9684x ne

Tests for a future article. 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 2310150-NE-9684XNE5490
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

Creator Workloads 4 Tests
Game Development 2 Tests
Multi-Core 4 Tests
Intel oneAPI 4 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
View Logs
Performance Per
Dollar
Date
Run
  Test
  Duration
a
October 15 2023
  2 Hours, 49 Minutes
b
October 15 2023
  41 Minutes
Invert Hiding All Results Option
  1 Hour, 45 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):

9684x ne, "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: 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 3D - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "a",0.856895,0.837903,0.860862 "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: 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: 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: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "a",2057.15,2041.75,2011.75 "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: bf16bf16bf16 - Engine: CPU", Lower Results Are Better "a",1.59496,1.56509,1.54644 "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: 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 Training - Data Type: u8s8f32 - Engine: CPU", Lower Results Are Better "a",1856.93,1773.09,1822.73 "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: 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_1d - Data Type: f32 - Engine: CPU", Lower Results Are Better "a",30.1444,30.5306,30.4209 "b", "Embree 4.3 - Binary: Pathtracer - Model: Asian Dragon Obj", Higher Results Are Better "a",189.5687,186.461,189.9801 "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", "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 "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: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only", Higher Results Are Better "a",3.7965072133637,3.7488284910965,3.8025560781958 "b",3.8035540408958 "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 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", "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: Crown", Higher Results Are Better "a",193.4349,192.0191,192.0039 "b", "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: 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 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 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", "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", "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: 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",