2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and ASPEED on Ubuntu 20.04 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 2212200-NE-XEONRGOLD29
xeon r gold onednn 3.0
2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and ASPEED on Ubuntu 20.04 via the Phoronix Test Suite.
a:
Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE
OS: Ubuntu 20.04, Kernel: 6.1.0-phx (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080
b:
Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE
OS: Ubuntu 20.04, Kernel: 6.1.0-phx (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080
c:
Processor: 2 x Intel Xeon Gold 5220R @ 3.90GHz (36 Cores / 72 Threads), Motherboard: TYAN S7106 (V2.01.B40 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 94GB, Disk: 500GB Samsung SSD 860, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel I210 + 2 x QLogic cLOM8214 1/10GbE
OS: Ubuntu 20.04, Kernel: 6.1.0-phx (x86_64), Desktop: GNOME Shell 3.36.9, Display Server: X Server 1.20.13, Compiler: GCC 9.4.0, File-System: ext4, Screen Resolution: 1920x1080
oneDNN 3.0
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1.73090 |===============================================================
b . 1.67674 |=============================================================
c . 1.80903 |==================================================================
oneDNN 3.0
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 3.81228 |================================================================
b . 3.77586 |===============================================================
c . 3.94714 |==================================================================
oneDNN 3.0
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 5.78540 |================================================================
b . 5.80604 |================================================================
c . 5.97953 |==================================================================
oneDNN 3.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 2.34752 |================================================================
b . 2.41696 |==================================================================
c . 2.35576 |================================================================
oneDNN 3.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.215615 |===============================================================
b . 0.221767 |=================================================================
c . 0.219184 |================================================================
oneDNN 3.0
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 9.59277 |================================================================
b . 9.61333 |================================================================
c . 9.86147 |==================================================================
oneDNN 3.0
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 8.14539 |==================================================================
b . 7.93565 |================================================================
c . 8.11243 |==================================================================
oneDNN 3.0
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 3.11322 |==================================================================
b . 3.04360 |=================================================================
c . 3.06163 |=================================================================
oneDNN 3.0
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1.29656 |==================================================================
b . 1.27572 |=================================================================
c . 1.26769 |=================================================================
oneDNN 3.0
Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.517990 |=================================================================
b . 0.510599 |================================================================
c . 0.507931 |================================================================
oneDNN 3.0
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 9.21597 |==================================================================
b . 9.06402 |=================================================================
c . 9.14855 |==================================================================
oneDNN 3.0
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.583986 |=================================================================
b . 0.581283 |=================================================================
c . 0.576626 |================================================================
oneDNN 3.0
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1409.74 |==================================================================
b . 1392.36 |=================================================================
c . 1401.03 |==================================================================
oneDNN 3.0
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 796.63 |==================================================================
b . 805.57 |===================================================================
c . 798.11 |==================================================================
oneDNN 3.0
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 802.64 |==================================================================
b . 802.95 |==================================================================
c . 811.32 |===================================================================
oneDNN 3.0
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 7.59329 |==================================================================
b . 7.58018 |==================================================================
c . 7.51633 |=================================================================
oneDNN 3.0
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1.75250 |==================================================================
b . 1.74919 |=================================================================
c . 1.76469 |==================================================================
oneDNN 3.0
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 7.07070 |==================================================================
b . 7.10229 |==================================================================
c . 7.04447 |=================================================================
oneDNN 3.0
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.696550 |=================================================================
b . 0.693376 |=================================================================
c . 0.695969 |=================================================================
oneDNN 3.0
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 2.76971 |==================================================================
b . 2.76176 |==================================================================
c . 2.75773 |==================================================================
oneDNN 3.0
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 1400.41 |==================================================================
b . 1399.00 |==================================================================
c . 1404.71 |==================================================================
oneDNN 3.0
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 800.12 |===================================================================
b . 799.43 |===================================================================
c . 797.84 |===================================================================
oneDNN 3.0
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1400.64 |==================================================================
b . 1397.35 |==================================================================
c . 1397.94 |==================================================================
oneDNN 3.0
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 6.41676 |==================================================================
b . 6.42421 |==================================================================
c . 6.43035 |==================================================================