Ryzen 9 3950X Ubuntu

AMD Ryzen 9 3950X 16-Core testing with a ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1302 BIOS) and AMD Radeon RX 5600 OEM/5600 XT / 5700/5700 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 2008235-FI-RYZEN939597
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August 23 2020
  23 Minutes
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August 23 2020
  22 Minutes
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August 23 2020
  22 Minutes
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Ryzen 9 3950X UbuntuOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 9 3950X 16-Core @ 3.50GHz (16 Cores / 32 Threads)ASUS ROG CROSSHAIR VIII HERO (WI-FI) (1302 BIOS)AMD Starship/Matisse16GB2000GB Corsair Force MP600 + 2000GBAMD Radeon RX 5600 OEM/5600 XT / 5700/5700 8GB (2100/875MHz)AMD Navi 10 HDMI AudioDELL P2415QRealtek RTL8125 2.5GbE + Intel I211 + Intel Wi-Fi 6 AX200Ubuntu 20.045.9.0-050900rc1daily20200821-generic (x86_64) 20200820GNOME Shell 3.36.4X Server 1.20.8amdgpu 19.1.04.6 Mesa 20.3.0-devel (git-32dc71b 2020-08-22 focal-oibaf-ppa) (LLVM 10.0.1)1.2.146ext42560x1440ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelDesktopDisplay ServerDisplay DriverOpenGLVulkanFile-SystemScreen ResolutionRyzen 9 3950X Ubuntu BenchmarksSystem Logs- Scaling Governor: acpi-cpufreq performance - CPU Microcode: 0x8701013- 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

Q1Q2Q3Result OverviewPhoronix Test Suite100%100%101%101%101%NAMDTensorFlow LiteTensorFlow LiteTensorFlow LiteTensorFlow LiteTensorFlow LiteTensorFlow LiteATPase Simulation - 327,506 AtomsNASNet MobileInception V4SqueezeNetMobilenet FloatMobilenet QuantI.R.V

Ryzen 9 3950X Ubuntunamd: ATPase Simulation - 327,506 Atomstensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2Q1Q2Q31.13204103957147457312276569314.671704.813302271.11931104117147345312249469370.471660.813300301.11761104080147633712238169362.271699.01329480OpenBenchmarking.org

NAMD

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsQ1Q2Q30.25470.50940.76411.01881.2735SE +/- 0.01930, N = 3SE +/- 0.00763, N = 3SE +/- 0.00661, N = 31.132041.119311.11761
OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsQ1Q2Q3246810Min: 1.09 / Avg: 1.13 / Max: 1.16Min: 1.11 / Avg: 1.12 / Max: 1.13Min: 1.11 / Avg: 1.12 / Max: 1.13

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: SqueezeNetQ1Q2Q320K40K60K80K100KSE +/- 33.91, N = 3SE +/- 76.70, N = 3SE +/- 41.80, N = 3103957104117104080
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetQ1Q2Q320K40K60K80K100KMin: 103890 / Avg: 103957.33 / Max: 103998Min: 103966 / Avg: 104116.67 / Max: 104217Min: 104000 / Avg: 104080 / Max: 104141

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4Q1Q2Q3300K600K900K1200K1500KSE +/- 387.40, N = 3SE +/- 1224.76, N = 3SE +/- 588.14, N = 3147457314734531476337
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4Q1Q2Q3300K600K900K1200K1500KMin: 1473960 / Avg: 1474573.33 / Max: 1475290Min: 1471180 / Avg: 1473453.33 / Max: 1475380Min: 1475170 / Avg: 1476336.67 / Max: 1477050

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileQ1Q2Q330K60K90K120K150KSE +/- 140.59, N = 3SE +/- 145.77, N = 3SE +/- 108.02, N = 3122765122494122381
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileQ1Q2Q320K40K60K80K100KMin: 122522 / Avg: 122764.67 / Max: 123009Min: 122208 / Avg: 122494.33 / Max: 122685Min: 122207 / Avg: 122381.33 / Max: 122579

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatQ1Q2Q315K30K45K60K75KSE +/- 24.50, N = 3SE +/- 48.87, N = 3SE +/- 24.27, N = 369314.669370.469362.2
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatQ1Q2Q312K24K36K48K60KMin: 69282.1 / Avg: 69314.6 / Max: 69362.6Min: 69290.8 / Avg: 69370.37 / Max: 69459.3Min: 69324.3 / Avg: 69362.17 / Max: 69407.4

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantQ1Q2Q315K30K45K60K75KSE +/- 42.17, N = 3SE +/- 23.74, N = 3SE +/- 46.17, N = 371704.871660.871699.0
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantQ1Q2Q312K24K36K48K60KMin: 71620.6 / Avg: 71704.77 / Max: 71751.6Min: 71614 / Avg: 71660.8 / Max: 71691.1Min: 71639.9 / Avg: 71699 / Max: 71790

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2Q1Q2Q3300K600K900K1200K1500KSE +/- 505.18, N = 3SE +/- 1379.60, N = 3SE +/- 696.16, N = 3133022713300301329480
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2Q1Q2Q3200K400K600K800K1000KMin: 1329350 / Avg: 1330226.67 / Max: 1331100Min: 1327560 / Avg: 1330030 / Max: 1332330Min: 1328100 / Avg: 1329480 / Max: 1330330