2 x AMD EPYC 9684X 96-Core testing with a AMD Titanite_4G (RTI1007B BIOS) and ASPEED on Ubuntu 23.10 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 2310151-NE-9684XNE6190
9684x ne,
"Embree 4.3 - Binary: Pathtracer - Model: Crown",
Higher Results Are Better
"a",193.4349,192.0191,192.0039
"b",
"Embree 4.3 - Binary: Pathtracer ISPC - Model: Crown",
Higher Results Are Better
"a",201.463,199.7135,200.3004
"b",
"Embree 4.3 - Binary: Pathtracer - Model: Asian Dragon",
Higher Results Are Better
"a",212.9697,211.6928,212.7729
"b",
"Embree 4.3 - Binary: Pathtracer - Model: Asian Dragon Obj",
Higher Results Are Better
"a",189.5687,186.461,189.9801
"b",
"Embree 4.3 - Binary: Pathtracer ISPC - Model: Asian Dragon",
Higher Results Are Better
"a",235.4234,234.559,234.4379
"b",
"Embree 4.3 - Binary: Pathtracer ISPC - Model: Asian Dragon Obj",
Higher Results Are Better
"a",201.8745,202.6354,200.0926
"b",
"Intel Open Image Denoise 2.1 - Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only",
Higher Results Are Better
"a",3.7965072133637,3.7488284910965,3.8025560781958
"b",3.8035540408958
"Intel Open Image Denoise 2.1 - Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only",
Higher Results Are Better
"a",3.7995075838171,3.8154541153488,3.7910523582241
"b",3.7827205326071
"Intel Open Image Denoise 2.1 - Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only",
Higher Results Are Better
"a",1.8933190450856,1.8984518125469,1.8921941315491
"b",1.8998331946455
"OpenVKL 2.0.0 - Benchmark: vklBenchmarkCPU ISPC",
Higher Results Are Better
"a",3528,3529,3532
"b",
"OpenVKL 2.0.0 - Benchmark: vklBenchmarkCPU Scalar",
Higher Results Are Better
"a",1496,1494,1492
"b",
"oneDNN 3.3 - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",11.5811,5.20502,7.93004,8.13779,11.5815,10.0245,4.58168,7.65496,9.00793,5.28746,8.58479,7.4166,5.54924,11.8631,9.22347
"b",
"oneDNN 3.3 - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",4.41957,4.41633,4.38363
"b",
"oneDNN 3.3 - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",16.108,4.87936,10.3333,10.0593,8.51529,17.5721,10.561,12.817,10.7189,15.2452,6.03719,8.86112,14.9772,8.7486,16.4375
"b",
"oneDNN 3.3 - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",0.856895,0.837903,0.860862
"b",
"oneDNN 3.3 - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",29.1481,33.2081,34.4403,34.3437,34.2476,30.3094,34.3403,34.2771,33.7954,33.6357,29.4103,28.5158,29.9166,29.1916,33.4478
"b",
"oneDNN 3.3 - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",3.52557,3.60055,3.33034,3.59629,3.44264,3.42241,3.46258,3.50675,3.36704,3.17476,3.50857,3.34415,3.41855,3.4478,3.56114
"b",
"oneDNN 3.3 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",0.483501,0.4726,0.486307
"b",
"oneDNN 3.3 - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",30.1444,30.5306,30.4209
"b",
"oneDNN 3.3 - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",0.956288,0.945818,0.944272
"b",
"oneDNN 3.3 - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",0.330185,0.307392,0.324574,0.327177,0.309315,0.308485,0.313227,0.335153,0.332543,0.309071,0.307619,0.326445,0.330759,0.324953,0.310255
"b",
"oneDNN 3.3 - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",0.7741,0.77171,0.786139
"b",
"oneDNN 3.3 - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",0.273335,0.278935,0.274718
"b",
"oneDNN 3.3 - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",1830.83,1877.3,1778.02,1898.94,1928.23,1871.55,1883.49,1827.79,1867.32
"b",
"oneDNN 3.3 - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU",
Lower Results Are Better
"a",2154.7,2108.78,2130.3
"b",
"oneDNN 3.3 - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",1856.93,1773.09,1822.73
"b",
"oneDNN 3.3 - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",0.366925,0.365919,0.383947,0.373156
"b",
"oneDNN 3.3 - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",1.59496,1.56509,1.54644
"b",
"oneDNN 3.3 - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",0.627582,0.629054,0.620798
"b",
"oneDNN 3.3 - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU",
Lower Results Are Better
"a",2025.86,2071.27,2115.39
"b",
"oneDNN 3.3 - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",1860.36,1889.96,1830.72
"b",
"oneDNN 3.3 - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU",
Lower Results Are Better
"a",2057.15,2041.75,2011.75
"b",
"easyWave r34 - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240",
Lower Results Are Better
"a",3.069,3.007,3.975,3.909,4.044,2.987,3.046,3.108,3.029,3.969,3.188,3.123,3.034,4.892,5.23
"b",
"easyWave r34 - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200",
Lower Results Are Better
"a",38.114,35.372,40.948,38.83,46.34,33.869,42.56,35.286,61.097,42.63,34.507,36.242
"b",
"easyWave r34 - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400",
Lower Results Are Better
"a",88.133,122.637,127.338,88.662,108.274,111.881,88.209,102.812,80.889,94.843,81.773,105.846,84.819,88.573,90.475
"b",