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:
Processor: AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads), Motherboard: TYAN B8026T70AE24HR (V1.02.B10 BIOS), Chipset: AMD 17h, Memory: 126GB, Disk: 280GB INTEL SSDPE21D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 2-port PCIe
OS: Ubuntu 20.04, Kernel: 5.4.0-53-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0 128 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080
Run 2:
Processor: AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads), Motherboard: TYAN B8026T70AE24HR (V1.02.B10 BIOS), Chipset: AMD 17h, Memory: 126GB, Disk: 280GB INTEL SSDPE21D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 2-port PCIe
OS: Ubuntu 20.04, Kernel: 5.4.0-53-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0 128 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080
Run 3:
Processor: AMD EPYC 7601 32-Core @ 2.20GHz (32 Cores / 64 Threads), Motherboard: TYAN B8026T70AE24HR (V1.02.B10 BIOS), Chipset: AMD 17h, Memory: 126GB, Disk: 280GB INTEL SSDPE21D280GA, Graphics: llvmpipe, Monitor: VE228, Network: 2 x Broadcom NetXtreme BCM5720 2-port PCIe
OS: Ubuntu 20.04, Kernel: 5.4.0-53-generic (x86_64), Desktop: GNOME Shell 3.36.4, Display Server: X Server 1.20.8, Display Driver: modesetting 1.20.8, OpenGL: 3.3 Mesa 20.0.8 (LLVM 10.0.0 128 bits), Compiler: GCC 9.3.0, File-System: ext4, Screen Resolution: 1920x1080
CLOMP 1.2
Static OMP Speedup
Speedup > Higher Is Better
Run 1 . 57.1 |================================================================
Run 2 . 57.7 |=================================================================
Run 3 . 57.8 |=================================================================
Monkey Audio Encoding 3.99.6
WAV To APE
Seconds < Lower Is Better
Run 1 . 18.35 |================================================================
Run 2 . 18.33 |================================================================
Run 3 . 18.42 |================================================================
Opus Codec Encoding 1.3.1
WAV To Opus Encode
Seconds < Lower Is Better
Run 1 . 10.19 |================================================================
Run 2 . 10.22 |================================================================
Run 3 . 10.20 |================================================================
WavPack Audio Encoding 5.3
WAV To WavPack
Seconds < Lower Is Better
Run 1 . 17.32 |================================================================
Run 2 . 17.31 |================================================================
Run 3 . 17.29 |================================================================
Timed HMMer Search 3.3.1
Pfam Database Search
Seconds < Lower Is Better
Run 1 . 200.30 |===============================================================
Run 2 . 199.71 |===============================================================
Run 3 . 200.75 |===============================================================
Timed MAFFT Alignment 7.471
Multiple Sequence Alignment - LSU RNA
Seconds < Lower Is Better
Run 1 . 15.02 |===============================================================
Run 2 . 15.15 |================================================================
Run 3 . 15.02 |===============================================================
NCNN 20201218
Target: CPU - Model: mobilenet
ms < Lower Is Better
Run 1 . 43.10 |================================================================
Run 2 . 41.84 |==============================================================
Run 3 . 43.26 |================================================================
NCNN 20201218
Target: CPU-v2-v2 - Model: mobilenet-v2
ms < Lower Is Better
Run 1 . 17.42 |=========================================================
Run 2 . 19.42 |================================================================
Run 3 . 18.22 |============================================================
NCNN 20201218
Target: CPU-v3-v3 - Model: mobilenet-v3
ms < Lower Is Better
Run 1 . 16.30 |============================================================
Run 2 . 17.48 |================================================================
Run 3 . 16.91 |==============================================================
NCNN 20201218
Target: CPU - Model: shufflenet-v2
ms < Lower Is Better
Run 1 . 17.51 |================================================================
Run 2 . 17.35 |===============================================================
Run 3 . 16.94 |==============================================================
NCNN 20201218
Target: CPU - Model: mnasnet
ms < Lower Is Better
Run 1 . 16.17 |================================================================
Run 2 . 15.76 |==============================================================
Run 3 . 16.26 |================================================================
NCNN 20201218
Target: CPU - Model: efficientnet-b0
ms < Lower Is Better
Run 1 . 22.19 |=============================================================
Run 2 . 22.24 |=============================================================
Run 3 . 23.22 |================================================================
NCNN 20201218
Target: CPU - Model: blazeface
ms < Lower Is Better
Run 1 . 7.79 |================================================================
Run 2 . 7.89 |=================================================================
Run 3 . 7.90 |=================================================================
NCNN 20201218
Target: CPU - Model: googlenet
ms < Lower Is Better
Run 1 . 48.06 |==============================================================
Run 2 . 46.99 |============================================================
Run 3 . 49.81 |================================================================
NCNN 20201218
Target: CPU - Model: vgg16
ms < Lower Is Better
Run 1 . 100.72 |===============================================================
Run 2 . 94.33 |===========================================================
Run 3 . 88.55 |=======================================================
NCNN 20201218
Target: CPU - Model: resnet18
ms < Lower Is Better
Run 1 . 41.83 |===========================================================
Run 2 . 45.70 |================================================================
Run 3 . 43.64 |=============================================================
NCNN 20201218
Target: CPU - Model: alexnet
ms < Lower Is Better
Run 1 . 33.20 |================================================================
Run 2 . 30.19 |==========================================================
Run 3 . 31.92 |==============================================================
NCNN 20201218
Target: CPU - Model: resnet50
ms < Lower Is Better
Run 1 . 60.78 |================================================================
Run 2 . 59.24 |==============================================================
Run 3 . 59.49 |===============================================================
NCNN 20201218
Target: CPU - Model: yolov4-tiny
ms < Lower Is Better
Run 1 . 57.99 |================================================================
Run 2 . 55.80 |==============================================================
Run 3 . 56.52 |==============================================================
NCNN 20201218
Target: CPU - Model: squeezenet_ssd
ms < Lower Is Better
Run 1 . 46.68 |================================================================
Run 2 . 46.89 |================================================================
Run 3 . 44.81 |=============================================================
NCNN 20201218
Target: CPU - Model: regnety_400m
ms < Lower Is Better
Run 1 . 117.02 |==============================================================
Run 2 . 119.23 |===============================================================
Run 3 . 118.48 |===============================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 5.34771 |==============================================================
