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, "CLOMP 1.2 - Static OMP Speedup", Higher Results Are Better "Run 1",57.6,57.1,56.5 "Run 2",58.3,56.3,58.5 "Run 3",58.6,57.2,57.5 "Monkey Audio Encoding 3.99.6 - WAV To APE", Lower Results Are Better "Run 1",18.4,18.319,18.328,18.332,18.351 "Run 2",18.325,18.329,18.359,18.326,18.321 "Run 3",18.747,18.313,18.335,18.339,18.347 "Opus Codec Encoding 1.3.1 - WAV To Opus Encode", Lower Results Are Better "Run 1",10.192,10.19,10.18,10.188,10.187 "Run 2",10.256,10.208,10.224,10.193,10.195 "Run 3",10.188,10.191,10.18,10.206,10.211 "WavPack Audio Encoding 5.3 - WAV To WavPack", Lower Results Are Better "Run 1",17.308,17.298,17.293,17.3,17.394 "Run 2",17.303,17.303,17.385,17.284,17.287 "Run 3",17.29,17.292,17.292,17.301,17.287 "Timed HMMer Search 3.3.1 - Pfam Database Search", Lower Results Are Better "Run 1",200.182,200.347,200.355 "Run 2",200.684,198.089,200.352 "Run 3",200.742,200.487,201.029 "Timed MAFFT Alignment 7.471 - Multiple Sequence Alignment - LSU RNA", Lower Results Are Better "Run 1",15.058,15.039,14.958 "Run 2",14.96,15.148,15.333 "Run 3",15.073,14.951,15.045 "NCNN 20201218 - Target: CPU - Model: mobilenet", Lower Results Are Better "Run 1",40.72,55.19,40.47,40.95,40.96,42.99,53.95,41.24,39.66,40.19,40.76,40.14 "Run 2",43.49,52.35,40.24,39.65,40.96,43.85,40.27,43.91,38.89,40.68,39.18,38.62 "Run 3",40.65,49.21,39.86,45.23,48.5,43.15,38.48,47.89,38.75,41.96,42.52,42.87 "NCNN 20201218 - Target: CPU-v2-v2 - Model: mobilenet-v2", Lower Results Are Better "Run 1",16.94,16.96,16.94,17.98,17.39,16.9,16.7,17.45,17.56,16.55,18.81,18.84 "Run 2",16.57,18.28,18.05,17.23,16.64,18.25,17.32,16.86,30.86,23.7,19.77,19.55 "Run 3",16.19,16.43,20.46,20.8,19.59,16.54,18.62,21.01,16.42,16.94,16.84,18.8 "NCNN 20201218 - Target: CPU-v3-v3 - Model: mobilenet-v3", Lower Results Are Better "Run 1",15.56,15.61,15.63,15.76,15.92,15.69,15.4,15.22,22.15,14.94,17.51,16.16 "Run 2",18.38,15.03,18.17,15.35,15.65,20.98,15.25,15.6,16.12,15.56,17.29,26.4 "Run 3",15.09,15.52,22.55,16.3,17.05,15.33,17.04,18.36,16.98,15.53,17.65,15.56 "NCNN 20201218 - Target: CPU - Model: shufflenet-v2", Lower Results Are Better "Run 1",19.49,17.09,16.41,16.87,17.2,16.91,21.36,16.51,16.78,17.48,16.85,17.12 "Run 2",16.63,17.21,16.97,16.2,16.59,16.8,16.88,17.72,22.85,16.9,17.11,16.35 "Run 3",16.69,17.05,16.46,17.3,17.87,17.13,16.36,16.54,16.69,17.36,16.54,17.24 "NCNN 20201218 - Target: CPU - Model: mnasnet", Lower Results Are Better "Run 1",15.24,19.57,14.93,15.15,15.95,16.4,19.55,16.36,14.97,15.41,15.54,14.98 "Run 2",15.11,16.2,16.22,15.33,15.44,14.93,15.69,15.43,18.12,15.45,15.87,15.38 "Run 3",15.15,15.56,15.52,15.99,18,15.85,15.43,15.35,16.32,15.21,20.25,16.43 "NCNN 20201218 - Target: CPU - Model: efficientnet-b0", Lower Results Are Better "Run 1",23.57,22.34,21.28,21.74,23.48,21.42,21.94,21.1,21.78,25.26,21.35,21.02 "Run 2",21.23,21.78,22.76,22.19,21.04,25.1,22.27,22.29,22.04,22.37,22.66,21.14 "Run 3",22.22,21.79,20.89,22.43,23.18,23.91,24.43,21.19,27.51,21.7,23.67,25.66 "NCNN 20201218 - Target: CPU - Model: blazeface", Lower Results Are Better "Run 1",7.64,7.54,7.78,7.66,7.94,7.77,7.94,7.96,7.76,7.79,8.23,7.52 "Run 2",7.66,8,7.81,7.82,7.72,7.69,7.99,7.78,7.7,8.12,8.15,8.27 "Run 3",8.55,7.76,7.6,7.79,8.21,7.6,7.63,7.58,7.75,8.03,8.04,8.22 "NCNN 20201218 - Target: CPU - Model: googlenet", Lower Results Are Better "Run 1",37.52,51.3,50.06,45.49,43.74,41.63,51.98,59.69,41.67,64.17,47.32,42.13 "Run 2",49.77,38.81,48.99,48.68,57.63,69.02,47.74,37.76,36.59,38.53,50.73,39.6 "Run 3",47.3,45.85,50.79,50.96,39.16,51.42,72.02,56.4,40.4,45.66,47.3,50.4 "NCNN 20201218 - Target: CPU - Model: vgg16", Lower Results Are Better "Run 1",111.42,104.76,108.51,64.7,93.36,107.54,120.31,94.68,114.17,112.43,106.8,69.97 "Run 2",122.18,109.19,88.01,86,95.76,93.3,86.85,99.31,94.75,76.59,79.83,100.24 "Run 3",83.46,100.4,93.86,69.4,84.66,97.48,105.58,66.33,90.71,90.35,76.88,103.46 "NCNN 20201218 - Target: CPU - Model: resnet18", Lower Results Are Better "Run 1",37.33,43.36,47.24,35.21,34.5,38.34,34.69,50.06,52.19,40.74,51.8,36.5 "Run 2",57.1,72.42,42.38,33.42,62.39,36.96,35.84,32.22,53.26,41.56,39.93,40.89 "Run 3",41.81,52.46,35.34,38.62,53.2,34.26,48,65.37,38.99,42.39,36.74,36.5 "NCNN 20201218 - Target: CPU - Model: alexnet", Lower Results Are Better "Run 1",27.06,32.4,35,29.54,25.38,39.9,36.36,29.36,42.28,41.06,32.2,27.88 "Run 2",38.32,29.46,24.64,32.08,34.18,26.26,28.08,33.8,26.84,29.63,29.86,29.13 "Run 3",30.9,37.32,27.63,29.51,32.82,23.08,31.41,43.62,28.51,29.39,29.72,39.12 "NCNN 20201218 - Target: CPU - Model: resnet50", Lower Results Are Better "Run 1",59.33,76.7,47.34,54.04,57.27,72.16,59.17,62.74,71.91,65.04,48.32,55.37 "Run 2",48.29,62.47,70.91,56.9,64.72,50.43,57.41,60.2,55.09,59.68,65.47,59.27 "Run 3",60.22,50.87,87.76,65.12,62.07,54.99,54.64,60.8,67.79,50.87,47.43,51.29 "NCNN 20201218 - Target: CPU - Model: yolov4-tiny", Lower Results Are Better "Run 1",63.55,56.43,61.4,59.05,54.65,53.53,55.24,63.26,58.2,53.71,59.79,57.05 "Run 2",55.28,57.18,60.91,54.93,54.35,58.39,54.18,55.7,53.16,58.73,53.98,52.75 "Run 3",56.84,56.9,59.65,52.78,58.37,55.01,58.17,57.49,57.16,54.2,56.87,54.77 "NCNN 20201218 - Target: CPU - Model: squeezenet_ssd", Lower Results Are Better "Run 1",41.74,41.7,43.44,46.29,49.87,42.42,54.79,55.64,51.81,42.98,44.65,44.82 "Run 2",44.39,59.38,52.99,43.64,47.84,45.33,47.44,41.68,46.13,46.93,45.24,41.72 "Run 3",45.72,49.28,41.6,51.8,41.3,44.59,41.64,51.07,48.56,39.67,41.56,40.9 "NCNN 20201218 - Target: CPU - Model: regnety_400m", Lower Results Are Better "Run 1",130.38,119.37,119.08,111.56,114.08,113.13,118.98,110.88,115.35,116.56,121.53,113.32 "Run 2",112.39,119.95,112.34,117.41,115.24,123.09,120.32,112.83,128.2,136.51,121.21,111.32 "Run 3",121.35,119.32,111.54,128.09,119.85,111.84,112.58,126.23,113.38,115.29,121.52,120.79 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",4.14751,10.2868,10.4454,4.70575,4.73693,4.57803,4.54155,4.68314,4.5274,4.63372,4.19541,4.69621,4.66246,4.67527,4.70006 "Run 2",4.59168,3.78955,4.5816,3.97894,4.66298,4.68694,4.7435,4.50603,4.6391,4.51896,4.74504,4.47204,4.60351,4.65085,4.20151 "Run 3",3.71803,3.99536,3.97376,5.3123,3.985,4.01508,4.53676,4.64141,4.68805,4.64929,4.63725,3.86995 