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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.

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a
December 24 2023
  1 Day, 26 Minutes
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December 25 2023
  7 Hours, 39 Minutes
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eps 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","b" Processor,,2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads),2 x AMD EPYC 9684X 96-Core @ 2.55GHz (192 Cores / 384 Threads) Motherboard,,AMD Titanite_4G (RTI1007B BIOS),AMD Titanite_4G (RTI1007B BIOS) Chipset,,AMD Device 14a4,AMD Device 14a4 Memory,,1520GB,1520GB Disk,,3201GB Micron_7450_MTFDKCB3T2TFS,3201GB Micron_7450_MTFDKCB3T2TFS Graphics,,ASPEED,ASPEED Network,,Broadcom NetXtreme BCM5720 PCIe,Broadcom NetXtreme BCM5720 PCIe OS,,Ubuntu 23.10,Ubuntu 23.10 Kernel,,6.5.0-13-generic (x86_64),6.5.0-13-generic (x86_64) Compiler,,GCC 13.2.0,GCC 13.2.0 File-System,,ext4,ext4 Screen Resolution,,800x600,800x600 ,,"a","b" "OpenSSL - Algorithm: ChaCha20 ()",HIB,, "OpenSSL - Algorithm: AES-128-GCM ()",HIB,, "OpenSSL - Algorithm: AES-256-GCM ()",HIB,, "OpenSSL - Algorithm: ChaCha20-Poly1305 ()",HIB,, "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,23.57,23.12 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,10.16,10.43 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,21.16,21.57 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,21.00,21.09 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,8.93,8.97 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-50 (batches/sec)",HIB,21.29,20.60 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,8.90,8.98 "PyTorch - Device: CPU - Batch Size: 256 - Model: ResNet-152 (batches/sec)",HIB,8.96,9.65 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.40,6.74 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.32,2.34 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.32,2.32 "PyTorch - Device: CPU - Batch Size: 256 - Model: Efficientnet_v2_l (batches/sec)",HIB,2.32,2.28 "OpenSSL - Algorithm: SHA256 (byte/s)",HIB,281869895760,282211175400 "OpenSSL - Algorithm: SHA512 (byte/s)",HIB,91630925473,91835961470 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,8.248,8.208 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,86.434,86.841 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,178.910,186.609 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,176.670,184.347 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,21.424,21.313 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,165.104,162.561 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,571.875,569.955 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,635.810,639.088 "Xmrig - Variant: KawPow - Hash Count: 1M (H/s)",HIB,123558.6,123411.1 "Xmrig - Variant: Monero - Hash Count: 1M (H/s)",HIB,123352.8,122971 "Xmrig - Variant: Wownero - Hash Count: 1M (H/s)",HIB,131141.9,131613.6 "Xmrig - Variant: GhostRider - Hash Count: 1M (H/s)",HIB,31859.7,31728.9 "Xmrig - Variant: CryptoNight-Heavy - Hash Count: 1M (H/s)",HIB,123041.6,123777.7 "Xmrig - Variant: CryptoNight-Femto UPX2 - Hash Count: 1M (H/s)",HIB,123199.0,122070.3 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,132.6580,132.2719 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,48.4476,48.3264 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,5540.6268,5540.517 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,190.7999,191.0971 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1758.5931,1756.5569 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,209.7998,206.969 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,17108.4634,17047.639 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,804.1784,804.7528 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,784.5178,786.8905 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,211.7448,211.7729 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,156.4159,156.4283 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,32.0229,31.9795 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1761.4041,1759.0746 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,208.1200,209.1955 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,796.0713,797.4124 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,212.0955,212.1305 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1136.7105,1136.6439 