2 x INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 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 2402196-NE-XEONFEBBY11
xeon febby
2 x INTEL XEON PLATINUM 8592+ testing with a Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.
a:
Processor: 2 x INTEL XEON PLATINUM 8592+ @ 3.90GHz (128 Cores / 256 Threads), Motherboard: Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS), Chipset: Intel Device 1bce, Memory: 1008GB, Disk: 3201GB Micron_7450_MTFDKCB3T2TFS, Graphics: ASPEED, Network: 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 23.10, Kernel: 6.6.0-060600-generic (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1024x768
b:
Processor: 2 x INTEL XEON PLATINUM 8592+ @ 3.90GHz (128 Cores / 256 Threads), Motherboard: Quanta Cloud QuantaGrid D54Q-2U S6Q-MB-MPS (3B05.TEL4P1 BIOS), Chipset: Intel Device 1bce, Memory: 1008GB, Disk: 3201GB Micron_7450_MTFDKCB3T2TFS, Graphics: ASPEED, Network: 2 x Intel X710 for 10GBASE-T
OS: Ubuntu 23.10, Kernel: 6.6.0-060600-generic (x86_64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1024x768
NAMD 3.0b6
Input: ATPase with 327,506 Atoms
ns/day > Higher Is Better
a . 5.98308 |==================================================================
b . 4.02029 |============================================
ONNX Runtime 1.17
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 15.96 |================================================
b . 22.66 |====================================================================
ONNX Runtime 1.17
Model: T5 Encoder - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 465.65 |===================================================================
b . 360.78 |====================================================
Llama.cpp b1808
Model: llama-2-70b-chat.Q5_0.gguf
Tokens Per Second > Higher Is Better
a . 0.43 |=====================================================================
b . 0.34 |=======================================================
Speedb 2.7
Test: Random Read
Op/s > Higher Is Better
a . 613257745 |================================================================
b . 490533153 |===================================================
Llama.cpp b1808
Model: llama-2-13b.Q4_0.gguf
Tokens Per Second > Higher Is Better
a . 0.55 |=====================================================================
b . 0.45 |========================================================
Llama.cpp b1808
Model: llama-2-7b.Q4_0.gguf
Tokens Per Second > Higher Is Better
a . 0.69 |=====================================================================
b . 0.58 |==========================================================
ONNX Runtime 1.17
Model: yolov4 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 15.38 |=============================================================
b . 17.17 |====================================================================
ONNX Runtime 1.17
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 35.92 |===============================================================
b . 38.56 |====================================================================
Speedb 2.7
Test: Read While Writing
Op/s > Higher Is Better
a . 16943739 |=============================================================
b . 18187110 |=================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: AlexNet
images/sec > Higher Is Better
a . 39.98 |====================================================================
b . 37.70 |================================================================
Quicksilver 20230818
Input: CORAL2 P2
Figure Of Merit > Higher Is Better
a . 8000000 |===============================================================
b . 8418000 |==================================================================
ONNX Runtime 1.17
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 9.90267 |==================================================================
b . 9.43170 |===============================================================
NAMD 3.0b6
Input: STMV with 1,066,628 Atoms
ns/day > Higher Is Better
a . 1.74622 |===============================================================
b . 1.81638 |==================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: ResNet-50
images/sec > Higher Is Better
a . 7.28 |=====================================================================
b . 7.01 |==================================================================
ONNX Runtime 1.17
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 257.00 |===================================================================
b . 247.67 |=================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: GoogLeNet
images/sec > Higher Is Better
a . 18.23 |====================================================================
b . 17.62 |==================================================================
Llamafile 0.6
Test: wizardcoder-python-34b-v1.0.Q6_K - Acceleration: CPU
Tokens Per Second > Higher Is Better
a . 3.74 |===================================================================
b . 3.86 |=====================================================================
TensorFlow 2.12
Device: CPU - Batch Size: 1 - Model: VGG-16
images/sec > Higher Is Better
a . 12.25 |====================================================================
b . 11.89 |==================================================================
Llamafile 0.6
Test: mistral-7b-instruct-v0.2.Q8_0 - Acceleration: CPU
Tokens Per Second > Higher Is Better
a . 8.57 |===================================================================
b . 8.81 |=====================================================================
Intel Open Image Denoise 2.2
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 5.14 |=====================================================================
b . 5.00 |===================================================================
Y-Cruncher 0.8.3
Pi Digits To Calculate: 1B
Seconds < Lower Is Better
a . 5.107 |==================================================================
b . 5.249 |====================================================================
GROMACS 2024
Implementation: MPI CPU - Input: water_GMX50_bare
Ns Per Day > Higher Is Better
a . 17.92 |==================================================================
b . 18.40 |====================================================================
Quicksilver 20230818
Input: CORAL2 P1
Figure Of Merit > Higher Is Better
a . 8625000 |=================================================================
