7713 2P

Tests for a future article. 2 x AMD EPYC 7303 16-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite.

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  Test
  Duration
a
October 03 2023
  50 Minutes
b
October 03 2023
  50 Minutes
c
October 03 2023
  50 Minutes
AMD EPYC 7303 16-Core
October 08 2023
  1 Hour, 37 Minutes
d
October 18 2023
  2 Hours, 29 Minutes
e
October 23 2023
  1 Hour, 53 Minutes
f
October 23 2023
  1 Hour, 53 Minutes
g
October 23 2023
  1 Hour, 53 Minutes
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  1 Hour, 32 Minutes

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7713 2P Tests for a future article. 2 x AMD EPYC 7303 16-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite. ,,"a","b","c","AMD EPYC 7303 16-Core","d","e","f","g" Processor,,2 x AMD EPYC 7713 64-Core @ 2.00GHz (128 Cores / 256 Threads),2 x AMD EPYC 7713 64-Core @ 2.00GHz (128 Cores / 256 Threads),2 x AMD EPYC 7713 64-Core @ 2.00GHz (128 Cores / 256 Threads),AMD EPYC 7303 16-Core @ 2.40GHz (16 Cores / 32 Threads),2 x AMD EPYC 7203 8-Core @ 2.80GHz (16 Cores / 32 Threads),2 x AMD EPYC 7303 16-Core @ 2.40GHz (32 Cores / 64 Threads),2 x AMD EPYC 7303 16-Core @ 2.40GHz (32 Cores / 64 Threads),2 x AMD EPYC 7303 16-Core @ 2.40GHz (32 Cores / 64 Threads) Motherboard,,AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS),AMD DAYTONA_X (RYM1009B BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse Memory,,256GB,256GB,256GB,256GB,512GB,512GB,512GB,512GB Disk,,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP,3841GB Micron_9300_MTFDHAL3T8TDP Graphics,,ASPEED,ASPEED,ASPEED,ASPEED,ASPEED,ASPEED,ASPEED,ASPEED Monitor,,VE228,VE228,VE228,VE228,,,, Network,,2 x Mellanox MT27710,2 x Mellanox MT27710,2 x Mellanox MT27710,2 x Mellanox MT27710,2 x Mellanox MT27710,2 x Mellanox MT27710,2 x Mellanox MT27710,2 x Mellanox MT27710 OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04 Kernel,,5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64),5.15.0-47-generic (x86_64) Desktop,,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4,GNOME Shell 42.4 Display Server,,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3,X Server 1.21.1.3 Vulkan,,1.2.204,1.2.204,1.2.204,1.2.204,1.2.204,1.2.204,1.2.204,1.2.204 Compiler,,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0,GCC 11.2.0 File-System,,ext4,ext4,ext4,ext4,ext4,ext4,ext4,ext4 Screen Resolution,,1920x1080,1920x1080,1920x1080,1920x1080,1024x768,1024x768,1024x768,1024x768 ,,"a","b","c","AMD EPYC 7303 16-Core","d","e","f","g" "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,9543.23,9544.95,9527.76,1754.67,1232.95,3153.9,3148.82,3148.97 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,2687.19,2655.09,2694.27,520.89,369.7,873.34,865.28,875.64 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,3308.28,3302.83,3303.59,570.58,470.46,1096.25,1095.27,1095.68 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,1342.68,1340.29,1340.12,237.79,195.5,415.32,415.12,415.15 "Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,153.78,152.94,152.5,871.17,974.94,505.43,504.18,506.55 "7-Zip Compression - Test: Decompression Rating (MIPS)",HIB,643823,647995,678765,116102,106826,214511,216258,218417 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,5333.24,5334.87,5335.15,858.21,856.63,1727.34,1708.45,1725.37 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,51.12,51.17,51.2,8.66,8.35,17.2,17.27,17.28 "Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,49.66,49.32,48.96,280.03,296.74,155.52,157.08,157.59 "Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,40.28,40.21,40.04,226.06,237.87,116.68,117.05,118.34 "Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,16.08,16.08,16.19,89.45,94.86,50.21,50.25,50.09 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,899.15,897.77,898.69,155.5,171.61,303.8,303.83,303.59 "Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,20.44,20.73,20.45,109.21,116.66,62.52,62.18,62.31 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,202.68,203.14,203.96,42.11,37.06,72.71,73.23,72.14 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,226.15,226.27,222.73,47.46,43.18,85.51,85.46,84.86 "Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,182.491,179.986,179.539,815.395,830.492,462.138,462.599,462.696 "7-Zip Compression - Test: Compression Rating (MIPS)",HIB,507611,505036,499347,135892,113384,210620,212139,209887 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,98494.41,101883.13,101985.21,23110.2,23725.57,46823.73,45126.45,47926.34 "Remhos - Test: Sample Remap Example (sec)",LIB,11.284,11.02,11.709,37.307,39.71,23.463,23.488,23.723 "OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,132.37,129.61,132.8,357.27,429.22,302.03,305.55,307.24 "OpenRadioss - Model: Chrysler Neon 1M (sec)",LIB,174.49,173.59,174.45,551.24,489.75,304.03,306.37,303.58 "Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,23.875,23.974,23.939,67.408,68.675,43.41,43.477,43.318 "OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,26.54,26.35,26.53,65.55,63.71,41.05,41.01,40.85 "libxsmm - M N K: 64 (GFLOPS/s)",HIB,980.5,923.9,920.8,406.9,442.5,727.2,737.3,723.4 "Laghos - Test: Sedov Blast Wave, ube_922_hex.mesh (Major Kernels Rate)",HIB,282.55,282.08,279.20,148.49,132.46,234.135345612,233.54,233.047063651 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,623.9,623.64,623.6,921.14,478.92,463.57,462.78,462.57 "libxsmm - M N K: 32 (GFLOPS/s)",HIB,540.3,471,467.3,273.2,356.5,484.7,482.9,470.9 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,9.66,9.68,9.67,14,8.49,7.29,7.29,7.29 "OpenVKL - Benchmark: vklBenchmarkCPU Scalar (Items / Sec)",HIB,,,,,144,273,275,274 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,,,,,15.8702,29.9805,30.1314,30.0261 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,,,,,13.8445,26.0698,26.2737,26.1574 "Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,,,,,17.4784,32.9696,33.0392,32.9662 "Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,,,,,16.1253,30.1028,30.3378,30.2552 "Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,,,,,15.7439,29.6195,29.5206,29.5908 "OpenVKL - Benchmark: vklBenchmarkCPU ISPC (Items / Sec)",HIB,,,,,267,495,497,495 "Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,,,,,14.3991,26.5233,26.6112,26.3986 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,141.33,141.24,143.55,168.3,92.53,93.45,93.5,94.17 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,3.34,3.34,3.35,4.55,3.24,2.53,2.53,2.53 "libavif avifenc - Encoder Speed: 6 (sec)",LIB,3.248,3.331,3.309,5.282,5.771,3.965,3.976,3.962 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,157.75,157.4,156.74,189.92,107.81,109.89,109.16,110.71 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,23.81,23.85,23.86,33.62,20.44,19.25,19.25,19.25 "Intel Open Image Denoise - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only (Images / Sec)",HIB,,,,,0.28,0.48,0.48,0.48 "Intel Open Image Denoise - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,,,,,0.56,0.95,0.95,0.95 "Intel Open Image Denoise - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only (Images / Sec)",HIB,,,,,0.56,0.95,0.95,0.95 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,11.89,12.03,11.86,15.34,10.8,9.13,9.21,9.11 "libavif avifenc - Encoder Speed: 0 (sec)",LIB,75.603,75.778,75.836,120.85,125.918,92.447,91.913,92.528 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,1.92811,1.16856,1.16545,1.17023 "OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,147.64,146.17,146.31,202.93,235.98,192.24,192.62,192.49 "Laghos - Test: Triple Point Problem (Major Kernels Rate)",HIB,164.19,162.65,167.28,132.65,114.18,182.23,181.55,180.93 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,1.6509,1.44749,1.92348,2.28469 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,2.67723,1.74244,1.69817,1.84652 "libavif avifenc - Encoder Speed: 2 (sec)",LIB,41.424,40.837,42.41,60.19,64.146,48.724,48.844,48.731 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,142.22,142.42,142.29,102.82,93.16,105.22,105.23,105.33 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,4.63185,3.14371,3.06111,3.14105 "libavif avifenc - Encoder Speed: 6, Lossless (sec)",LIB,6.786,6.881,6.929,9.186,10.133,7.801,7.824,7.825 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,0.768449,1.05324,1.14379,1.08661 "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,2.34565,1.89405,1.77064,2.1207 "OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,130.4,134.61,131.25,102.8,129.36,118.07,120.33,122.81 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,0.85,0.85,0.86,0.68,0.66,0.67,0.7,0.66 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,1451.2,1114.67,1139.54,1129.54 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,1466.25,1137.11,1128.3,1149.15 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,23.98,23.98,23.97,18.63,18.65,18.46,18.67,18.48 "OpenRadioss - Model: Bumper Beam (sec)",LIB,115.89,116.73,116.3,118.33,134.32,105.44,105.68,107.04 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,1445.86,1161.8,1153.45,1145.62 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 (sec)",LIB,,,,,3.291,2.831,2.672,2.766 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 (sec)",LIB,,,,,65.733,55.336,60.049,56.337 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,4.51333,3.84916,4.18974,3.94733 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,3037.72,3379.25,3281.24,3556.44 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,3019.31,3494.51,3341.93,3451.09 "easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 (sec)",LIB,,,,,149.203,140.297,130.447,133.177 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,3028.36,3216.13,3462.9,3272.36 "libavif avifenc - Encoder Speed: 10, Lossless (sec)",LIB,5.474,5.485,5.535,5.779,6.186,5.855,5.817,5.815 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,4.27338,4.10412,3.83995,4.05768 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,,,,,5.54136,5.67018,5.66435,5.63319 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,,,,,1.94173,1.93352,1.9522,1.95024 "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,,,, "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,,,, "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,,,, "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,,,, "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,,,,,,,