xeon jan

Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 12 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 2401144-NE-XEONJAN1706

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BLAS (Basic Linear Algebra Sub-Routine) Tests ^{2 Tests}
CPU Massive ^{4 Tests}
HPC - High Performance Computing ^{5 Tests}
Machine Learning ^{5 Tests}
Python Tests ^{3 Tests}

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a

January 14

1 Hour, 57 Minutes

b

January 14

1 Hour, 56 Minutes

c

January 15

1 Hour, 57 Minutes

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xeon jan Intel Xeon Silver 4216 testing with a TYAN S7100AG2NR (V4.02 BIOS) and ASPEED on Debian 12 via the Phoronix Test Suite. ,,"a","b","c" Processor,,Intel Xeon Silver 4216 @ 3.20GHz (16 Cores / 32 Threads),Intel Xeon Silver 4216 @ 3.20GHz (16 Cores / 32 Threads),Intel Xeon Silver 4216 @ 3.20GHz (16 Cores / 32 Threads) Motherboard,,TYAN S7100AG2NR (V4.02 BIOS),TYAN S7100AG2NR (V4.02 BIOS),TYAN S7100AG2NR (V4.02 BIOS) Chipset,,Intel Sky Lake-E DMI3 Registers,Intel Sky Lake-E DMI3 Registers,Intel Sky Lake-E DMI3 Registers Memory,,6 x 8 GB DDR4-2400MT/s,6 x 8 GB DDR4-2400MT/s,6 x 8 GB DDR4-2400MT/s Disk,,240GB Corsair Force MP500,240GB Corsair Force MP500,240GB Corsair Force MP500 Graphics,,ASPEED,ASPEED,ASPEED Audio,,Realtek ALC892,Realtek ALC892,Realtek ALC892 Network,,2 x Intel I350,2 x Intel I350,2 x Intel I350 OS,,Debian 12,Debian 12,Debian 12 Kernel,,6.1.0-11-amd64 (x86_64),6.1.0-11-amd64 (x86_64),6.1.0-11-amd64 (x86_64) Display Server,,X Server,X Server,X Server Compiler,,GCC 12.2.0,GCC 12.2.0,GCC 12.2.0 File-System,,ext4,ext4,ext4 Screen Resolution,,1024x768,1024x768,1024x768 ,,"a","b","c" "CacheBench - Test: Read (MB/s)",HIB,6062.370107,6058.744667,6057.993007 "CacheBench - Test: Write (MB/s)",HIB,23161.605712,23134.972437,23165.587056 "CacheBench - Test: Read / Modify / Write (MB/s)",HIB,61680.563289,59877.337189,60843.704817 "TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,18.21,18.25,18.35 "TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,5.96,5.96,5.96 "TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,83.17,82.83,83.33 "TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,17.26,15.86,16.19 "TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,4.81,4.87,4.88 "TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,47.63,47.51,47.36 "Quicksilver - Input: CORAL2 P2 (Figure Of Merit)",HIB,9287000,9354000,9308000 "Quicksilver - Input: CORAL2 P1 (Figure Of Merit)",HIB,10170000,10110000,10150000 "Quicksilver - Input: CTS2 (Figure Of Merit)",HIB,8446000,8497000,8607000 "TensorFlow - Device: CPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,3.27,3.24,3.26 "TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,16.22,16.25,16.21 "LeelaChessZero - Backend: BLAS (Nodes/s)",HIB,37,38,37 "Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,20.623,20.682,20.575 "LeelaChessZero - Backend: Eigen (Nodes/s)",HIB,33,33,32 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,7.5161,7.2935,7.5416 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1061.8943,1073.1318,1060.5969 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,283.9607,286.9078,285.941 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,28.1401,27.8492,27.95 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,116.2812,116.0381,116.0803 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,68.7723,68.9149,68.8893 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,732.3634,736.8677,734.4208 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,10.9015,10.8351,10.8705 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,48.1923,48.2735,48.2187 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,165.9672,165.6879,165.8752 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,9.4169,9.4557,9.4646 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,845.9518,845.9952,845.2007 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,116.1827,115.8799,116.403 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,68.7595,68.9544,68.677 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,48.5709,48.598,48.652 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,164.6289,164.5919,164.0809 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,64.9099,64.5367,64.617 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,123.2229,123.8886,123.7819 "Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,46.091,45.445,45.928 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,14.384,14.5017,14.3009 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,552.534,549.3992,553.4687 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,125.9588,126.098,125.7734 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,63.4829,63.3871,63.5329 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,7.3201,7.5195,7.2847 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,1071.4101,1063.8379,1072.9766 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,29.51,29.64,28.89 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,11.11,11.17,11.07 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,21.57,21.27,21.73 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,21.56,21.64,21.74 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,8.14,8.13,8.22 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,8.18,8.09,8.08 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.91,6.90,6.95 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,4.72,4.76,4.70 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,4.79,4.75,4.75 "Llama.cpp - Model: llama-2-7b.Q4_0.gguf (Tokens/sec)",HIB,16.95,15.89,16.55 "Llama.cpp - Model: llama-2-13b.Q4_0.gguf (Tokens/sec)",HIB,8.7,8.73,8.62 "Llama.cpp - Model: llama-2-70b-chat.Q5_0.gguf (Tokens/sec)",HIB,1.5,1.51,1.5 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,2.462,2.423,2.421 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,24.384,23.99,24.069 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,82.805,78.544,82.222 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,82.392,82.623,83.269 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,7.326,7.379,7.251 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,45.633,46.686,45.935 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,165.265,170.98,168.012 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,188.993,184.724,187.023 "Speedb - Test: Random Fill (Op/s)",HIB,379730,298026,377206 "Speedb - Test: Random Read (Op/s)",HIB,53271554,52915603,52443533 "Speedb - Test: Update Random (Op/s)",HIB,172891,163726,151137 "Speedb - Test: Sequential Fill (Op/s)",HIB,565169,558662,549382 "Speedb - Test: Random Fill Sync (Op/s)",HIB,8962,13397,10150 "Speedb - Test: Read While Writing (Op/s)",HIB,3897119,3867484,4014397 "Speedb - Test: Read Random Write Random (Op/s)",HIB,1640953,1658156,1656172