epyc milan x xmas

Tests for a future article. 2 x AMD EPYC 7773X 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED 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 2212247-NE-EPYCMILAN08
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a
December 24 2022
  1 Hour, 35 Minutes
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December 24 2022
  1 Hour, 35 Minutes
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epyc milan x xmas Tests for a future article. 2 x AMD EPYC 7773X 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 20.04 via the Phoronix Test Suite. ,,"a","b" Processor,,2 x AMD EPYC 7773X 64-Core @ 2.20GHz (128 Cores / 256 Threads),2 x AMD EPYC 7773X 64-Core @ 2.20GHz (128 Cores / 256 Threads) Motherboard,,AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse Memory,,512GB,512GB Disk,,800GB INTEL SSDPF21Q800GB,800GB INTEL SSDPF21Q800GB Graphics,,ASPEED,ASPEED Monitor,,VE228,VE228 Network,,2 x Mellanox MT27710,2 x Mellanox MT27710 OS,,Ubuntu 20.04,Ubuntu 20.04 Kernel,,6.1.0-rc8-phx (x86_64),6.1.0-rc8-phx (x86_64) Display Server,,X Server,X Server Vulkan,,1.1.182,1.1.182 Compiler,,GCC 9.4.0,GCC 9.4.0 File-System,,ext4,ext4 Screen Resolution,,1920x1080,1920x1080 ,,"a","b" "CockroachDB - Workload: MoVR - Concurrency: 128 (ops/s)",HIB,767,763 "CockroachDB - Workload: MoVR - Concurrency: 256 (ops/s)",HIB,764.1,758.9 "CockroachDB - Workload: MoVR - Concurrency: 512 (ops/s)",HIB,772.7,765.3 "CockroachDB - Workload: MoVR - Concurrency: 1024 (ops/s)",HIB,765.6,768.3 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 128 (ops/s)",HIB,49496,51045.1 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 256 (ops/s)",HIB,51117.2,49348.6 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 512 (ops/s)",HIB,49998.2,51201 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 128 (ops/s)",HIB,64904.1,67808.6 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 256 (ops/s)",HIB,68275.7,69421.4 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 512 (ops/s)",HIB,69331.1,69785 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 128 (ops/s)",HIB,70821.7,71858.8 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 256 (ops/s)",HIB,74632.2,73457.7 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 512 (ops/s)",HIB,72451.2,73171.5 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 128 (ops/s)",HIB,87221.5,75051.9 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 256 (ops/s)",HIB,88931.9,90275.6 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 512 (ops/s)",HIB,72081.8,89654.1 "CockroachDB - Workload: KV, 10% Reads - Concurrency: 1024 (ops/s)",HIB,47190.8,49337.7 "CockroachDB - Workload: KV, 50% Reads - Concurrency: 1024 (ops/s)",HIB,64847.1,59441.5 "CockroachDB - Workload: KV, 60% Reads - Concurrency: 1024 (ops/s)",HIB,70580.2,70896.9 "CockroachDB - Workload: KV, 95% Reads - Concurrency: 1024 (ops/s)",HIB,88959.2,88708.4 "Numenta Anomaly Benchmark - Detector: KNN CAD (sec)",LIB,88.693,89.535 "Numenta Anomaly Benchmark - Detector: Windowed Gaussian (sec)",LIB,5.491,5.493 "Numenta Anomaly Benchmark - Detector: Earthgecko Skyline (sec)",LIB,73.627,73.312 "Numenta Anomaly Benchmark - Detector: Contextual Anomaly Detector OSE (sec)",LIB,44.555,44.294 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,2.29566,2.24618 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,1.8373,1.83852 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.5784,1.57114 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.967326,0.937745 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,0.589938,0.576758 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,27.4833,27.2422 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,3.4928,3.54446 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.583512,0.746936 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.03703,1.04002 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.717443,0.696358 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,1579.22,1700.59 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,1473.67,1176.84 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1685.58,1680.1 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1372.4,1471.68 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,2.68287,2.66346 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1920.66,1863.81 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1448.33,1298.93 "oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.56092,2.63047 "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,22.35,21.95 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,1414.79,1448.08 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,15.55,15.43 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,2028.3,2051.22 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,15.46,15.51 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,2047.78,2040.99 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,2329.23,2337.11 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,13.72,13.68 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,55.77,55.87 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,571.16,570.36 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,3755.03,3756.88 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,8.51,8.51 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,2442.86,2442.72 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,13.09,13.09 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,278.21,276.86 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,114.89,115.44 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,5753.13,5750.27 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,22.23,22.24 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,3231.85,3229.45 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,9.89,9.9 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,76694.48,77006.49 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,1.64,1.64 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,83051.23,82943.37 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,1.52,1.52 "OpenVKL - Benchmark: vklBenchmark ISPC (Items / Sec)",HIB,622,664 "OpenVKL - Benchmark: vklBenchmark Scalar (Items / Sec)",HIB,456,440 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,24.026,23.955 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,177.977,177.397