Run 2 . 4.49148 |====================================================
Run 3 . 4.33519 |==================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 12.42 |================================================================
Run 2 . 11.77 |=============================================================
Run 3 . 12.09 |==============================================================
oneDNN 2.0
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 2.67937 |==============================================================
Run 2 . 2.68511 |==============================================================
Run 3 . 2.66509 |==============================================================
oneDNN 2.0
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 3.56511 |==============================================================
Run 2 . 3.55970 |==============================================================
Run 3 . 3.57153 |==============================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 18.51 |===============================================================
Run 2 . 18.68 |================================================================
Run 3 . 18.66 |================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 4.03281 |==============================================================
Run 2 . 4.00713 |==============================================================
Run 3 . 4.01827 |==============================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 9.04439 |==============================================================
Run 2 . 9.03893 |==============================================================
Run 3 . 9.08767 |==============================================================
oneDNN 2.0
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 23.30 |================================================================
Run 2 . 22.46 |==============================================================
Run 3 . 23.21 |================================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 4.60248 |=============================================================
Run 2 . 4.71473 |==============================================================
Run 3 . 4.22841 |========================================================
oneDNN 2.0
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 4.41314 |==============================================================
Run 2 . 4.37097 |=============================================================
Run 3 . 4.40049 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 10732.73 |=============================================================
Run 2 . 10747.50 |=============================================================
Run 3 . 10314.22 |===========================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 3293.49 |============================================================
Run 2 . 3322.68 |=============================================================
Run 3 . 3393.82 |==============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 10583.10 |============================================================
Run 2 . 10647.60 |============================================================
Run 3 . 10812.54 |=============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 3300.07 |============================================================
Run 2 . 3434.14 |==============================================================
Run 3 . 3327.91 |============================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 1.71220 |=============================================================
Run 2 . 1.74056 |==============================================================
Run 3 . 1.66512 |===========================================================
oneDNN 2.0
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 10689.16 |===========================================================
Run 2 . 10915.65 |============================================================
Run 3 . 11077.90 |=============================================================
oneDNN 2.0
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
Run 1 . 3332.79 |=============================================================
Run 2 . 3312.30 |============================================================
Run 3 . 3405.82 |==============================================================
oneDNN 2.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
Run 1 . 1.78892 |==============================================================
Run 2 . 1.77953 |==============================================================
Run 3 . 1.79366 |==============================================================
Coremark 1.0
CoreMark Size 666 - Iterations Per Second
Iterations/Sec > Higher Is Better
Run 1 . 879248.02 |============================================================
Run 2 . 879237.12 |============================================================
Run 3 . 876909.95 |============================================================
Timed FFmpeg Compilation 4.2.2
Time To Compile
Seconds < Lower Is Better
Run 1 . 39.09 |================================================================
Run 2 . 39.11 |================================================================
Run 3 . 39.19 |================================================================
Build2 0.13
Time To Compile
Seconds < Lower Is Better
Run 1 . 102.30 |===============================================================
Run 2 . 102.59 |===============================================================
Run 3 . 102.35 |===============================================================
Timed Eigen Compilation 3.3.9
Time To Compile
Seconds < Lower Is Better
Run 1 . 120.02 |===============================================================
Run 2 . 119.98 |===============================================================
Run 3 . 120.19 |===============================================================
SQLite Speedtest 3.30
Timed Time - Size 1,000
Seconds < Lower Is Better
Run 1 . 90.12 |================================================================
Run 2 . 90.32 |================================================================
Run 3 . 90.11 |================================================================
Node.js V8 Web Tooling Benchmark
runs/s > Higher Is Better
Run 1 . 6.78 |================================================================
Run 2 . 6.74 |================================================================
Run 3 . 6.85 |=================================================================
simdjson 0.7.1
Throughput Test: Kostya
GB/s > Higher Is Better
Run 1 . 0.33 |=================================================================
Run 2 . 0.33 |=================================================================
Run 3 . 0.33 |=================================================================
simdjson 0.7.1
Throughput Test: LargeRandom
GB/s > Higher Is Better
Run 1 . 0.28 |=================================================================
Run 2 . 0.28 |=================================================================
Run 3 . 0.28 |=================================================================
simdjson 0.7.1
Throughput Test: PartialTweets
GB/s > Higher Is Better
Run 1 . 0.36 |=================================================================
Run 2 . 0.36 |=================================================================
Run 3 . 0.36 |=================================================================
simdjson 0.7.1
Throughput Test: DistinctUserID
GB/s > Higher Is Better
Run 1 . 0.37 |=================================================================
Run 2 . 0.37 |=================================================================
Run 3 . 0.37 |=================================================================