dgga

AMD Ryzen Threadripper 3970X 32-Core testing with a ASUS ROG ZENITH II EXTREME (1802 BIOS) and AMD Radeon RX 5700 8GB on Ubuntu 22.04 via the Phoronix Test Suite.

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AMD Ryzen Threadripper 3970X 32-Core
February 02
  1 Hour, 58 Minutes
b
February 02
  1 Hour, 57 Minutes
c
February 02
  1 Hour, 57 Minutes
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dgga AMD Ryzen Threadripper 3970X 32-Core testing with a ASUS ROG ZENITH II EXTREME (1802 BIOS) and AMD Radeon RX 5700 8GB on Ubuntu 22.04 via the Phoronix Test Suite. ,,"AMD Ryzen Threadripper 3970X 32-Core","b","c" Processor,,AMD Ryzen Threadripper 3970X 32-Core @ 3.70GHz (32 Cores / 64 Threads),AMD Ryzen Threadripper 3970X 32-Core @ 3.70GHz (32 Cores / 64 Threads),AMD Ryzen Threadripper 3970X 32-Core @ 3.70GHz (32 Cores / 64 Threads) Motherboard,,ASUS ROG ZENITH II EXTREME (1802 BIOS),ASUS ROG ZENITH II EXTREME (1802 BIOS),ASUS ROG ZENITH II EXTREME (1802 BIOS) Chipset,,AMD Starship/Matisse,AMD Starship/Matisse,AMD Starship/Matisse Memory,,64GB,64GB,64GB Disk,,Samsung SSD 980 PRO 500GB,Samsung SSD 980 PRO 500GB,Samsung SSD 980 PRO 500GB Graphics,,AMD Radeon RX 5700 8GB (1750/875MHz),AMD Radeon RX 5700 8GB (1750/875MHz),AMD Radeon RX 5700 8GB (1750/875MHz) Audio,,AMD Navi 10 HDMI Audio,AMD Navi 10 HDMI Audio,AMD Navi 10 HDMI Audio Monitor,,ASUS VP28U,ASUS VP28U,ASUS VP28U Network,,Aquantia AQC107 NBase-T/IEEE + Intel I211 + Intel Wi-Fi 6 AX200,Aquantia AQC107 NBase-T/IEEE + Intel I211 + Intel Wi-Fi 6 AX200,Aquantia AQC107 NBase-T/IEEE + Intel I211 + Intel Wi-Fi 6 AX200 OS,,Ubuntu 22.04,Ubuntu 22.04,Ubuntu 22.04 Kernel,,6.2.0-36-generic (x86_64),6.2.0-36-generic (x86_64),6.2.0-36-generic (x86_64) Desktop,,GNOME Shell 42.2,GNOME Shell 42.2,GNOME Shell 42.2 Display Server,,X Server + Wayland,X Server + Wayland,X Server + Wayland OpenGL,,4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.49),4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.49),4.6 Mesa 22.0.1 (LLVM 13.0.1 DRM 3.49) Vulkan,,1.2.204,1.2.204,1.2.204 Compiler,,GCC 11.4.0,GCC 11.4.0,GCC 11.4.0 File-System,,ext4,ext4,ext4 Screen Resolution,,3840x2160,3840x2160,3840x2160 ,,"AMD Ryzen Threadripper 3970X 32-Core","b","c" "WebP2 Image Encode - Encode Settings: Quality 100, Lossless Compression (MP/s)",HIB,0.04,0.04,0.04 "PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,5.98,5.92,6.06 "PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,5.97,5.96,6.00 "PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.00,6.09,6.01 "Xmrig - Variant: GhostRider - Hash Count: 1M (H/s)",HIB,4771.3,4833.4,4784.7 "OpenSSL - Algorithm: ChaCha20-Poly1305 (byte/s)",HIB,86074896380,86178622410,85685137680 "OpenSSL - Algorithm: AES-256-GCM (byte/s)",HIB,139151548420,138940289020,139048226820 "OpenSSL - Algorithm: AES-128-GCM (byte/s)",HIB,152254146970,152243466510,152248762370 "OpenSSL - Algorithm: SHA256 (byte/s)",HIB,42506964290,42475560230,42542937860 "OpenSSL - Algorithm: ChaCha20 (byte/s)",HIB,132709569060,132535967470,132480014470 "OpenSSL - Algorithm: SHA512 (byte/s)",HIB,14922955600,14877687680,14860394950 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,11.85,11.59,11.69 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,11.79,11.65,11.95 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,11.76,11.79,11.71 "PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,7.89,7.95,7.93 "WebP2 Image Encode - Encode Settings: Quality 95, Compression Effort 7 (MP/s)",HIB,0.18,0.18,0.18 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,14.04,14.26,14.30 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,431.396,427.7464,431.935 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,37.0667,37.1773,36.9162 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.6407,11.6045,11.5257 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,85.8602,86.1225,86.7163 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,46.6936,47.0734,47.081 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,21.412,21.2394,21.2359 "PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,30.33,29.85,30.87 "PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,30.97,30.44,31.11 "PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,30.97,31.08,31.36 "WebP2 Image Encode - Encode Settings: Quality 75, Compression Effort 7 (MP/s)",HIB,0.36,0.36,0.36 "Xmrig - Variant: CryptoNight-Heavy - Hash Count: 1M (H/s)",HIB,16450.1,16398.3,16312.4 "Xmrig - Variant: KawPow - Hash Count: 1M (H/s)",HIB,16276.6,16472.3,16425.2 "Xmrig - Variant: CryptoNight-Femto UPX2 - Hash Count: 1M (H/s)",HIB,16317.7,16315.6,16555.2 "Xmrig - Variant: Monero - Hash Count: 1M (H/s)",HIB,16368.8,16429.8,16527 "OpenSSL - Algorithm: RSA4096 (verify/s)",HIB,609998,609586.6,609364.3 "OpenSSL - Algorithm: RSA4096 (sign/s)",HIB,9381,9368.1,9368 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.896,5.9607,5.9747 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,169.4233,167.5851,167.2022 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,23.712,23.7852,23.8232 