Xeon Gold 6226R December

Intel Xeon Gold 6226R testing with a Supermicro X11SPL-F v1.02 (3.1 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 2012200-HA-XEONGOLD615
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Audio Encoding 2 Tests
Bioinformatics 2 Tests
Timed Code Compilation 3 Tests
C/C++ Compiler Tests 5 Tests
CPU Massive 5 Tests
Creator Workloads 4 Tests
Encoding 2 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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December 20 2020
  2 Hours, 20 Minutes
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December 20 2020
  2 Hours, 17 Minutes
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December 20 2020
  2 Hours
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Xeon Gold 6226R December Suite 1.0.0 System Test suite extracted from Xeon Gold 6226R December. pts/brl-cad-1.1.2 VGR Performance Metric pts/hmmer-1.2.2 Pfam Database Search pts/build2-1.1.0 Time To Compile pts/build-eigen-1.1.0 Time To Compile pts/node-web-tooling-1.0.0 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_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_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=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - 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 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/simdjson-1.1.1 Kostya Throughput Test: Kostya pts/sqlite-speedtest-1.0.1 Timed Time - Size 1,000 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/ncnn-1.1.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.1.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.1.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.1.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.1.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.1.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.1.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.1.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.1.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.1.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.1.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.1.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.1.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.1.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.1.0 -1 Target: CPU - Model: mobilenet pts/build-ffmpeg-1.0.2 Time To Compile pts/encode-ape-1.4.0 WAV To APE pts/encode-wavpack-1.4.0 WAV To WavPack pts/coremark-1.0.1 CoreMark Size 666 - Iterations Per Second pts/clomp-1.1.1 Static OMP Speedup pts/onednn-1.6.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - 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_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU 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_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - 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 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - 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/mafft-1.6.2 Multiple Sequence Alignment - LSU RNA pts/onednn-1.6.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - 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 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-1.6.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - 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 --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_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/onednn-1.6.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - 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