EPYC 7502

AMD EPYC 7502 32-Core testing with a ASRockRack EPYCD8 (P2.10 BIOS) and llvmpipe on Ubuntu 20.10 via the Phoronix Test Suite.

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Bioinformatics 2 Tests
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December 12 2020
  2 Hours, 14 Minutes
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December 12 2020
  1 Hour, 18 Minutes
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December 12 2020
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EPYC 7502 Suite 1.0.0 System Test suite extracted from EPYC 7502. pts/compilebench-1.0.3 COMPILE Test: Compile pts/compilebench-1.0.3 INITIAL_CREATE Test: Initial Create pts/compilebench-1.0.3 READ_COMPILED_TREE Test: Read Compiled Tree pts/brl-cad-1.1.2 VGR Performance Metric pts/hmmer-1.2.2 Pfam Database Search pts/mafft-1.6.2 Multiple Sequence Alignment - LSU RNA pts/onednn-1.6.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-1.6.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/coremark-1.0.1 CoreMark Size 666 - Iterations Per Second pts/build-ffmpeg-1.0.2 Time To Compile pts/graphics-magick-2.0.2 -swirl 90 Operation: Swirl pts/graphics-magick-2.0.2 -rotate 90 Operation: Rotate pts/graphics-magick-2.0.2 -sharpen 0x2.0 Operation: Sharpen pts/graphics-magick-2.0.2 -enhance Operation: Enhanced pts/graphics-magick-2.0.2 -resize 50% Operation: Resizing pts/graphics-magick-2.0.2 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian pts/graphics-magick-2.0.2 -colorspace HWB Operation: HWB Color Space pts/apache-siege-1.0.5 -c10 Concurrent Users: 10 pts/apache-siege-1.0.5 -c50 Concurrent Users: 50 pts/apache-siege-1.0.5 -c100 Concurrent Users: 100 pts/apache-siege-1.0.5 -c200 Concurrent Users: 200 pts/apache-siege-1.0.5 -c250 Concurrent Users: 250 pts/apache-siege-1.0.5 -c500 Concurrent Users: 500 pts/sqlite-speedtest-1.0.1 Timed Time - Size 1,000