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
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:
Processor: 2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads), Motherboard: AMD Titanite_4G (RTI1004D BIOS), Chipset: AMD Device 14a4, Memory: 1520GB, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe
OS: Clear Linux OS 38660, Kernel: 6.2.8-1293.native (x86_64), Display Server: X Server, Compiler: GCC 12.2.1 20230323 releases/gcc-12.2.0-616-g1b6b7f214c + Clang 15.0.7 + LLVM 15.0.7, File-System: ext4, Screen Resolution: 800x600
b:
Processor: 2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads), Motherboard: AMD Titanite_4G (RTI1004D BIOS), Chipset: AMD Device 14a4, Memory: 1520GB, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe
OS: Clear Linux OS 38660, Kernel: 6.2.8-1293.native (x86_64), Display Server: X Server, Compiler: GCC 12.2.1 20230323 releases/gcc-12.2.0-616-g1b6b7f214c + Clang 15.0.7 + LLVM 15.0.7, File-System: ext4, Screen Resolution: 800x600
c:
Processor: 2 x AMD EPYC 9654 96-Core @ 2.40GHz (192 Cores / 384 Threads), Motherboard: AMD Titanite_4G (RTI1004D BIOS), Chipset: AMD Device 14a4, Memory: 1520GB, Disk: 2 x 1920GB SAMSUNG MZWLJ1T9HBJR-00007, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe
OS: Clear Linux OS 38660, Kernel: 6.2.8-1293.native (x86_64), Display Server: X Server, Compiler: GCC 12.2.1 20230323 releases/gcc-12.2.0-616-g1b6b7f214c + Clang 15.0.7 + LLVM 15.0.7, File-System: ext4, Screen Resolution: 800x600
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1297.60 |============================================================
b . 1317.63 |=============================================================
c . 1424.56 |==================================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.753564 |===========================================================
b . 0.816992 |================================================================
c . 0.824101 |=================================================================
oneDNN 3.1
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 5.16295 |=============================================================
b . 5.56995 |==================================================================
c . 5.13421 |=============================================================
oneDNN 3.1
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 3.81667 |=============================================================
b . 4.12976 |==================================================================
c . 3.98273 |================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.282128 |===============================================================
b . 0.292579 |=================================================================
c . 0.277235 |==============================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1.81517 |==================================================================
b . 1.75887 |================================================================
c . 1.72273 |===============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1007.48 |=================================================================
b . 1016.04 |==================================================================
c . 968.29 |===============================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.521813 |==============================================================
b . 0.547057 |=================================================================
c . 0.530342 |===============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1302.83 |===============================================================
b . 1323.12 |================================================================
c . 1357.36 |==================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.923951 |===============================================================
b . 0.946937 |=================================================================
c . 0.911944 |===============================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 998.04 |================================================================
b . 1022.78 |==================================================================
c . 985.50 |================================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 999.38 |==================================================================
b . 1001.27 |==================================================================
c . 974.37 |================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.643195 |================================================================
b . 0.640364 |================================================================
c . 0.653813 |=================================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.414999 |=================================================================
b . 0.408003 |================================================================
c . 0.406869 |================================================================
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
b . 1373.66 |==================================================================
c . 1347.87 |=================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 2.23356 |==================================================================
b . 2.22682 |==================================================================
c . 2.21262 |=================================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.362781 |=================================================================
b . 0.363114 |=================================================================
c . 0.360127 |================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.946786 |================================================================
b . 0.952539 |=================================================================
c . 0.954284 |=================================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 20.84 |====================================================================
b . 20.69 |====================================================================
c . 20.68 |===================================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 1.54627 |=============================================================
b . 1.64919 |==================================================================
c . 1.66125 |==================================================================
oneDNN 3.1
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 3.80454 |======================================================
b . 3.87903 |========================================================
c . 4.61093 |==================================================================