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,224.5798,225.4047 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,248.5770,249.4983 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,65.2070,65.0079 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,2608.0090,2596.0961 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,68.2655,68.2636 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,132.0485,132.4219 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,48.4917,48.332 "Java SciMark - Computational Test: Composite (Mflops)",HIB,3984.62,3996.76 "Java SciMark - Computational Test: Monte Carlo (Mflops)",HIB,1631.42,1632.45 "Java SciMark - Computational Test: Fast Fourier Transform (Mflops)",HIB,420.74,421.91 "Java SciMark - Computational Test: Sparse Matrix Multiply (Mflops)",HIB,2809.01,2792.09 "Java SciMark - Computational Test: Dense LU Matrix Factorization (Mflops)",HIB,13358.53,13434.09 "Java SciMark - Computational Test: Jacobi Successive Over-Relaxation (Mflops)",HIB,1703.42,1703.25 "WebP2 Image Encode - Encode Settings: Default (MP/s)",HIB,9.48,9.63 "WebP2 Image Encode - Encode Settings: Quality 75, Compression Effort 7 (MP/s)",HIB,0.83,0.82 "WebP2 Image Encode - Encode Settings: Quality 95, Compression Effort 7 (MP/s)",HIB,0.45,0.45 "WebP2 Image Encode - Encode Settings: Quality 100, Compression Effort 5 (MP/s)",HIB,6.51,6.28 "WebP2 Image Encode - Encode Settings: Quality 100, Lossless Compression (MP/s)",HIB,0.11,0.11 "LeelaChessZero - Backend: BLAS (Nodes/s)",HIB,853,871 "LeelaChessZero - Backend: Eigen (Nodes/s)",HIB,704,715 "OpenSSL - Algorithm: RSA4096 (sign/s)",HIB,98622.0,98528.8 "OpenSSL - Algorithm: RSA4096 (verify/s)",HIB,3244390.3,3243345.2 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,715.0362,717.5936 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,20.6345,20.686 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,17.3023,17.3019 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.2377,5.2296 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,54.5064,54.5546 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,4.7637,4.829 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,5.5955,5.6159 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,1.2413,1.2404 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,122.0312,121.6067 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,4.7188,4.718 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,607.5664,607.5735 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,31.2154,31.2575 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,54.4097,54.4859 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,4.8022,4.7775 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,120.2065,120.0373 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,4.7126,4.7117 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,84.2519,84.2491 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,4.4503,4.4341 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,383.2004,382.5561 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,15.3183,15.3653 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,36.7508,36.9146 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,14.6422,14.6426 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,719.2814,717.9791 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,20.6157,20.6837 "Apache Spark TPC-H - Scale Factor: 1 - Geometric Mean Of All Queries (sec)",LIB,2.44964916,2.49517747 "Apache Spark TPC-H - Scale Factor: 1 - Q01 (sec)",LIB,4.32006081,4.44657946 "Apache Spark TPC-H - Scale Factor: 1 - Q02 (sec)",LIB,2.06179071,2.08224201 "Apache Spark TPC-H - Scale Factor: 1 - Q03 (sec)",LIB,3.86442184,3.86610818 "Apache Spark TPC-H - Scale Factor: 1 - Q04 (sec)",LIB,3.92525745,3.75427246 "Apache Spark TPC-H - Scale Factor: 1 - Q05 (sec)",LIB,4.13122161,3.69217634 "Apache Spark TPC-H - Scale Factor: 1 - Q06 (sec)",LIB,0.46822915,0.35801557 "Apache Spark TPC-H - Scale Factor: 1 - Q07 (sec)",LIB,4.01044806,3.87790275 "Apache Spark TPC-H - Scale Factor: 1 - Q08 (sec)",LIB,2.65584644,2.60907817 "Apache Spark TPC-H - Scale Factor: 1 - Q09 (sec)",LIB,5.70969407,5.89775848 "Apache Spark TPC-H - Scale Factor: 1 - Q10 (sec)",LIB,3.81359665,3.81245542 "Apache Spark TPC-H - Scale Factor: 1 - Q11 (sec)",LIB,1.27338135,1.13998687 "Apache Spark TPC-H - Scale Factor: 1 - Q12 (sec)",LIB,2.17542648,2.26641607 "Apache Spark TPC-H - Scale