AMD Ryzen Threadripper 3960X 24-Core testing with a MSI Creator TRX40 (MS-7C59) v1.0 (1.12N1 BIOS) and Sapphire AMD Radeon RX 5500/5500M / Pro 5500M 4GB 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 2009286-PTS-TR3960XW65
TR 3960X WK,
"Dolfyn 0.527 - Computational Fluid Dynamics",
Lower Results Are Better
"1",15.801,15.819,15.824
"2",15.878,15.867,15.834
"3",15.751,15.783,15.581
"FFTE 7.0 - N=256, 3D Complex FFT Routine",
Higher Results Are Better
"1",83971.807178768,83385.881699763,82553.576751862
"2",84203.176609399,83878.391365945,83858.059888444
"3",83806.373863735,82850.352691757,83740.79985266
"Timed HMMer Search 3.3.1 - Pfam Database Search",
Lower Results Are Better
"1",130.779,131.275,131.174
"2",131.474,131.507,131.303
"3",131.114,131.637,131.772
"Timed MAFFT Alignment 7.471 - Multiple Sequence Alignment - LSU RNA",
Lower Results Are Better
"1",8.032,8.098,8.228
"2",8.196,8.229,8.304
"3",8.158,8.246,8.158
"BYTE Unix Benchmark 3.6 - Computational Test: Dhrystone 2",
Higher Results Are Better
"1",43623340.3,46810419.2,45338216.5,47039177.3,46609681.9,46659512.7
"2",45364860.1,46071532.7,45882885.9
"3",46996922.3,46599057,44678040.1
"Apache CouchDB 3.1.1 - Bulk Size: 100 - Inserts: 1000 - Rounds: 24",
Lower Results Are Better
"1",109.159,108.032,107.271
"2",107.044,108.429,107.196
"3",108.405,107.906,108.008
"GROMACS 2020.3 - Water Benchmark",
Higher Results Are Better
"1",2.534,2.527,2.526
"2",2.532,2.527,2.525
"3",2.531,2.527,2.524
"Caffe 2020-02-13 - Model: AlexNet - Acceleration: CPU - Iterations: 100",
Lower Results Are Better
"1",63520,64001,63870
"2",63350,63540,63685
"3",63555,64008,63536
"Caffe 2020-02-13 - Model: AlexNet - Acceleration: CPU - Iterations: 200",
Lower Results Are Better
"1",127195,127518,126913
"2",127056,127882,127526
"3",127128,127493,127961
"Caffe 2020-02-13 - Model: AlexNet - Acceleration: CPU - Iterations: 1000",
Lower Results Are Better
"1",635353,635476,633946
"2",635074,639679,635776
"3",638378,639971,638196
"Caffe 2020-02-13 - Model: GoogleNet - Acceleration: CPU - Iterations: 100",
Lower Results Are Better
"1",158965,158233,158909
"2",158890,158950,158832
"3",159345,159132,159286
"Caffe 2020-02-13 - Model: GoogleNet - Acceleration: CPU - Iterations: 200",
Lower Results Are Better
"1",317974,318393,317997
"2",319106,318322,319959
"3",318876,316118,317246
"Caffe 2020-02-13 - Model: GoogleNet - Acceleration: CPU - Iterations: 1000",
Lower Results Are Better
"1",1585990,1584820,1585290
"2",1592640,1591290,1585740
"3",1588060,1589630,1582580
"NCNN 20200916 - Target: CPU - Model: squeezenet",
Lower Results Are Better
"1",16.26,16.45,16.5
"2",16.17,16.63,16.37
"3",16.21,16.41,16.66
"NCNN 20200916 - Target: CPU - Model: mobilenet",
Lower Results Are Better
"1",17.39,17.25,17.75
"2",16.96,17.6,17.16
"3",17.08,17.34,17.25
"NCNN 20200916 - Target: CPU-v2-v2 - Model: mobilenet-v2",
Lower Results Are Better
"1",7.3,7.28,7.25
"2",7.19,7.32,7.2
"3",7.35,7.27,7.38
"NCNN 20200916 - Target: CPU-v3-v3 - Model: mobilenet-v3",
Lower Results Are Better
"1",6.86,6.78,6.82
"2",6.76,6.85,6.74
"3",6.84,6.79,6.94
"NCNN 20200916 - Target: CPU - Model: shufflenet-v2",
Lower Results Are Better
"1",7.32,7.24,7.36
"2",7.27,7.3,7.27
"3",7.37,7.31,7.34
"NCNN 20200916 - Target: CPU - Model: mnasnet",
Lower Results Are Better
"1",6.64,6.54,6.55
"2",6.48,6.56,6.54
"3",6.56,6.55,6.6
"NCNN 20200916 - Target: CPU - Model: efficientnet-b0",
Lower Results Are Better
"1",8.85,8.83,8.69
"2",8.61,8.71,8.6
"3",8.66,8.62,8.78
"NCNN 20200916 - Target: CPU - Model: blazeface",
Lower Results Are Better
"1",3.02,2.96,2.94
"2",2.92,2.96,2.92
"3",2.94,2.93,2.96
"NCNN 20200916 - Target: CPU - Model: googlenet",
Lower Results Are Better
"1",17.33,17.99,17.96
"2",17.08,18.08,18.08
"3",17.22,17.71,18.4
"NCNN 20200916 - Target: CPU - Model: vgg16",
