AMD EPYC 7F32 8-Core testing with a Supermicro H11DSi-NT v2.00 (2.1 BIOS) and llvmpipe 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 2012274-HA-EPYC7F32L08
EPYC 7F32 Last
AMD EPYC 7F32 8-Core testing with a Supermicro H11DSi-NT v2.00 (2.1 BIOS) and llvmpipe on Ubuntu 20.04 via the Phoronix Test Suite.
,,"Run 1","Run 2","Run 3","Run 4"
Processor,,AMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads),AMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads),AMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads),AMD EPYC 7F32 8-Core @ 3.70GHz (8 Cores / 16 Threads)
Motherboard,,Supermicro H11DSi-NT v2.00 (2.1 BIOS),Supermicro H11DSi-NT v2.00 (2.1 BIOS),Supermicro H11DSi-NT v2.00 (2.1 BIOS),Supermicro H11DSi-NT v2.00 (2.1 BIOS)
Chipset,,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse
Memory,,64GB,64GB,64GB,64GB
Disk,,280GB INTEL SSDPE21D280GA,280GB INTEL SSDPE21D280GA,280GB INTEL SSDPE21D280GA,280GB INTEL SSDPE21D280GA
Graphics,,llvmpipe,llvmpipe,llvmpipe,llvmpipe
Monitor,,VE228,VE228,VE228,VE228
OS,,Ubuntu 20.04,Ubuntu 20.04,Ubuntu 20.04,Ubuntu 20.04
Kernel,,5.8.0-050800rc6daily20200721-generic (x86_64) 20200720,5.8.0-050800rc6daily20200721-generic (x86_64) 20200720,5.8.0-050800rc6daily20200721-generic (x86_64) 20200720,5.8.0-050800rc6daily20200721-generic (x86_64) 20200720
Desktop,,GNOME Shell 3.36.1,GNOME Shell 3.36.1,GNOME Shell 3.36.1,GNOME Shell 3.36.1
Display Server,,X Server 1.20.8,X Server 1.20.8,X Server 1.20.8,X Server 1.20.8
Display Driver,,modesetting 1.20.8,modesetting 1.20.8,modesetting 1.20.8,modesetting 1.20.8
OpenGL,,3.3 Mesa 20.0.4 (LLVM 9.0.1 128 bits),3.3 Mesa 20.0.4 (LLVM 9.0.1 128 bits),3.3 Mesa 20.0.4 (LLVM 9.0.1 128 bits),3.3 Mesa 20.0.4 (LLVM 9.0.1 128 bits)
Compiler,,GCC 9.3.0,GCC 9.3.0,GCC 9.3.0,GCC 9.3.0
File-System,,ext4,ext4,ext4,ext4
Screen Resolution,,1920x1080,1920x1080,1920x1080,1920x1080
,,"Run 1","Run 2","Run 3","Run 4"
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,3604.40,3436.43,3431.50,3417.31
"Build2 - Time To Compile (sec)",LIB,112.078,112.778,112.673,112.457
"Timed Eigen Compilation - Time To Compile (sec)",LIB,82.883,82.951,83.069,82.994
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3417.91,3441.93,3441.20,3421.95
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3429.54,3438.99,3436.78,3427.26
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1711.39,1718.14,1716.53,1711.28
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1704.97,1715.65,1712.85,1714.22
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,1708.29,1708.22,1711.14,1711.87
"NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,32.07,32.19,32.35,32.03
"NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,24.86,24.48,24.46,25.31
"NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,27.06,26.71,26.40,27.10
"NCNN - Target: CPU - Model: resnet50 (ms)",LIB,23.02,22.96,22.89,23.04
"NCNN - Target: CPU - Model: alexnet (ms)",LIB,7.58,7.60,7.60,7.59
"NCNN - Target: CPU - Model: resnet18 (ms)",LIB,11.52,11.57,11.48,11.57
"NCNN - Target: CPU - Model: vgg16 (ms)",LIB,32.45,32.41,32.32,32.31
"NCNN - Target: CPU - Model: googlenet (ms)",LIB,15.65,15.52,15.48,16.38
"NCNN - Target: CPU - Model: blazeface (ms)",LIB,3.31,3.34,3.3,3.31
"NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,10.58,10.55,10.46,10.48
"NCNN - Target: CPU - Model: mnasnet (ms)",LIB,6.14,5.94,5.97,6.02
"NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,9.61,9.63,9.60,9.60
"NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,6.66,6.64,6.62,6.60
"NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,6.87,6.84,6.85,6.83
"NCNN - Target: CPU - Model: mobilenet (ms)",LIB,19.94,19.81,19.89,19.88
"CLOMP - Static OMP Speedup (Speedup)",HIB,29.8,29.6,29.0,29.7
"Unpacking Firefox - Extracting: firefox-84.0.source.tar.xz (sec)",LIB,20.372,20.273,20.221,20.210
"WavPack Audio Encoding - WAV To WavPack (sec)",LIB,13.731,13.731,13.730,13.732
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,7.20000,7.21888,7.22277,7.22008
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,4.94409,4.96823,4.95793,4.93690
"Monkey Audio Encoding - WAV To APE (sec)",LIB,12.507,12.512,12.489,12.493
"Ogg Audio Encoding - WAV To Ogg (sec)",LIB,20.574,20.574,20.583,20.555
"oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,3.54939,3.55093,3.55967,3.55938
"oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.79140,2.78976,2.79003,2.79287
"Opus Codec Encoding - WAV To Opus Encode (sec)",LIB,7.972,7.973,7.969,7.966
"oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,1.16439,1.15868,1.15947,1.16157
"oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.59741,3.59581,3.59103,3.59799
"oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,6.22116,6.15155,6.12560,6.17937
"oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.788963,0.795590,0.797020,0.788043
"Unpacking The Linux Kernel - linux-4.15.tar.xz (sec)",LIB,5.974,5.972,6.016,5.852
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,9.66757,9.62355,9.46619,9.59529
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,4.98064,4.92337,4.95449,4.92898
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,5.58298,5.57986,5.57860,5.58763
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,6.42420,6.42196,6.42311,6.41776