Factor: 1 - Q13 (sec)",LIB,1.58815936,1.74074161 "Apache Spark TPC-H - Scale Factor: 1 - Q14 (sec)",LIB,2.06485331,2.21146965 "Apache Spark TPC-H - Scale Factor: 1 - Q15 (sec)",LIB,2.50185966,2.58714175 "Apache Spark TPC-H - Scale Factor: 1 - Q16 (sec)",LIB,1.38147259,1.51779914 "Apache Spark TPC-H - Scale Factor: 1 - Q17 (sec)",LIB,2.95993924,2.88348198 "Apache Spark TPC-H - Scale Factor: 1 - Q18 (sec)",LIB,5.62853845,5.13171148 "Apache Spark TPC-H - Scale Factor: 1 - Q19 (sec)",LIB,0.79092395,0.85797596 "Apache Spark TPC-H - Scale Factor: 1 - Q20 (sec)",LIB,3.05739617,3.05001688 "Apache Spark TPC-H - Scale Factor: 1 - Q21 (sec)",LIB,9.64531231,9.55909538 "Apache Spark TPC-H - Scale Factor: 1 - Q22 (sec)",LIB,1.00769047,1.06679213 "Apache Spark TPC-H - Scale Factor: 10 - Geometric Mean Of All Queries (sec)",LIB,10.72150208,10.65793942 "Apache Spark TPC-H - Scale Factor: 10 - Q01 (sec)",LIB,7.58889151,7.28826714 "Apache Spark TPC-H - Scale Factor: 10 - Q02 (sec)",LIB,7.43104283,7.39245987 "Apache Spark TPC-H - Scale Factor: 10 - Q03 (sec)",LIB,13.97308763,14.28984642 "Apache Spark TPC-H - Scale Factor: 10 - Q04 (sec)",LIB,12.34571203,11.26242161 "Apache Spark TPC-H - Scale Factor: 10 - Q05 (sec)",LIB,16.44365629,18.86129189 "Apache Spark TPC-H - Scale Factor: 10 - Q06 (sec)",LIB,2.05104745,1.8559593 "Apache Spark TPC-H - Scale Factor: 10 - Q07 (sec)",LIB,14.65200933,14.89605904 "Apache Spark TPC-H - Scale Factor: 10 - Q08 (sec)",LIB,15.51824761,14.55769348 "Apache Spark TPC-H - Scale Factor: 10 - Q09 (sec)",LIB,21.90670204,22.52552795 "Apache Spark TPC-H - Scale Factor: 10 - Q10 (sec)",LIB,15.17488098,14.77719498 "Apache Spark TPC-H - Scale Factor: 10 - Q11 (sec)",LIB,8.00292349,8.27814293 "Apache Spark TPC-H - Scale Factor: 10 - Q12 (sec)",LIB,9.94400438,10.03829002 "Apache Spark TPC-H - Scale Factor: 10 - Q13 (sec)",LIB,7.37728373,7.94083786 "Apache Spark TPC-H - Scale Factor: 10 - Q14 (sec)",LIB,7.07622369,6.90602303 "Apache Spark TPC-H - Scale Factor: 10 - Q15 (sec)",LIB,5.84138076,5.43870592 "Apache Spark TPC-H - Scale Factor: 10 - Q16 (sec)",LIB,6.87131294,6.95270681 "Apache Spark TPC-H - Scale Factor: 10 - Q17 (sec)",LIB,12.77044550,13.01374149 "Apache Spark TPC-H - Scale Factor: 10 - Q18 (sec)",LIB,18.46971194,17.31370163 "Apache Spark TPC-H - Scale Factor: 10 - Q19 (sec)",LIB,6.20677837,6.0604167 "Apache Spark TPC-H - Scale Factor: 10 - Q20 (sec)",LIB,11.43560823,11.53966141 "Apache Spark TPC-H - Scale Factor: 10 - Q21 (sec)",LIB,32.90715027,32.70154953 "Apache Spark TPC-H - Scale Factor: 10 - Q22 (sec)",LIB,6.05411895,6.04430914 "Apache Spark TPC-H - Scale Factor: 50 - Geometric Mean Of All Queries (sec)",LIB,19.58745807,19.56475658 "Apache Spark TPC-H - Scale Factor: 50 - Q01 (sec)",LIB,12.00795619,12.86835003 "Apache Spark TPC-H - Scale Factor: 50 - Q02 (sec)",LIB,14.25487200,14.53046799 "Apache Spark TPC-H - Scale Factor: 50 - Q03 (sec)",LIB,26.18788719,29.68590546 "Apache Spark TPC-H - Scale Factor: 50 - Q04 (sec)",LIB,20.99598312,21.8167572 "Apache Spark TPC-H - Scale Factor: 50 - Q05 (sec)",LIB,29.83627891,31.20059776 "Apache Spark TPC-H - Scale Factor: 50 - Q06 (sec)",LIB,5.90309207,5.88382483 "Apache Spark TPC-H - Scale Factor: 50 - Q07 (sec)",LIB,24.85711161,25.86055183 "Apache Spark TPC-H - Scale Factor: 50 - Q08 (sec)",LIB,26.73535283,26.62909508 "Apache Spark TPC-H - Scale Factor: 50 - Q09 (sec)",LIB,36.66458511,36.66526794 "Apache Spark TPC-H - Scale Factor: 50 - Q10 (sec)",LIB,24.36003748,24.68585587 "Apache Spark TPC-H - Scale Factor: 50 - Q11 (sec)",LIB,13.58028253,13.31200027 "Apache Spark TPC-H - Scale Factor: 50 - Q12 (sec)",LIB,19.40537771,17.70001793 "Apache Spark TPC-H - Scale Factor: 50 - Q13 (sec)",LIB,12.75901413,13.04496479 "Apache Spark TPC-H - Scale Factor: 50 - Q14 (sec)",LIB,12.70455011,12.56767082 "Apache Spark TPC-H - Scale Factor: 50 - Q15 (sec)",LIB,9.77733866,9.48287773 "Apache Spark TPC-H - Scale Factor: 50 - Q16 (sec)",LIB,14.21570397,14.97535801 "Apache Spark TPC-H - Scale Factor: 50 - Q17 (sec)",LIB,24.30927912,24.55788994 "Apache Spark TPC-H - Scale Factor: 50 - Q18 (sec)",LIB,34.51305643,33.74198151 "Apache Spark TPC-H - Scale Factor: 50 - Q19 (sec)",LIB,10.45287259,12.08592796 "Apache Spark TPC-H - Scale Factor: 50 - Q20 (sec)",LIB,20.79876137,21.05384445 "Apache Spark TPC-H - Scale Factor: 50 - Q21 (sec)",LIB,87.89528910,77.70675659 "Apache Spark TPC-H - Scale Factor: 50 - Q22 (sec)",LIB,10.69325638,10.87410069