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
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.
a:
Processor: 2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads), Motherboard: AMD Titanite_4G (RTI1007B BIOS), Chipset: AMD Device 14a4, Memory: 1520GB, Disk: 3201GB Micron_7450_MTFDKCC3T2TFS, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe
OS: Ubuntu 23.10, Kernel: 6.6.0-060600rc1-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server 1.21.1.7, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
b:
Processor: 2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads), Motherboard: AMD Titanite_4G (RTI1007B BIOS), Chipset: AMD Device 14a4, Memory: 1520GB, Disk: 3201GB Micron_7450_MTFDKCC3T2TFS, Graphics: ASPEED, Network: Broadcom NetXtreme BCM5720 PCIe
OS: Ubuntu 23.10, Kernel: 6.6.0-060600rc1-generic (x86_64), Desktop: GNOME Shell, Display Server: X Server 1.21.1.7, Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1920x1200
Embree 4.3
Binary: Pathtracer - Model: Crown
Frames Per Second > Higher Is Better
a . 192.49 |===================================================================
b . 192.17 |===================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Crown
Frames Per Second > Higher Is Better
a . 200.49 |===================================================================
b . 199.71 |===================================================================
Embree 4.3
Binary: Pathtracer - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 212.48 |===================================================================
b . 212.02 |===================================================================
Embree 4.3
Binary: Pathtracer - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 188.67 |===================================================================
b . 189.71 |===================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Asian Dragon
Frames Per Second > Higher Is Better
a . 234.81 |===================================================================
b . 233.92 |===================================================================
Embree 4.3
Binary: Pathtracer ISPC - Model: Asian Dragon Obj
Frames Per Second > Higher Is Better
a . 201.53 |===================================================================
b . 200.80 |===================================================================
Intel Open Image Denoise 2.1
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 3.78 |=====================================================================
b . 3.80 |=====================================================================
Intel Open Image Denoise 2.1
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 3.80 |=====================================================================
b . 3.78 |=====================================================================
Intel Open Image Denoise 2.1
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 1.89 |=====================================================================
b . 1.90 |=====================================================================
OpenVKL 2.0.0
Benchmark: vklBenchmarkCPU ISPC
Items / Sec > Higher Is Better
a . 3530 |=====================================================================
b . 3529 |=====================================================================
OpenVKL 2.0.0
Benchmark: vklBenchmarkCPU Scalar
Items / Sec > Higher Is Better
a . 1494 |=====================================================================
b . 1494 |=====================================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 8.24195 |==================================================================
b . 6.89651 |=======================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 4.40651 |==================================================================
b . 4.19777 |===============================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 11.46 |==============================================
b . 16.95 |====================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.851887 |=================================================================
b . 0.818265 |==============================================================
oneDNN 3.3
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 32.15 |================================================================
b . 33.91 |====================================================================
oneDNN 3.3
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 3.44728 |================================================================
b . 3.55742 |==================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.480803 |=============================================================
b . 0.509185 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 30.37 |====================================================================
b . 30.56 |====================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 0.948793 |================================================================
b . 0.969145 |=================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.319810 |================================================================
b . 0.326161 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.777316 |=================================================================
b . 0.771600 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 0.275663 |================================================================
b . 0.278455 |=================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 1862.61 |==================================================================
b . 1852.73 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
ms < Lower Is Better
a . 2131.26 |==================================================================
b . 2062.29 |================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 1817.58 |==================================================================
b . 1798.18 |=================================================================
oneDNN 3.3
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.372487 |===============================================================
b . 0.386826 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 1.56883 |==================================================================
b . 1.54248 |=================================================================
oneDNN 3.3
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 0.625811 |=================================================================
b . 0.617057 |================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU
ms < Lower Is Better
a . 2070.84 |=================================================================
b . 2104.98 |==================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 1860.35 |==================================================================
b . 1831.26 |=================================================================
oneDNN 3.3
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
a . 2036.88 |================================================================
b . 2096.03 |==================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240
Seconds < Lower Is Better
a . 3.574 |====================================================================
b . 3.079 |===========================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200
Seconds < Lower Is Better
a . 40.48 |====================================================================
b . 39.75 |===================================================================
easyWave r34
Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400
Seconds < Lower Is Better
a . 97.68 |====================================================================
b . 97.45 |====================================================================