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.
Compare your own system(s) to this result file with the
Phoronix Test Suite by running the command:
phoronix-test-suite benchmark 2312245-NE-DGGA0402643
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