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,674.1167,671.9488,670.8731 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,530.7568,539.6021,536.858 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,29.8842,29.5264,29.6241 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,522.4351,532.7014,536.3063 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,30.5082,29.9828,29.6746 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,46.2963,46.3817,46.4012 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,345.4208,344.6466,344.6426 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,57.041,58.2042,58.0081 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.5286,17.1783,17.2364 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,56.2287,57.1874,57.1103 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,17.7819,17.4838,17.5075 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,469.2415,476.8669,477.2306 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,33.9103,33.5294,33.4894 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,46.0342,46.4632,46.9683 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,21.7159,21.5157,21.284 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,64.7241,65.1634,64.9519 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,247.0871,245.4336,246.1586 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,11.0925,11.9675,11.9026 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,90.0923,83.5111,83.9665 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,8.7652,8.8357,8.821 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1820.1556,1805.6704,1808.3307 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,103.4836,107.1318,107.1516 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,154.4159,149.1863,149.2703 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.8073,10.904,10.9073 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,92.4279,91.6072,91.5797 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,10.5994,10.6301,10.6602 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,94.279,94.009,93.7414 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,49.4836,49.8705,49.9441 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,323.1865,320.683,320.2104 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,101.1265,101.838,100.8112 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,158.0562,157.0528,158.4441 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,4.712,4.699,4.797 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,6.894,6.8579,7.0064 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,144.8781,145.6431,142.5611 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,6.5694,6.7922,6.6191 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,152.0352,147.051,150.8915 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,49.16,49.2645,49.3431 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,325.3167,324.6279,324.1138 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,1.3008,1.324,1.3006 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,766.4844,752.7752,766.4 "Xmrig - Variant: Wownero - Hash Count: 1M (H/s)",HIB,27698.5,28017.5,28103.3 "PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,35.38,36.22,35.95 "Java SciMark - Computational Test: Composite (Mflops)",HIB,3628.54,3500.03,3630.68 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,33.989,33.9219,33.9649 "Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,37.7699,37.5554,37.5529 "Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,34.8277,34.6107,34.7267 "Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,38.6634,38.6826,38.788 "Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,39.4881,39.3303,39.3408 "Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,41.9049,41.9584,41.9227 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,44.813,44.26,44.897 "SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,12.238,12.17,12.173 "SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,77.466,77.744,77.763 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,141.072,143.719,137.23 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,139.049,140.202,141.265 "SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,302.256,303.201,303.029 "SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,361.391,358.894,353.489 "WebP2 Image Encode - Encode Settings: Quality 100, Compression Effort 5 (MP/s)",HIB,7.89,7.99,7.78 "WebP2 Image Encode - Encode Settings: Default (MP/s)",HIB,9.74,9.47,9.77 "Java SciMark - Computational Test: Jacobi Successive Over-Relaxation (Mflops)",HIB,1822.2,1823.29,1824.92 "Java SciMark - Computational Test: Dense LU Matrix Factorization (Mflops)",HIB,10937.37,11007.91,11024.98 "Java SciMark - Computational Test: Sparse Matrix Multiply (Mflops)",HIB,3259.85,2552.03,3252.08 "Java SciMark - Computational Test: Fast Fourier Transform (Mflops)",HIB,460.52,452.84,447.63 "Java SciMark - Computational Test: Monte Carlo (Mflops)",HIB,1662.78,1664.07,1603.8