b . 8820000 |==================================================================
Quicksilver 20230818
Input: CTS2
Figure Of Merit > Higher Is Better
a . 9556000 |==================================================================
b . 9354000 |=================================================================
ONNX Runtime 1.17
Model: GPT-2 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 205.56 |==================================================================
b . 208.99 |===================================================================
dav1d 1.4
Video Input: Chimera 1080p 10-bit
FPS > Higher Is Better
a . 235.42 |==================================================================
b . 239.01 |===================================================================
Y-Cruncher 0.8.3
Pi Digits To Calculate: 500M
Seconds < Lower Is Better
a . 2.723 |===================================================================
b . 2.761 |====================================================================
PyTorch 2.1
Device: CPU - Batch Size: 1 - Model: ResNet-50
batches/sec > Higher Is Better
a . 51.66 |====================================================================
b . 51.00 |===================================================================
ONNX Runtime 1.17
Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 38.33 |====================================================================
b . 37.88 |===================================================================
Intel Open Image Denoise 2.2
Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 5.10 |====================================================================
b . 5.16 |=====================================================================
dav1d 1.4
Video Input: Summer Nature 1080p
FPS > Higher Is Better
a . 87.78 |====================================================================
b . 86.79 |===================================================================
dav1d 1.4
Video Input: Chimera 1080p
FPS > Higher Is Better
a . 204.38 |===================================================================
b . 202.83 |==================================================================
PyTorch 2.1
Device: CPU - Batch Size: 1 - Model: ResNet-152
batches/sec > Higher Is Better
a . 19.08 |====================================================================
b . 19.21 |====================================================================
Intel Open Image Denoise 2.2
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
a . 2.46 |=====================================================================
b . 2.47 |=====================================================================
Speedb 2.7
Test: Read Random Write Random
Op/s > Higher Is Better
a . 1520436 |==================================================================
b . 1514331 |==================================================================
ONNX Runtime 1.17
Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 786.26 |===================================================================
b . 789.31 |===================================================================
Speedb 2.7
Test: Update Random
Op/s > Higher Is Better
a . 157060 |===================================================================
b . 156458 |===================================================================
ONNX Runtime 1.17
Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
a . 170.02 |===================================================================
b . 170.24 |===================================================================
dav1d 1.4
Video Input: Summer Nature 4K
FPS > Higher Is Better
a . 68.43 |====================================================================
b . 68.36 |====================================================================
Llamafile 0.6
Test: llava-v1.5-7b-q4 - Acceleration: CPU
Tokens Per Second > Higher Is Better
a . 0.53 |=====================================================================
b . 0.53 |=====================================================================
PyTorch 2.1
Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l
batches/sec > Higher Is Better
a . 0.42 |=====================================================================
ONNX Runtime 1.17
Model: Faster R-CNN R-50-FPN-int8 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 26.09 |===================================================================
b . 26.40 |====================================================================
ONNX Runtime 1.17
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 3.89053 |================================================================
b . 4.03712 |==================================================================
ONNX Runtime 1.17
Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 5.88091 |==================================================================
b . 5.87369 |==================================================================
ONNX Runtime 1.17
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 27.84 |====================================================================
b . 25.93 |===============================================================
ONNX Runtime 1.17
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 100.98 |================================================================
b . 106.02 |===================================================================
ONNX Runtime 1.17
Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 1.27117 |==================================================================
b . 1.26631 |==================================================================
ONNX Runtime 1.17
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 62.64 |====================================================================
b . 44.13 |================================================
ONNX Runtime 1.17
Model: T5 Encoder - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 2.14695 |===================================================
b . 2.77091 |==================================================================
ONNX Runtime 1.17
Model: yolov4 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 65.01 |====================================================================
b . 58.24 |=============================================================
ONNX Runtime 1.17
Model: GPT-2 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
a . 4.86151 |==================================================================
b . 4.78113 |=================================================================