Lower Results Are Better
"1",40.88,41.48,40.95
"2",43.24,42.22,41.45
"3",40.69,41.46,41.84
"NCNN 20200916 - Target: CPU - Model: resnet18",
Lower Results Are Better
"1",13.81,13.88,13.97
"2",13.66,14.02,14.15
"3",13.65,14.11,14.13
"NCNN 20200916 - Target: CPU - Model: alexnet",
Lower Results Are Better
"1",11.37,11.51,11.6
"2",11.32,11.64,11.71
"3",11.29,11.57,11.74
"NCNN 20200916 - Target: CPU - Model: resnet50",
Lower Results Are Better
"1",23.4,23.17,23.55
"2",23.52,23.58,23.54
"3",23.29,23.61,23.7
"NCNN 20200916 - Target: CPU - Model: yolov4-tiny",
Lower Results Are Better
"1",27.85,27.84,27.96
"2",28.47,28,28.14
"3",27.86,28.15,28.19
"NCNN 20200916 - Target: Vulkan GPU - Model: squeezenet",
Lower Results Are Better
"1",6.38,6.1,6.19
"2",6.4,6.31,6.09
"3",6.14,6.09,6.17
"NCNN 20200916 - Target: Vulkan GPU - Model: mobilenet",
Lower Results Are Better
"1",8.83,9.06,10.91
"2",10.53,9.78,9.05
"3",9.36,10.55,10.74
"NCNN 20200916 - Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2",
Lower Results Are Better
"1",4.36,4.35,4.34
"2",4.35,4.35,4.36
"3",4.34,4.37,4.35
"NCNN 20200916 - Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3",
Lower Results Are Better
"1",8.23,8.44,8.38
"2",8.71,8.04,7.93
"3",8.75,8.5,8.13
"NCNN 20200916 - Target: Vulkan GPU - Model: shufflenet-v2",
Lower Results Are Better
"1",3.2,3.2,3.19
"2",3.19,3.2,3.2
"3",3.19,3.2,3.2
"NCNN 20200916 - Target: Vulkan GPU - Model: mnasnet",
Lower Results Are Better
"1",4.44,4.44,4.43
"2",4.43,4.44,4.45
"3",4.44,4.43,4.46
"NCNN 20200916 - Target: Vulkan GPU - Model: efficientnet-b0",
Lower Results Are Better
"1",11.8,12.42,11.57
"2",12.58,11.67,12.35
"3",12.38,12.16,12.21
"NCNN 20200916 - Target: Vulkan GPU - Model: blazeface",
Lower Results Are Better
"1",0.9,0.89,0.9
"2",0.89,0.89,0.9
"3",0.88,0.9,0.89
"NCNN 20200916 - Target: Vulkan GPU - Model: googlenet",
Lower Results Are Better
"1",8.47,8.82,8.61
"2",8.97,8.12,7.66
"3",8.67,7.61,8.94
"NCNN 20200916 - Target: Vulkan GPU - Model: vgg16",
Lower Results Are Better
"1",80.84,79.96,80.19
"2",80.37,80.58,81.09
"3",80.62,80.11,79.56
"NCNN 20200916 - Target: Vulkan GPU - Model: resnet18",
Lower Results Are Better
"1",3.81,3.84,3.83
"2",3.82,3.82,3.82
"3",3.81,3.8,3.81
"NCNN 20200916 - Target: Vulkan GPU - Model: alexnet",
Lower Results Are Better
"1",28.62,27.68,28.05
"2",29.19,28.58,28.2
"3",28.6,28.33,28.15
"NCNN 20200916 - Target: Vulkan GPU - Model: resnet50",
Lower Results Are Better
"1",12.16,11.88,11.34
"2",11.94,12.47,11.13
"3",11.41,12.51,11.84
"NCNN 20200916 - Target: Vulkan GPU - Model: yolov4-tiny",
Lower Results Are Better
"1",21.73,21.67,21.71
"2",21.14,21.47,21.35
"3",21.43,21.25,20.48
"Hierarchical INTegration 1.0 - Test: FLOAT",
Higher Results Are Better
"1",386872797.88367,387644260.17334,387170154.87255
"2",387474675.50403,387067568.82305,387014133.9601
"3",388308244.95019,389264068.90479,388221605.34882
"Mlpack Benchmark - Benchmark: scikit_ica",
Lower Results Are Better
"1",54.139469861984,52.859108924866,53.588717937469
"2",51.839515447617,53.847733259201,52.060949802399
"3",53.817333936691,54.883629322052,54.299300193787
"Mlpack Benchmark - Benchmark: scikit_qda",
Lower Results Are Better
"1",45.28490447998,44.33241891861,45.314866542816
"2",45.677800178528,46.026683807373,46.022891521454
"3",45.927628040314,45.931308031082,45.810789346695
"Mlpack Benchmark - Benchmark: scikit_svm",
Lower Results Are Better
"1",19.855820655823,19.727839946747,19.673773527145
"2",19.735104084015,19.77538228035,19.550065279007
"3",19.750029087067,19.67284154892,19.58926987648
"Mlpack Benchmark - Benchmark: scikit_linearridgeregression",
Lower Results Are Better
"1",1.4220814704895,1.4367837905884,1.4505636692047
"2",1.4481935501099,1.4267988204956,1.4147696495056
"3",1.429417848587,1.4446098804474,1.4279325008392