Tests for a future article. AMD EPYC 8534PN 64-Core testing with a AMD Cinnabar (RCB1009C 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 2401089-NE-DDF54911740
ddf
Tests for a future article. AMD EPYC 8534PN 64-Core testing with a AMD Cinnabar (RCB1009C BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.
,,"a","b"
Processor,,AMD EPYC 8534PN 64-Core @ 2.00GHz (64 Cores / 128 Threads),AMD EPYC 8534PN 64-Core @ 2.00GHz (64 Cores / 128 Threads)
Motherboard,,AMD Cinnabar (RCB1009C BIOS),AMD Cinnabar (RCB1009C BIOS)
Chipset,,AMD Device 14a4,AMD Device 14a4
Memory,,192GB,192GB
Disk,,3201GB Micron_7450_MTFDKCB3T2TFS,3201GB Micron_7450_MTFDKCB3T2TFS
Graphics,,ASPEED,ASPEED
Network,,2 x Broadcom NetXtreme BCM5720 PCIe,2 x Broadcom NetXtreme BCM5720 PCIe
OS,,Ubuntu 23.10,Ubuntu 23.10
Kernel,,6.5.0-5-generic (x86_64),6.5.0-5-generic (x86_64)
Desktop,,GNOME Shell,GNOME Shell
Display Server,,X Server 1.21.1.7,X Server 1.21.1.7
Compiler,,GCC 13.2.0,GCC 13.2.0
File-System,,ext4,ext4
Screen Resolution,,640x480,640x480
,,"a","b"
"Blender - Blend File: BMW27 - Compute: CPU-Only (sec)",LIB,26.71,26.75
"Blender - Blend File: Classroom - Compute: CPU-Only (sec)",LIB,67.06,67.51
"Blender - Blend File: Fishy Cat - Compute: CPU-Only (sec)",LIB,35.54,35.33
"Blender - Blend File: Barbershop - Compute: CPU-Only (sec)",LIB,239.83,240.12
"Blender - Blend File: Pabellon Barcelona - Compute: CPU-Only (sec)",LIB,86.14,86.16
"CloverLeaf - Input: clover_bm (sec)",LIB,13.67,13.93
"CloverLeaf - Input: clover_bm64_short (sec)",LIB,57.21,57.09
"easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 (sec)",LIB,1.947,1.958
"easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 (sec)",LIB,39.643,39.484
"easyWave - Input: e2Asean Grid + BengkuluSept2007 Source - Time: 2400 (sec)",LIB,111.038,111.072
"Embree - Binary: Pathtracer - Model: Crown (FPS)",HIB,67.9313,67.1138
"Embree - Binary: Pathtracer ISPC - Model: Crown (FPS)",HIB,69.2031,68.5827
"Embree - Binary: Pathtracer - Model: Asian Dragon (FPS)",HIB,77.2581,77.103
"Embree - Binary: Pathtracer - Model: Asian Dragon Obj (FPS)",HIB,69.0867,68.5948
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon (FPS)",HIB,83.8771,83.5831
"Embree - Binary: Pathtracer ISPC - Model: Asian Dragon Obj (FPS)",HIB,71.4939,71.4428
"FFmpeg - Encoder: libx265 - Scenario: Live (FPS)",HIB,114.74,115.73
"FFmpeg - Encoder: libx265 - Scenario: Upload (FPS)",HIB,23.20,23.29
"FFmpeg - Encoder: libx265 - Scenario: Platform (FPS)",HIB,47.15,47.07
"FFmpeg - Encoder: libx265 - Scenario: Video On Demand (FPS)",HIB,47.03,47.05
"LeelaChessZero - Backend: BLAS (Nodes/s)",HIB,315,354
"LeelaChessZero - Backend: Eigen (Nodes/s)",HIB,272,282
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,36.8118,37.0225
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,852.1762,853.3085
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,27.5732,27.5404
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,36.2588,36.3016
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1458.049,1454.9899
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,21.9243,21.9623
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,195.6,191.4824
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.1074,5.217
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,486.6043,485.7323
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,65.6643,65.7823
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,185.3007,186.2351
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.3901,5.3631
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,3813.6379,3800.6772
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,8.3709,8.399
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,800.5916,799.9581
