AMD Ryzen 5 3600XT Linux 5.9 Performance

AMD Ryzen 5 3600XT 6-Core testing with a MSI X470 GAMING M7 AC (MS-7B77) v1.0 (1.E0 BIOS) and MSI AMD Radeon R7 370 / R9 270/370 OEM 4GB 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 2008258-FI-AMDRYZEN521

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HPC - High Performance Computing 3 Tests
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Linux 5.8.0
August 25 2020
  1 Hour, 5 Minutes
Linux 5.8.0 Confirmation
August 25 2020
  1 Hour, 5 Minutes
Linux 5.9 Git
August 25 2020
  1 Hour, 4 Minutes
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AMD Ryzen 5 3600XT Linux 5.9 PerformanceOpenBenchmarking.orgPhoronix Test Suite 10.2.0AMD Ryzen 5 3600XT 6-Core @ 3.80GHz (6 Cores / 12 Threads)MSI X470 GAMING M7 AC (MS-7B77) v1.0 (1.E0 BIOS)AMD Starship/Matisse16GBSamsung SSD 960 EVO 500GBMSI AMD Radeon R7 370 / R9 270/370 OEM 4GBAMD Oland/Hainan/CapeG237HLQualcomm Atheros Killer E2500 + Intel 8265 / 8275Ubuntu 20.045.8.0-050800-generic (x86_64)5.9.0-050900rc2daily20200825-generic (x86_64) 20200824GNOME Shell 3.36.4X Server 1.20.8modesetting 1.20.84.5 Mesa 20.0.4 (LLVM 9.0.1)GCC 9.3.0ext41920x1080ProcessorMotherboardChipsetMemoryDiskGraphicsAudioMonitorNetworkOSKernelsDesktopDisplay ServerDisplay DriverOpenGLCompilerFile-SystemScreen ResolutionAMD Ryzen 5 3600XT Linux 5.9 Performance BenchmarksSystem LogsOPC Classification- Scaling Governor: acpi-cpufreq ondemand - CPU Microcode: 0x8701021- Python 3.8.2- 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

Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 GitResult OverviewPhoronix Test Suite 10.2.0100%101%102%103%103%ECP-CANDLEECP-CANDLETensorFlow LiteNAMDTensorFlow LiteTensorFlow LiteECP-CANDLETensorFlow LiteTensorFlow LiteTensorFlow LiteP1B2P3B2SqueezeNetATPase Simulation - 327,506 AtomsInception V4NASNet MobileP3B1Mobilenet QuantMobilenet FloatI.R.V

AMD Ryzen 5 3600XT Linux 5.9 Performancenamd: ATPase Simulation - 327,506 Atomstensorflow-lite: SqueezeNettensorflow-lite: Inception V4tensorflow-lite: NASNet Mobiletensorflow-lite: Mobilenet Floattensorflow-lite: Mobilenet Quanttensorflow-lite: Inception ResNet V2ecp-candle: P1B2ecp-candle: P3B1ecp-candle: P3B2Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git2.705292518853737547215503173209177838341105037.6041159.85622.6682.729412542383764053216850173965178637341831737.51163.622619.3362.725432537753764280216606173927178295341697738.7751157.774614.895OpenBenchmarking.org

NAMD

OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git0.61411.22821.84232.45643.0705SE +/- 0.00938, N = 3SE +/- 0.00609, N = 3SE +/- 0.00911, N = 32.705292.729412.72543
OpenBenchmarking.orgdays/ns, Fewer Is BetterNAMD 2.14ATPase Simulation - 327,506 AtomsLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git246810Min: 2.69 / Avg: 2.71 / Max: 2.72Min: 2.72 / Avg: 2.73 / Max: 2.74Min: 2.71 / Avg: 2.73 / Max: 2.74

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: SqueezeNetLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git50K100K150K200K250KSE +/- 98.10, N = 3SE +/- 118.67, N = 3SE +/- 67.35, N = 3251885254238253775
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: SqueezeNetLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git40K80K120K160K200KMin: 251754 / Avg: 251885 / Max: 252077Min: 254024 / Avg: 254237.67 / Max: 254434Min: 253642 / Avg: 253775 / Max: 253860

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git800K1600K2400K3200K4000KSE +/- 6718.28, N = 3SE +/- 2298.87, N = 3SE +/- 6443.07, N = 3373754737640533764280
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception V4Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git700K1400K2100K2800K3500KMin: 3724910 / Avg: 3737546.67 / Max: 3747820Min: 3759730 / Avg: 3764053.33 / Max: 3767570Min: 3752680 / Avg: 3764280 / Max: 3774940

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git50K100K150K200K250KSE +/- 209.87, N = 3SE +/- 163.95, N = 3215503216850216606
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: NASNet MobileLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git40K80K120K160K200KMin: 216431 / Avg: 216850.33 / Max: 217076Min: 216299 / Avg: 216606.33 / Max: 216859

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git40K80K120K160K200KSE +/- 219.54, N = 3SE +/- 145.75, N = 3SE +/- 36.54, N = 3173209173965173927
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet FloatLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git30K60K90K120K150KMin: 172779 / Avg: 173209.33 / Max: 173500Min: 173682 / Avg: 173965 / Max: 174167Min: 173872 / Avg: 173926.67 / Max: 173996

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git40K80K120K160K200KSE +/- 137.61, N = 3SE +/- 61.73, N = 3SE +/- 241.10, N = 3177838178637178295
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Mobilenet QuantLinux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git30K60K90K120K150KMin: 177563 / Avg: 177837.67 / Max: 177990Min: 178514 / Avg: 178637.33 / Max: 178704Min: 177814 / Avg: 178295 / Max: 178565

OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git700K1400K2100K2800K3500KSE +/- 1861.53, N = 3SE +/- 2702.73, N = 3SE +/- 3163.86, N = 3341105034183173416977
OpenBenchmarking.orgMicroseconds, Fewer Is BetterTensorFlow Lite 2020-08-23Model: Inception ResNet V2Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git600K1200K1800K2400K3000KMin: 3408350 / Avg: 3411050 / Max: 3414620Min: 3413000 / Avg: 3418316.67 / Max: 3421820Min: 3410650 / Avg: 3416976.67 / Max: 3420240

ECP-CANDLE

The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P1B2Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git91827364537.6037.5038.78

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P3B1Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git300600900120015001159.851163.621157.77

OpenBenchmarking.orgSeconds, Fewer Is BetterECP-CANDLE 0.3Benchmark: P3B2Linux 5.8.0Linux 5.8.0 ConfirmationLinux 5.9 Git130260390520650622.67619.34614.90


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