Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 10 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 2103218-HA-XEONSILVE43
Xeon Silver March,
"Basis Universal 1.13 - Settings: ETC1S",
Lower Results Are Better
"1",32.678,32.735,32.701
"2",32.804,32.497,32.67
"3",32.476,32.662,32.691
"Basis Universal 1.13 - Settings: UASTC Level 0",
Lower Results Are Better
"1",10.072,10.124,10.057
"2",10.064,10.084,10.051
"3",10.061,10.088,10.067
"Basis Universal 1.13 - Settings: UASTC Level 2",
Lower Results Are Better
"1",31.894,31.901,31.881
"2",31.923,31.841,31.849
"3",31.908,31.829,31.866
"Basis Universal 1.13 - Settings: UASTC Level 3",
Lower Results Are Better
"1",57.177,57.164,57.169
"2",57.228,57.149,57.182
"3",57.182,56.982,57.028
"Mobile Neural Network 1.1.3 - Model: SqueezeNetV1.0",
Lower Results Are Better
"1",8.042,8.297,8.06
"2",8.18,8.033,8.176
"3",8.317,8.291,8.101
"Mobile Neural Network 1.1.3 - Model: resnet-v2-50",
Lower Results Are Better
"1",43.546,43.931,43.492
"2",44.34,43.632,43.984
"3",44.568,44.054,44.074
"Mobile Neural Network 1.1.3 - Model: MobileNetV2_224",
Lower Results Are Better
"1",5.042,5.074,4.999
"2",5.089,5.027,5.168
"3",5.099,5.12,5.078
"Mobile Neural Network 1.1.3 - Model: mobilenet-v1-1.0",
Lower Results Are Better
"1",3.352,3.237,3.206
"2",3.281,3.206,3.193
"3",3.316,3.296,3.264
"Mobile Neural Network 1.1.3 - Model: inception-v3",
Lower Results Are Better
"1",54.139,55.665,55.482
"2",54.139,55.493,54.009
"3",54.15,54.128,53.992
"oneDNN 2.1.2 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",4.76004,4.75839,4.74616
"2",4.73925,4.74085,4.7591
"3",4.74332,4.73862,4.7394
"oneDNN 2.1.2 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",3.8831,3.95782,3.95791
"2",3.85663,3.89085,3.80541
"3",3.97971,3.92498,3.85087
"oneDNN 2.1.2 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",1.05779,1.04799,1.04792
"2",1.04862,1.05038,1.04863
"3",1.0464,1.0483,1.0519
"oneDNN 2.1.2 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",1.45005,1.44751,1.481
"2",1.41816,1.42324,1.41923
"3",1.43651,1.44518,1.43911
"oneDNN 2.1.2 - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",10.5824,10.597,10.6191
"2",10.6064,10.5737,10.583
"3",10.5904,10.6099,10.5826
"oneDNN 2.1.2 - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",3.109,3.1213,3.14429
"2",3.11518,3.13759,3.12098
"3",3.13006,3.1433,3.1182
"oneDNN 2.1.2 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",6.62917,6.49048,6.60438
"2",6.53242,6.51816,6.49877
"3",6.47891,6.59992,6.58727
"oneDNN 2.1.2 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",11.7434,11.7385,11.7958
"2",11.7651,11.781,11.7187
"3",11.6997,11.7976,11.8546
"oneDNN 2.1.2 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",7.64819,7.63386,7.67023
"2",7.63675,7.64774,7.65807
"3",7.63852,7.63599,7.63486
"oneDNN 2.1.2 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",6.35336,6.55691,6.28844
"2",6.39812,6.15385,6.09639
"3",6.0849,6.13141,6.2819
"oneDNN 2.1.2 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",1.26638,1.27102,1.2674
"2",1.27095,1.26853,1.27428
"3",1.26663,1.28227,1.26813
"oneDNN 2.1.2 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",1.88411,1.89058,1.88377
"2",1.88419,1.8839,1.88376
"3",1.88382,1.89088,1.88387
"oneDNN 2.1.2 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",3589.67,3585.08,3585.35
"2",3589.55,3585.82,3590.38
"3",3584.72,3602.87,3590.05
"oneDNN 2.1.2 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",1861.23,1858.3,1857.84
"2",1852.01,1866.73,1850.72
"3",1853.83,1854.82,1854.2
"oneDNN 2.1.2 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",3589.92,3592.97,3586.73
"2",3590.93,3594.61,3584.36
"3",3587.07,3586.94,3602.88
"oneDNN 2.1.2 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",16.2876,16.2766,16.2787
"2",16.3191,16.2775,16.2756
"3",16.2776,16.2771,16.2798
"oneDNN 2.1.2 - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",18.4123,18.4113,18.4587
"2",18.422,18.4343,18.4209
"3",18.4138,18.4211,18.4757
"oneDNN 2.1.2 - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",21.7694,21.7657,21.7698
"2",21.7708,21.7892,21.7626
"3",21.7671,21.7963,21.7687
"oneDNN 2.1.2 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",1853.53,1851.86,1853.03
"2",1856.07,1850.3,1855.03
"3",1853.64,1852.7,1850.67