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,1.2456,1.2465
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,220.0706,220.1545
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,145.021,144.9123
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (items/sec)",HIB,145.6232,145.1485
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream (ms/batch)",LIB,6.8554,6.8775
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,46.9635,46.8626
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,673.3343,674.9861
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (items/sec)",HIB,30.3213,30.1617
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream (ms/batch)",LIB,32.9699,33.1445
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,485.9989,485.8785
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,65.6917,65.7405
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,185.9864,184.5078
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,5.3704,5.4136
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,222.2492,221.7174
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,143.5315,143.8442
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,147.2472,147.1485
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,6.7843,6.7891
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,323.0662,322.9189
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,98.8186,98.8586
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,130.9206,131.4576
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,7.6305,7.5994
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,64.0666,63.9058
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,496.3373,497.6216
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,41.0087,40.9888
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,24.3612,24.3739
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,698.3574,695.3481
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,45.7574,45.9477
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,63.2873,63.3431
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,15.7849,15.77
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,37.088,37.16
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,854.1221,853.694
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,27.5048,27.5295
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,36.3487,36.316
"OpenRadioss - Model: Bumper Beam (sec)",LIB,88.11,88.16
"OpenRadioss - Model: Chrysler Neon 1M (sec)",LIB,297.08,295.72
"OpenRadioss - Model: Cell Phone Drop Test (sec)",LIB,31.94,31.84
"OpenRadioss - Model: Bird Strike on Windshield (sec)",LIB,143.85,142.38
"OpenRadioss - Model: Rubber O-Ring Seal Installation (sec)",LIB,76.78,76.1
"OpenRadioss - Model: INIVOL and Fluid Structure Interaction Drop Container (sec)",LIB,164.67,163.46
"PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-50 (batches/sec)",HIB,45.19,45.42
"PyTorch - Device: CPU - Batch Size: 1 - Model: ResNet-152 (batches/sec)",HIB,16.54,16.58
"PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-50 (batches/sec)",HIB,36.12,36.35
"PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-50 (batches/sec)",HIB,36.66,36.38
"PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-50 (batches/sec)",HIB,36.87,36.29
"PyTorch - Device: CPU - Batch Size: 16 - Model: ResNet-152 (batches/sec)",HIB,14.70,14.18
"PyTorch - Device: CPU - Batch Size: 32 - Model: ResNet-152 (batches/sec)",HIB,14.37,14.13
"PyTorch - Device: CPU - Batch Size: 64 - Model: ResNet-152 (batches/sec)",HIB,14.89,14.61
"PyTorch - Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l (batches/sec)",HIB,9.53,9.61
