R3R2R1600K1200K1800K2400K3000KSE +/- 3741.23, N = 3SE +/- 2468.40, N = 3SE +/- 4122.19, N = 3275770327560272758073
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4R3R2R1500K1000K1500K2000K2500KMin: 2752060 / Avg: 2757703.33 / Max: 2764780Min: 2751290 / Avg: 2756026.67 / Max: 2759600Min: 2749970 / Avg: 2758073.33 / Max: 2763440

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileR3R2R140K80K120K160K200KSE +/- 1258.69, N = 3SE +/- 2104.44, N = 3SE +/- 609.26, N = 3166611167203166065
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileR3R2R130K60K90K120K150KMin: 164402 / Avg: 166611.33 / Max: 168761Min: 164982 / Avg: 167203.33 / Max: 171410Min: 164847 / Avg: 166065 / Max: 166705

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatR3R2R130K60K90K120K150KSE +/- 113.06, N = 3SE +/- 115.94, N = 3SE +/- 57.66, N = 3128128128066128235
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatR3R2R120K40K60K80K100KMin: 127903 / Avg: 128128 / Max: 128260Min: 127844 / Avg: 128066 / Max: 128235Min: 128127 / Avg: 128235 / Max: 128324

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantR3R2R130K60K90K120K150KSE +/- 10.41, N = 3SE +/- 62.41, N = 3SE +/- 59.14, N = 3131012130967131051
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantR3R2R120K40K60K80K100KMin: 130995 / Avg: 131012.33 / Max: 131031Min: 130844 / Avg: 130966.67 / Max: 131048Min: 130934 / Avg: 131051.33 / Max: 131123

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2R3R2R1500K1000K1500K2000K2500KSE +/- 2120.80, N = 3SE +/- 2439.16, N = 3SE +/- 1187.02, N = 3249643724968372499163
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2R3R2R1400K800K1200K1600K2000KMin: 2492320 / Avg: 2496436.67 / Max: 2499380Min: 2492220 / Avg: 2496836.67 / Max: 2500510Min: 2496790 / Avg: 2499163.33 / Max: 2500400