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",11.6561,14.12,10.5749,11.6964,11.7066,14.1014,14.1614,11.7139,11.6957,13.056,11.7123,12.1024,14.5383,11.732,11.698 "Run 2",11.6912,10.6109,11.7135,11.9521,10.5399,11.7347,11.7475,11.7066,11.7511,11.7573,10.331,13.407,11.7243,11.7003,14.1811 "Run 3",11.7116,14.204,11.7478,12.4949,11.7255,11.735,11.7764,11.7304,14.2333,11.7248,11.7086,11.7065,11.7328,11.739,11.3138 "oneDNN 2.0 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",2.65938,2.70912,2.66962 "Run 2",2.67897,2.6999,2.67646 "Run 3",2.66566,2.65999,2.66962 "oneDNN 2.0 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",3.53221,3.58102,3.5821 "Run 2",3.52029,3.55227,3.60653 "Run 3",3.55981,3.56433,3.59046 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",18.6909,18.1238,18.7237 "Run 2",18.7823,18.1339,19.1238 "Run 3",18.9286,18.158,18.8803 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",3.98113,4.06486,4.05245 "Run 2",3.97476,4.0014,4.04522 "Run 3",4.03759,4.06272,3.9545 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",9.58759,8.65334,9.16602,9.64638,8.32336,8.77674,9.45969,9.38156,9.20572,9.43138,8.83075,8.46893,9.19468,8.80624,8.73344 "Run 2",9.14312,9.15925,8.81441 "Run 3",8.99496,8.91443,9.80537,9.07578,9.0721,9.1094,8.97691,9.76564,8.65133,8.59179,10.0052,8.35116,8.94266,9.19252,8.8658 "oneDNN 2.0 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",23.0713,23.4324,23.4054 "Run 2",17.6261,23.0003,22.9971,22.5335,23.0687,22.9423,23.1158,23.1757,22.4957,22.4328,23.139,22.9639 "Run 3",23.0428,23.4456,23.1475 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",6.28817,4.58778,4.25375,4.25447,4.50508,6.12753,4.20098,4.18789,4.20889,4.2146,4.50524,4.57247,4.23656,4.58502,4.30879 "Run 2",5.31791,5.09678,4.30967,4.36433,4.20886,5.5174,5.17639,4.60157,5.11852,4.23852,4.31373,4.27964,4.55468,5.39272,4.23029 "Run 3",4.15458,4.22569,4.30497 "oneDNN 2.0 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",4.43719,4.62893,4.22883,4.38936,4.40664,4.3879 "Run 2",4.25818,4.47278,4.38196 "Run 3",4.24778,4.51835,4.35925,4.47656 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",11631.4,10548.5,10721.2,10287.1,9657.35,11224.8,9906.73,10722.4,11234.6,10829.4,10926.7,11102.6 "Run 2",11514.3,10724.4,10121.6,10315.9,10692.3,10869.5,10776.3,10833,10104.8,10604.9,10959.8,11452.9 "Run 3",11016.1,9781.01,11067.8,10049.6,10930.1,9901.1,11236.4,9405.68,9847.1,10195.3,10936,9295.41,10423.3 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",3122.72,3364.77,3337.05,3154.27,3144.26,3361.03,3241.55,3435.68,3369.42,3429.94,3372.91,3578.08,2841.3,3379.07,3270.26 "Run 2",3028.88,2990.49,3370.87,3285.09,3438.95,3392.34,3429.77,3410.09,3410.74,3117.65,3378.03,3376.06,3386.04,3377.94,3447.3 "Run 3",3415.09,3374.42,3391.96 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",9897.87,10469.7,9783.05,10806.3,10094.7,10458.6,11507.2,10475.7,10517.3,11820.6 "Run 2",11908.9,10153.1,9847.78,10714.9,9947.65,11659.1,11489.3,10469,10680.3,9792.46,10455.7,10653 "Run 3",11048.8,11378.4,14078.4,10373,11038.9,9864.42,10279.4,10911.1,9945.82,9823.3,10395.1,10613.8 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",3028.19,3499.87,3368.94,3347.19,2982.07,3378.63,3510.65,3372.86,3081.74,3177.29,3368.96,3397.83,3358.99,3469,3158.8 "Run 2",3413.44,3512.75,3243.86,3463.34,3453.98,3517.49 "Run 3",3513.43,3237.73,3365.95,3562.85,3384.91,3562.39,3399.38,3369.77,3370.42,2921.63,3370.65,3217.62,3402.07,3044.32,3195.49 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU", Lower Results Are Better "Run 1",1.73554,1.7107,1.39145,1.73636,1.84715,1.40359,1.72751,1.64598,2.28959,1.45097,1.41406,1.83695,1.85064,1.72212,1.92035 "Run 2",1.9277,1.75888,1.73872,1.38689,1.68225,1.9658,1.73697,1.72969,1.9817,1.70999,1.88757,1.38059 "Run 3",1.50541,1.96052,1.96614,1.74082,1.43053,1.27665,1.71551,1.70391,1.96185,1.96954,1.44327,1.20576,1.83068,1.52195,1.74424 "oneDNN 2.0 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "Run 1",11391.2,11339.4,10689.5,11801.4,9548.66,11103.8,10070.3,10433.5,9824.7 "Run 2",11176.2,9898.12,11375.3,10316.2,11321.5,10809.6,11741.1,11090.5,10044.1,11165.1,11733.9,10316.2 "Run 3",10943.2,10851,11472.6,11044.7 "oneDNN 2.0 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "Run 1",3297.83,3202.03,3509.87,3147.96,3374.39,3341.38,3373,3345.61,3368.51,3367.31 "Run 2",2758.19,3215.03,2977.41,3208.2,3527.11,3369.21,3418.5,3191.15,3391.41,3395.09,3584.15,3486.72,3397.47,3580.2,3184.65 "Run 3",3346.43,3361.96,3509.07 "oneDNN 2.0 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "Run 1",1.75128,1.80937,1.80611 "Run 2",1.80971,1.68848,1.82942,1.69495,1.81296,1.81661,1.69423,1.83029,1.72604,1.81389,1.69412,1.81545,1.82362,1.81391,1.82922 "Run 3",1.75691,1.81848,1.8056 "Coremark 1.0 - CoreMark Size 666 - Iterations Per Second", Higher Results Are Better "Run 1",889094.697847,879241.654073,869407.715993 "Run 2",882596.304128,879362.462215,875752.599891 "Run 3",881259.466654,874794.969929,874675.413421 "Timed FFmpeg Compilation 4.2.2 - Time To Compile", Lower Results Are Better "Run 1",39.094,39.187,39.001 "Run 2",38.979,39.278,39.067 "Run 3",39.052,39.469,39.046 "Build2 0.13 - Time To Compile", Lower Results Are Better "Run 1",102.393,102.869,101.623 "Run 2",102.747,102.717,102.301 "Run 3",102.295,102.326,102.442 "Timed Eigen Compilation 3.3.9 - Time To Compile", Lower Results Are Better "Run 1",120.053,120.024,119.972 "Run 2",119.974,119.946,120.022 "Run 3",120.532,120.023,120.017 "SQLite Speedtest 3.30 - Timed Time - Size 1,000", Lower Results Are Better "Run 1",90.113,90.098,90.136 "Run 2",89.456,89.236,92.267 "Run 3",89.915,90.437,89.968 "Node.js V8 Web Tooling Benchmark - ", Higher Results Are Better "Run 1",6.64,6.9,6.79 "Run 2",6.74,6.76,6.72 "Run 3",6.82,6.92,6.81 "simdjson 0.7.1 - Throughput Test: Kostya", Higher Results Are Better "Run 1",0.33,0.33,0.33 "Run 2",0.33,0.33,0.33 "Run 3",0.33,0.33,0.33 "simdjson 0.7.1 - Throughput Test: LargeRandom", Higher Results Are Better "Run 1",0.28,0.28,0.28 "Run 2",0.28,0.28,0.28 "Run 3",0.28,0.28,0.28 "simdjson 0.7.1 - Throughput Test: PartialTweets", Higher Results Are Better "Run 1",0.36,0.36,0.36 "Run 2",0.36,0.36,0.36 "Run 3",0.36,0.36,0.36 "simdjson 0.7.1 - Throughput Test: DistinctUserID", Higher Results Are Better "Run 1",0.37,0.37,0.37 "Run 2",0.37,0.37,0.37 "Run 3",0.37,0.37,0.37