amd-genoa-onednn-31

2 x AMD EPYC 9654 96-Core testing with a AMD Titanite_4G (RTI1004D BIOS) and ASPEED on Clear Linux OS 38660 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 2303310-NE-AMDGENOAO60
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

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
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
Performance Per
Dollar
Date
Run
  Test
  Duration
a
March 31 2023
  1 Hour, 9 Minutes
b
April 01 2023
  11 Minutes
c
April 01 2023
  11 Minutes
Invert Hiding All Results Option
  30 Minutes

Only show results where is faster than
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):

amd-genoa-onednn-31 2 x AMD EPYC 9654 96-Core testing with a AMD Titanite_4G (RTI1004D BIOS) and ASPEED on Clear Linux OS 38660 via the Phoronix Test Suite. ,,"a","b","c" Processor,,2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads),2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads),2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads) Motherboard,,AMD Titanite_4G (RTI1004D BIOS),AMD Titanite_4G (RTI1004D BIOS),AMD Titanite_4G (RTI1004D BIOS) Chipset,,AMD Device 14a4,AMD Device 14a4,AMD Device 14a4 Memory,,1520GB,1520GB,1520GB Disk,,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007,2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007 Graphics,,ASPEED,ASPEED,ASPEED Network,,Broadcom NetXtreme BCM5720 PCIe,Broadcom NetXtreme BCM5720 PCIe,Broadcom NetXtreme BCM5720 PCIe OS,,Clear Linux OS 38660,Clear Linux OS 38660,Clear Linux OS 38660 Kernel,,6.2.8-1293.native (x86_64),6.2.8-1293.native (x86_64),6.2.8-1293.native (x86_64) Display Server,,X Server,X Server,X Server Compiler,,GCC 12.2.1 20230323 releases/gcc-12.2.0-616-g1b6b7f214c + Clang 15.0.7 + LLVM 15.0.7,GCC 12.2.1 20230323 releases/gcc-12.2.0-616-g1b6b7f214c + Clang 15.0.7 + LLVM 15.0.7,GCC 12.2.1 20230323 releases/gcc-12.2.0-616-g1b6b7f214c + Clang 15.0.7 + LLVM 15.0.7 File-System,,ext4,ext4,ext4 Screen Resolution,,800x600,800x600,800x600 ,,"a","b","c" "oneDNN - Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU (ms)",LIB,5.16295,5.56995,5.13421 "oneDNN - Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU (ms)",LIB,1.81517,1.75887,1.72273 "oneDNN - Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,3.81667,4.12976,3.98273 "oneDNN - Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.753564,0.816992,0.824101 "oneDNN - Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,3.80454,3.87903,4.61093 "oneDNN - Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,1.54627,1.64919,1.66125 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU (ms)",LIB,0.521813,0.547057,0.530342 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU (ms)",LIB,20.8426,20.6935,20.68 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU (ms)",LIB,0.946786,0.952539,0.954284 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.362781,0.363114,0.360127 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.923951,0.946937,0.911944 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,0.282128,0.292579,0.277235 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU (ms)",LIB,999.384,1001.27,974.37 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU (ms)",LIB,1297.60,1317.63,1424.56 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1007.475,1016.04,968.294 "oneDNN - Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,0.414999,0.408003,0.406869 "oneDNN - Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,2.23356,2.22682,2.21262 "oneDNN - Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,0.643195,0.640364,0.653813 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU (ms)",LIB,1302.83,1323.12,1357.36 "oneDNN - Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,998.040,1022.78,985.498 "oneDNN - Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU (ms)",LIB,,1373.66,1347.87