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, "BRL-CAD 7.30.8 - VGR Performance Metric", Higher Results Are Better "1", "2", "Timed HMMer Search 3.3.1 - Pfam Database Search", Lower Results Are Better "1",173.985,174.929,173.766 "2",174.955,173.975,173.576 "3",174.561,174.731,174.05 "Build2 0.13 - Time To Compile", Lower Results Are Better "1",95.235,96.04,95.464 "2",95.246,96.112,95.701 "3",95.234,95.623,95.728 "Timed Eigen Compilation 3.3.9 - Time To Compile", Lower Results Are Better "1",85.715,85.315,85.55 "2",85.999,85.337,85.317 "3",85.555,85.551,86.246 "Node.js V8 Web Tooling Benchmark - ", Higher Results Are Better "1",10.72,10.76,10.86 "2",10.65,10.73,10.68 "3",10.72,10.57,10.74 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",1646.1,1644.26,1646.48 "2",1640.58,1644.52,1645.9 "3",1646.09,1645.24,1643.15 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",1639.17,1648.16,1643.45 "2",1654.86,1643.66,1638.39 "3",1643.91,1648.11,1645.71 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",1641.89,1643.85,1643.91 "2",1646.26,1645.41,1643.58 "3",1642.25,1643.68,1655.44 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",922.732,923.506,922.385 "2",922.376,923.777,923.954 "3",922.887,923.289,923.832 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",921.082,921.798,922.143 "2",926.649,921.962,921.703 "3",919.344,921.894,921.352 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",922.467,922.831,923.678 "2",922.507,926.087,922.015 "3",925.14,921.682,919.987 "simdjson 0.7.1 - Throughput Test: Kostya", Higher Results Are Better "1",0.56,0.56,0.56 "2",0.56,0.56,0.56 "3",0.56,0.56,0.56 "SQLite Speedtest 3.30 - Timed Time - Size 1,000", Lower Results Are Better "1",65.442,65.284,65.364 "2",65.696,65.771,66.088 "3",65.896,65.227,65.658 "simdjson 0.7.1 - Throughput Test: LargeRandom", Higher Results Are Better "1",0.39,0.39,0.39 "2",0.39,0.39,0.39 "3",0.39,0.39,0.39 "simdjson 0.7.1 - Throughput Test: PartialTweets", Higher Results Are Better "1",0.57,0.57,0.57 "2",0.57,0.57,0.57 "3",0.57,0.57,0.57 "simdjson 0.7.1 - Throughput Test: DistinctUserID", Higher Results Are Better "1",0.58,0.58,0.58 "2",0.58,0.58,0.58 "3",0.58,0.58,0.58 "NCNN 20201218 - Target: CPU - Model: regnety_400m", Lower Results Are Better "1",27.28,27.21,27.07 "2",27.11,26.95,27.33 "3",27.2,27.49,27.54 "NCNN 20201218 - Target: CPU - Model: squeezenet_ssd", Lower Results Are Better "1",16.81,16.86,16.52 "2",16.45,16.44,16.45 "3",17.19,16.66,16.96 "NCNN 20201218 - Target: CPU - Model: yolov4-tiny", Lower Results Are Better "1",25.47,24.94,23.76 "2",23.68,24.95,24.57 "3",25.7,24.55,25.41 "NCNN 20201218 - Target: CPU - Model: resnet50", Lower Results Are Better "1",20.74,20.82,19.26 "2",18.84,19.08,18.86 "3",20.93,19.34,20.87 "NCNN 20201218 - Target: CPU - Model: alexnet", Lower Results Are Better "1",8.05,8.07,8.1 "2",6.73,6.71,6.74 "3",6.78,7.42,6.77 "NCNN 20201218 - Target: CPU - Model: resnet18", Lower Results Are Better "1",10.98,11.02,9.73 "2",9.41,9.39,9.43 "3",9.49,9.48,9.49 "NCNN 20201218 - Target: CPU - Model: vgg16", Lower Results Are Better "1",30.15,30.15,28.65 "2",28.03,27.99,28.09 "3",29.17,28.65,29.89 "NCNN 20201218 - Target: CPU - Model: googlenet", Lower Results Are Better "1",15.01,15.03,13.65 "2",12.98,12.98,13.02 "3",13.08,13.1,13.1 "NCNN 20201218 - Target: CPU - Model: blazeface", Lower Results Are Better "1",3.02,2.88,2.89 "2",2.88,2.87,2.89 "3",2.89,2.9,2.91 "NCNN 20201218 - Target: CPU - Model: efficientnet-b0", Lower Results Are Better "1",7.62,7.64,7.83 "2",7.25,7.26,7.27 "3",7.2,7.21,7.19 "NCNN 20201218 - Target: CPU - Model: mnasnet", Lower Results Are Better "1",5.65,5.47,5.57 "2",5.34,5.38,5.39 "3",5.31,5.34,5.38 "NCNN 20201218 - Target: CPU - Model: shufflenet-v2", Lower Results Are Better "1",5.92,5.95,5.94 "2",5.93,6 "3",5.95,6.01,6.06 "NCNN 20201218 - Target: CPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "1",5.33,5.11,5.2 "2",5.21,5.16,5.04 "3",5.09,5.05,5.07 "NCNN 20201218 - Target: CPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "1",6.21,5.93,6.07 "2",5.98,5.94,5.97 "3",5.92,5.86,5.9 "NCNN 20201218 - Target: CPU - Model: mobilenet", Lower Results Are Better "1",17.85,17.92,17.54 "2",16.92,16.97,16.99 "3",18.03,17.2,17.97 "Timed FFmpeg Compilation 4.2.2 - Time To Compile", Lower Results Are Better "1",43.296,43.397,43.797 "2",43.082,43.397,43.229 "3",43.639,43.425,43.298 "Monkey Audio Encoding 3.99.6 - WAV To APE", Lower Results Are Better "1",17.525,17.548,17.491,17.507,17.57 "2",17.519,17.588,17.574,17.51,17.483 "3",17.552,17.546,17.546,17.545,17.528 "WavPack Audio Encoding 5.3 - WAV To WavPack", Lower Results Are Better "1",16.757,16.72,16.749,16.711,16.716 "2",16.765,16.761,16.716,16.766,16.757 "3",16.805,16.771,16.807,16.741,16.769 "Coremark 1.0 - CoreMark Size 666 - Iterations Per Second", Higher Results Are Better "1",539424.333095,537002.852828,537431.246589 "2",536867.71244,535676.919858,533222.245366 "3",529757.470408,538811.247685,536080.747163 "CLOMP 1.2 - Static OMP Speedup", Higher Results Are Better "1",27.4,26.4,26.5 "2",26.8,25.4,26.4 "3",26.6,26.3,26.7 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",11.2825,11.2972,11.2939 "2",11.2741,11.2948,11.2985 "3",11.3148,11.3026,11.3312 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",0.52541,0.52663,0.528037 "2",0.525967,0.528426,0.527363 "3",0.526095,0.526575,0.526641 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",2.75124,2.75967,2.75401 "2",2.75242,2.75976,2.77413 "3",2.75349,2.75608,2.759 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",5.58984,5.6159,5.63464 "2",5.5882,5.61458,5.61834 "3",5.58738,5.61037,5.61656 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",2.34078,2.36243,2.35754 "2",2.34295,2.35712,2.35107 "3",2.34797,2.34654,2.3552 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",0.495283,0.496118,0.497334 "2",0.490352,0.491595,0.492431 "3",0.495619,0.496336,0.495244 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",0.972276,0.976289,0.975307 "2",0.972993,0.97623,0.9724 "3",0.973083,0.979193,0.978943 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",2.04773,2.06115,2.0537 "2",2.04831,2.06862,2.05622 "3",2.05139,2.06352,2.05533 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",0.489984,0.48317,0.486167 "2",0.48812,0.473594,0.472138 "3",0.470351,0.483276,0.484022 "Timed MAFFT Alignment 7.471 - Multiple Sequence Alignment - LSU RNA", Lower Results Are Better "1",10.793,10.58,10.537 "2",10.69,10.654,10.426 "3",10.568,10.487,10.591 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",2.58982,2.60782,2.59066 "2",2.57092,2.59831,2.59495 "3",2.60325,2.62033,2.61693 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",1.24027,1.24492,1.24955 "2",1.24346,1.25182,1.24816 "3",1.2413,1.24474,1.24535 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",3.12592,3.15748,3.16108 "2",3.12575,3.1421,3.14336 "3",3.14035,3.16499,3.16971 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",9.39023,9.44727,9.42773 "2",9.41642,9.43331,9.4193 "3",9.40914,9.43765,9.42151 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",4.13559,4.18262,4.18146 "2",4.13939,4.19177,4.15595 "3",4.14343,4.19339,4.18653 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",4.34533,4.37199,4.31276 "2",4.32074,4.36721,4.33451 "3",4.32136,4.36453,4.36652 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "1",0.862413,0.857094,0.881614 "2",0.84683,0.895305,0.837807,0.843707,0.879665 "3",0.871682,0.846214,0.870673 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "1",12.5334,12.5336,12.5152 "2",12.5396,12.5609,12.5249 "3",12.5226,12.5371,12.5408 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU", Lower Results Are Better "1",3.20487,3.21743,3.21147 "2",3.21791,3.20879,3.19804 "3",3.20004,3.23844,3.19987