AMD EPYC 7601 32-Core testing with a TYAN B8026T70AE24HR (V1.02.B10 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 2012222-HA-AMDEPYC7628
AMD EPYC 7601 Xmas 2020
AMD EPYC 7601 32-Core testing with a TYAN B8026T70AE24HR (V1.02.B10 BIOS) and llvmpipe on Ubuntu 20.04 via the Phoronix Test Suite.
,,"Run 1","Run 2","Run 3"
Processor,,AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads),AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads),AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads)
Motherboard,,TYAN B8026T70AE24HR (V1.02.B10 BIOS),TYAN B8026T70AE24HR (V1.02.B10 BIOS),TYAN B8026T70AE24HR (V1.02.B10 BIOS)
Chipset,,AMD 17h,AMD 17h,AMD 17h
Memory,,126GB,126GB,126GB
Disk,,280GB INTEL SSDPE21D280GA,280GB INTEL SSDPE21D280GA,280GB INTEL SSDPE21D280GA
Graphics,,llvmpipe,llvmpipe,llvmpipe
Monitor,,VE228,VE228,VE228
Network,,2 x Broadcom NetXtreme BCM5720 2-port PCIe,2 x Broadcom NetXtreme BCM5720 2-port PCIe,2 x Broadcom NetXtreme BCM5720 2-port PCIe
OS,,Ubuntu 20.04,Ubuntu 20.04,Ubuntu 20.04
Kernel,,5.4.0-53-generic (x86_64),5.4.0-53-generic (x86_64),5.4.0-53-generic (x86_64)
Desktop,,GNOME Shell 3.36.4,GNOME Shell 3.36.4,GNOME Shell 3.36.4
Display Server,,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
OpenGL,,3.3 Mesa 20.0.8 (LLVM 10.0.0 128 bits),3.3 Mesa 20.0.8 (LLVM 10.0.0 128 bits),3.3 Mesa 20.0.8 (LLVM 10.0.0 128 bits)
Compiler,,GCC 9.3.0,GCC 9.3.0,GCC 9.3.0
File-System,,ext4,ext4,ext4
Screen Resolution,,1920x1080,1920x1080,1920x1080
,,"Run 1","Run 2","Run 3"
"Build2 - Time To Compile (sec)",LIB,102.295,102.588,102.354
"CLOMP - Static OMP Speedup (Speedup)",HIB,57.1,57.7,57.8
"Coremark - CoreMark Size 666 - Iterations Per Second (Iterations/Sec)",HIB,879248.022638,879237.122078,876909.950001
"Monkey Audio Encoding - WAV To APE (sec)",LIB,18.346,18.332,18.416
"NCNN - Target: CPU - Model: mobilenet (ms)",LIB,43.10,41.84,43.26
"NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,17.42,19.42,18.22
"NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,16.30,17.48,16.91
"NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,17.51,17.35,16.94
"NCNN - Target: CPU - Model: mnasnet (ms)",LIB,16.17,15.76,16.26
"NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,22.19,22.24,23.22
"NCNN - Target: CPU - Model: blazeface (ms)",LIB,7.79,7.89,7.90
"NCNN - Target: CPU - Model: googlenet (ms)",LIB,48.06,46.99,49.81
"NCNN - Target: CPU - Model: vgg16 (ms)",LIB,100.72,94.33,88.55
"NCNN - Target: CPU - Model: resnet18 (ms)",LIB,41.83,45.70,43.64
"NCNN - Target: CPU - Model: alexnet (ms)",LIB,33.20,30.19,31.92
"NCNN - Target: CPU - Model: resnet50 (ms)",LIB,60.78,59.24,59.49
"NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,57.99,55.80,56.52
"NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,46.68,46.89,44.81
"NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,117.02,119.23,118.48
"Node.js V8 Web Tooling Benchmark - (runs/s)",HIB,6.78,6.74,6.85
"oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,5.34771,4.49148,4.33519
"oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,12.4177,11.7699,12.0856
"oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,2.67937,2.68511,2.66509
"oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.56511,3.55970,3.57153
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,18.5128,18.6800,18.6556
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,4.03281,4.00713,4.01827
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,9.04439,9.03893,9.08767
"oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,23.3030,22.4576,23.2120
"oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,4.60248,4.71473,4.22841
"oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,4.41314,4.37097,4.40049
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,10732.73,10747.5,10314.22
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,3293.49,3322.68,3393.82
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,10583.10,10647.60,10812.54
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3300.07,3434.14,3327.91
"oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU (ms)",LIB,1.71220,1.74056,1.66512
"oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,10689.16,10915.65,11077.9
"oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3332.79,3312.30,3405.82
"oneDNN - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1.78892,1.77953,1.79366
"Opus Codec Encoding - WAV To Opus Encode (sec)",LIB,10.187,10.215,10.195
"simdjson - Throughput Test: Kostya (GB/s)",HIB,0.33,0.33,0.33
"simdjson - Throughput Test: LargeRandom (GB/s)",HIB,0.28,0.28,0.28
"simdjson - Throughput Test: PartialTweets (GB/s)",HIB,0.36,0.36,0.36
"simdjson - Throughput Test: DistinctUserID (GB/s)",HIB,0.37,0.37,0.37
"SQLite Speedtest - Timed Time - Size 1,000 (sec)",LIB,90.116,90.320,90.107
"Timed Eigen Compilation - Time To Compile (sec)",LIB,120.016,119.981,120.191
"Timed FFmpeg Compilation - Time To Compile (sec)",LIB,39.094,39.108,39.189
"Timed HMMer Search - Pfam Database Search (sec)",LIB,200.295,199.708,200.753
"Timed MAFFT Alignment - Multiple Sequence Alignment - LSU RNA (sec)",LIB,15.018,15.147,15.023
"WavPack Audio Encoding - WAV To WavPack (sec)",LIB,17.319,17.312,17.292