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.

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
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Audio Encoding 3 Tests
Bioinformatics 2 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 5 Tests
CPU Massive 4 Tests
Creator Workloads 4 Tests
Encoding 3 Tests
HPC - High Performance Computing 4 Tests
Machine Learning 2 Tests
Multi-Core 5 Tests
Programmer / Developer System Benchmarks 6 Tests
Scientific Computing 2 Tests
Server 3 Tests

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Run 1
December 21 2020
  11 Hours, 33 Minutes
Run 2
December 21 2020
  11 Hours, 37 Minutes
Run 3
December 22 2020
  10 Hours, 48 Minutes
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  11 Hours, 19 Minutes

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AMD EPYC 7601 Xmas 2020 Suite 1.0.0 System Test suite extracted from AMD EPYC 7601 Xmas 2020. pts/simdjson-1.1.1 Kostya Throughput Test: Kostya pts/simdjson-1.1.1 LargeRandom Throughput Test: LargeRandom pts/simdjson-1.1.1 PartialTweets Throughput Test: PartialTweets pts/simdjson-1.1.1 DistinctUserID Throughput Test: DistinctUserID pts/coremark-1.0.1 CoreMark Size 666 - Iterations Per Second pts/node-web-tooling-1.0.0 pts/clomp-1.1.1 Static OMP Speedup pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.1 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.1 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.1 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/ncnn-1.1.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.1.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.1.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.1.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.1.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.1.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.1.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.1.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.1.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.1.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.1.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.1.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.1.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.1.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.1.0 -1 Target: CPU - Model: regnety_400m pts/hmmer-1.2.2 Pfam Database Search pts/mafft-1.6.2 Multiple Sequence Alignment - LSU RNA pts/build-ffmpeg-1.0.2 Time To Compile pts/build2-1.1.0 Time To Compile pts/build-eigen-1.1.0 Time To Compile pts/encode-ape-1.4.0 WAV To APE pts/encode-opus-1.1.1 WAV To Opus Encode pts/sqlite-speedtest-1.0.1 Timed Time - Size 1,000 pts/encode-wavpack-1.4.1 WAV To WavPack