"oneDNN 2.1.2 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"1",1.62186,1.60389,1.60632
"2",1.62028,1.6189,1.6168
"3",1.60344,1.59692,1.61409
"oneDNN 2.1.2 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",3591.64,3584.99,3592
"2",3589.47,3602.7,3587.8
"3",3586.52,3587.96,3589.68
"oneDNN 2.1.2 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",1853.97,1853.74,1851.45
"2",1856.11,1853.63,1850
"3",1849.63,1853.76,1863.37
"oneDNN 2.1.2 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"1",0.839012,0.838401,0.822162
"2",0.83336,0.835894,0.834031
"3",0.834894,0.83486,0.822453
"oneDNN 2.1.2 - Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"1",3.56654,3.56486,3.55764
"2",3.56494,3.55755,3.5649
"3",3.56123,3.56479,3.56736
"Xcompact3d Incompact3d 2021-03-11 - Input: input.i3d 129 Cells Per Direction",
Lower Results Are Better
"1",16.6550236,16.7573071,16.6956253
"2",16.8445663,16.8632793,16.8332386
"3",16.8450451,16.8673401,16.8662777
"Xcompact3d Incompact3d 2021-03-11 - Input: input.i3d 193 Cells Per Direction",
Lower Results Are Better
"1",66.2773132,66.5406113,66.5220032
"2",66.0492325,66.7892227,67.1624298
"3",65.5417328,66.1038589,66.8349304
"Stockfish 13 - Total Time",
Higher Results Are Better
"1",32208342,31248624,31538132
"2",30362851,32576226,30459366,31374071,30676608
"3",32388856,31327841,30339552,31375334
"Sysbench 1.0.20 - Test: RAM / Memory",
Higher Results Are Better
"1",15695.61,15624.82,15778.69
"2",15681.28,15557.72,15658.21
"3",15679.78,15670.33,15685.3
"Sysbench 1.0.20 - Test: CPU",
Higher Results Are Better
"1",22944.13,22942.28,22944.43
"2",22942.56,22944.25,22941.67
"3",22942.21,22942.16,22945.24
"AOM AV1 2.1-rc - Encoder Mode: Speed 0 Two-Pass",
Higher Results Are Better
"1",0.21,0.21,0.21
"2",0.2,0.21,0.2
"3",0.21,0.2,0.21
"AOM AV1 2.1-rc - Encoder Mode: Speed 4 Two-Pass",
Higher Results Are Better
"1",3.77,3.77,3.77
"2",3.76,3.78,3.77
"3",3.75,3.77,3.77
"AOM AV1 2.1-rc - Encoder Mode: Speed 6 Realtime",
Higher Results Are Better
"1",11.66,11.73,11.88
"2",11.8,11.6,11.65
"3",11.74,11.6,11.77
"AOM AV1 2.1-rc - Encoder Mode: Speed 6 Two-Pass",
Higher Results Are Better
"1",9.35,9.29,9.24
"2",9.36,9.32,9.25
"3",9.23,9.33,9.37
"AOM AV1 2.1-rc - Encoder Mode: Speed 8 Realtime",
Higher Results Are Better
"1",29.82,30.34,30.01
"2",29.71,29.79,30.51
"3",29.5,29.4,30.03
"SVT-VP9 0.3 - Tuning: VMAF Optimized - Input: Bosphorus 1080p",
Higher Results Are Better
"1",216,216.18,217.13
"2",218.88,218.27,217.97
"3",216.29,219.99,220.97
"SVT-VP9 0.3 - Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p",
Higher Results Are Better
"1",220.39,223.33,220.77
"2",221.78,221.99,221.96
"3",220.5,222.36,223.29
"SVT-VP9 0.3 - Tuning: Visual Quality Optimized - Input: Bosphorus 1080p",
Higher Results Are Better
"1",170.28,169.83,168.76
"2",170.09,169.11,170.23
"3",169.54,172.01,169.9
"SVT-HEVC 1.5.0 - Tuning: 1 - Input: Bosphorus 1080p",
Higher Results Are Better
"1",7.87,7.9,7.87
"2",7.85,7.85,7.89
"3",7.89,7.89,7.9
"SVT-HEVC 1.5.0 - Tuning: 7 - Input: Bosphorus 1080p",
Higher Results Are Better
"1",120.6,120.68,120.68
"2",120.99,120.97,121.29
"3",120.82,120.94,121.02
"SVT-HEVC 1.5.0 - Tuning: 10 - Input: Bosphorus 1080p",
Higher Results Are Better
"1",259.52,257.4,257.95
"2",257.4,257.95,256.52
"3",255.43,255.54,256.63
"LuaRadio 0.9.1 - Test: Five Back to Back FIR Filters",
Higher Results Are Better
"1",804.1,811.9,809.9
"2",803.4,812,808.1
"3",810.6,811.1,808.8
"LuaRadio 0.9.1 - Test: FM Deemphasis Filter",
Higher Results Are Better
"1",254.1,254.2,254.2
"2",253.9,254,254.2
"3",254.1,253.8,252
"LuaRadio 0.9.1 - Test: Hilbert Transform",
Higher Results Are Better
"1",55.3,55.4,55.4
"2",55.5,55.4,55.3
"3",55.3,55.5,55.3
"LuaRadio 0.9.1 - Test: Complex Phase",
Higher Results Are Better
"1",439.8,428.7,433.9
"2",444.5,438.9,431.1
"3",437.8,441.4,438.4
"simdjson 0.8.2 - Throughput Test: Kostya",
Higher Results Are Better
"1",1.13,1.13,1.13
"2",1.13,1.13,1.07,1.13
"3",1.13,1.13,1.13
"simdjson 0.8.2 - Throughput Test: LargeRandom",
Higher Results Are Better
"1",0.38,0.38,0.38
"2",0.38,0.38,0.38
"3",0.38,0.38,0.37
"simdjson 0.8.2 - Throughput Test: PartialTweets",
Higher Results Are Better
"1",1.37,1.37,1.36
"2",1.37,1.37,1.37
"3",1.36,1.37,1.36
"simdjson 0.8.2 - Throughput Test: DistinctUserID",
Higher Results Are Better
"1",1.48,1.48,1.48
"2",1.47,1.48,1.48
"3",1.48,1.48,1.48