3900X NAMD + TensorFlow Lite

AMD Ryzen 9 3900X 12-Core testing with a ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS) and MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB 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 2009025-PTS-3900XNAM94
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September 02 2020
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September 02 2020
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September 02 2020
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3900X NAMD + TensorFlow Lite - Phoronix Test Suite

3900X NAMD + TensorFlow Lite

AMD Ryzen 9 3900X 12-Core testing with a ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS) and MSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB on Ubuntu 20.04 via the Phoronix Test Suite.

HTML result view exported from: https://openbenchmarking.org/result/2009025-PTS-3900XNAM94&grw.

3900X NAMD + TensorFlow LiteProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanCompilerFile-SystemScreen ResolutionRun 1Run 2Run 3AMD Ryzen 9 3900X 12-Core @ 3.80GHz (12 Cores / 24 Threads)ASUS TUF GAMING X570-PLUS (WI-FI) (2203 BIOS)AMD Starship/Matisse16GBSamsung SSD 970 EVO Plus 250GBMSI AMD Radeon RX 470/480/570/570X/580/580X/590 8GB (1366/2000MHz)AMD Ellesmere HDMI AudioG237HLRealtek RTL8111/8168/8411 + Intel-AC 9260Ubuntu 20.045.8.0-050800daily20200626-generic (x86_64)GNOME Shell 3.36.3X Server 1.20.8modesetting 1.20.84.6 Mesa 20.2.0-devel (git-64cdc13 2020-07-02 focal-oibaf-ppa) (LLVM 10.0.0)1.2.131GCC 9.3.0ext41920x1080OpenBenchmarking.orgProcessor Details- Scaling Governor: acpi-cpufreq ondemand - CPU Microcode: 0x8701021Security Details- 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

3900X NAMD + TensorFlow Litetensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2namd: ATPase Simulation - 327,506 AtomsRun 1Run 2Run 3139071201363314149394746.897075.918098671.55718139099201627714227094808.197138.118133771.55880139434201592014250394831.997115.718124801.55756OpenBenchmarking.org

TensorFlow Lite

Model: SqueezeNet

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetRun 1Run 2Run 330K60K90K120K150KSE +/- 98.00, N = 3SE +/- 54.67, N = 3SE +/- 143.24, N = 3139071139099139434

TensorFlow Lite

Model: Inception V4

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4Run 1Run 2Run 3400K800K1200K1600K2000KSE +/- 679.71, N = 3SE +/- 982.21, N = 3SE +/- 1110.99, N = 3201363320162772015920

TensorFlow Lite

Model: NASNet Mobile

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileRun 1Run 2Run 330K60K90K120K150KSE +/- 726.28, N = 3SE +/- 983.14, N = 3SE +/- 1263.95, N = 3141493142270142503

TensorFlow Lite

Model: Mobilenet Float

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatRun 1Run 2Run 320K40K60K80K100KSE +/- 43.09, N = 3SE +/- 122.03, N = 3SE +/- 127.35, N = 394746.894808.194831.9

TensorFlow Lite

Model: Mobilenet Quant

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantRun 1Run 2Run 320K40K60K80K100KSE +/- 51.15, N = 3SE +/- 58.50, N = 3SE +/- 86.72, N = 397075.997138.197115.7

TensorFlow Lite

Model: Inception ResNet V2

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2Run 1Run 2Run 3400K800K1200K1600K2000KSE +/- 1167.51, N = 3SE +/- 583.16, N = 3SE +/- 489.93, N = 3180986718133771812480

NAMD

ATPase Simulation - 327,506 Atoms

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsRun 1Run 2Run 30.35070.70141.05211.40281.7535SE +/- 0.00371, N = 3SE +/- 0.00152, N = 3SE +/- 0.00130, N = 31.557181.558801.55756


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