"PyTorch - Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.00,5.98
"PyTorch - Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.06,6.08
"PyTorch - Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l (batches/sec)",HIB,6.03,6.03
"QuantLib - Configuration: Multi-Threaded (MFLOPS)",HIB,176928.1,170381.3
"QuantLib - Configuration: Single-Threaded (MFLOPS)",HIB,2634.4,2633.8
"Quicksilver - Input: CTS2 (Figure Of Merit)",HIB,16240000,16310000
"Quicksilver - Input: CORAL2 P1 (Figure Of Merit)",HIB,21350000,21280000
"Quicksilver - Input: CORAL2 P2 (Figure Of Merit)",HIB,16170000,16190000
"rav1e - Speed: 1 (FPS)",HIB,0.85,0.85
"rav1e - Speed: 5 (FPS)",HIB,3.584,3.596
"rav1e - Speed: 6 (FPS)",HIB,4.851,4.891
"rav1e - Speed: 10 (FPS)",HIB,12.32,12.36
"Speedb - Test: Random Fill (Op/s)",HIB,369023,367684
"Speedb - Test: Random Read (Op/s)",HIB,303866048,304143127
"Speedb - Test: Update Random (Op/s)",HIB,350612,361192
"Speedb - Test: Sequential Fill (Op/s)",HIB,371857,369178
"Speedb - Test: Random Fill Sync (Op/s)",HIB,239469,244535
"Speedb - Test: Read While Writing (Op/s)",HIB,15411684,15231425
"Speedb - Test: Read Random Write Random (Op/s)",HIB,2539184,2539687
"SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,6.628,6.75
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,67.98,68.523
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,190.949,194.49
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,187.077,194.587
"SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,17.075,17.467
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,131.9,129.181
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,511.853,503.618
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,597.058,601.573
"TensorFlow - Device: CPU - Batch Size: 1 - Model: VGG-16 (images/sec)",HIB,9.87,9.87
"TensorFlow - Device: CPU - Batch Size: 1 - Model: AlexNet (images/sec)",HIB,30.46,30.45
"TensorFlow - Device: CPU - Batch Size: 16 - Model: VGG-16 (images/sec)",HIB,35.21,35.14
"TensorFlow - Device: CPU - Batch Size: 16 - Model: AlexNet (images/sec)",HIB,299.15,297.84
"TensorFlow - Device: CPU - Batch Size: 1 - Model: GoogLeNet (images/sec)",HIB,17,17
"TensorFlow - Device: CPU - Batch Size: 1 - Model: ResNet-50 (images/sec)",HIB,5.97,5.97
"TensorFlow - Device: CPU - Batch Size: 16 - Model: GoogLeNet (images/sec)",HIB,155.77,155.78
"TensorFlow - Device: CPU - Batch Size: 16 - Model: ResNet-50 (images/sec)",HIB,51.06,50.8
"Timed FFmpeg Compilation - Time To Compile (sec)",LIB,18.145,18.042
"Timed Gem5 Compilation - Time To Compile (sec)",LIB,211.562,223.062
"WebP2 Image Encode - Encode Settings: Default (MP/s)",HIB,7.44,7.49
"WebP2 Image Encode - Encode Settings: Quality 75, Compression Effort 7 (MP/s)",HIB,0.54,0.51
"WebP2 Image Encode - Encode Settings: Quality 95, Compression Effort 7 (MP/s)",HIB,0.27,0.26
"WebP2 Image Encode - Encode Settings: Quality 100, Compression Effort 5 (MP/s)",HIB,11.63,11.68
"WebP2 Image Encode - Encode Settings: Quality 100, Lossless Compression (MP/s)",HIB,0.06,0.06
"Xmrig - Variant: KawPow - Hash Count: 1M (H/s)",HIB,20682.1,20732.7
"Xmrig - Variant: Monero - Hash Count: 1M (H/s)",HIB,20714.7,20732.3
"Xmrig - Variant: Wownero - Hash Count: 1M (H/s)",HIB,40330.7,40146.1
"Xmrig - Variant: GhostRider - Hash Count: 1M (H/s)",HIB,4436.2,4442.9
"Xmrig - Variant: CryptoNight-Heavy - Hash Count: 1M (H/s)",HIB,20666.7,20071.9
"Xmrig - Variant: CryptoNight-Femto UPX2 - Hash Count: 1M (H/s)",HIB,20698.4,20683
"Y-Cruncher - Pi Digits To Calculate: 1B (sec)",LIB,10.245,10.202
"Y-Cruncher - Pi Digits To Calculate: 5B (sec)",LIB,53.068,53.146
"Y-Cruncher - Pi Digits To Calculate: 10B (sec)",LIB,112.543,112.813
"Y-Cruncher - Pi Digits To Calculate: 500M (sec)",LIB,5.11,5.106