ARMv8 Neoverse-N1 testing with a GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 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 2403174-NE-N1N13670960
n1n1
ARMv8 Neoverse-N1 testing with a GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS) and ASPEED on Ubuntu 23.10 via the Phoronix Test Suite.
,,"a","aa","b","c"
Processor,,ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores),ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores),ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores),ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores)
Motherboard,,GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS),GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS),GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS),GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS)
Chipset,,Ampere Computing LLC Altra PCI Root Complex A,Ampere Computing LLC Altra PCI Root Complex A,Ampere Computing LLC Altra PCI Root Complex A,Ampere Computing LLC Altra PCI Root Complex A
Memory,,16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE,16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE,16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE,16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE
Disk,,800GB Micron_7450_MTFDKBA800TFS,800GB Micron_7450_MTFDKBA800TFS,800GB Micron_7450_MTFDKBA800TFS,800GB Micron_7450_MTFDKBA800TFS
Graphics,,ASPEED,ASPEED,ASPEED,ASPEED
Monitor,,VGA HDMI,VGA HDMI,VGA HDMI,VGA HDMI
Network,,2 x Intel I350,2 x Intel I350,2 x Intel I350,2 x Intel I350
OS,,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10,Ubuntu 23.10
Kernel,,6.5.0-15-generic (aarch64),6.5.0-15-generic (aarch64),6.5.0-15-generic (aarch64),6.5.0-15-generic (aarch64)
Compiler,,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0,GCC 13.2.0
File-System,,ext4,ext4,ext4,ext4
Screen Resolution,,1024x768,1024x768,1024x768,1024x768
,,"a","aa","b","c"
"WavPack Audio Encoding - WAV To WavPack (sec)",LIB,,25.199,25.205,25.2
"Google Draco - Model: Lion (ms)",LIB,,7351,7320,7332
"Google Draco - Model: Church Facade (ms)",LIB,,10100,9847,9848
"JPEG-XL Decoding libjxl - CPU Threads: 1 (MP/s)",HIB,27.237,27.152,27.417,27.396
"JPEG-XL Decoding libjxl - CPU Threads: All (MP/s)",HIB,558.569,523.019,564.893,542.103
"JPEG-XL libjxl - Input: PNG - Quality: 80 (MP/s)",HIB,43.097,40.279,41.309,41.354
"JPEG-XL libjxl - Input: PNG - Quality: 90 (MP/s)",HIB,39.249,37.895,39.669,39.251
"JPEG-XL libjxl - Input: JPEG - Quality: 80 (MP/s)",HIB,39.268,38.921,37.766,39.315
"JPEG-XL libjxl - Input: JPEG - Quality: 90 (MP/s)",HIB,37.591,37.415,37.79,35.843
"JPEG-XL libjxl - Input: PNG - Quality: 100 (MP/s)",HIB,29.603,29.238,29.494,29.544
"JPEG-XL libjxl - Input: JPEG - Quality: 100 (MP/s)",HIB,31.665,31.121,31.621,31.624
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,33.4187,33.7125,33.6797
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,1844.1246,1834.8257,1833.4487
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (items/sec)",HIB,,26.0871,25.9453,26.3315
"Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,38.3162,38.5246,37.9597
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,1149.4724,1144.8012,1144.7727
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,55.0261,55.2555,55.2435
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,,132.1849,132.9867,131.4797
"Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,7.5520,7.5061,7.5924
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,474.8976,475.8212,479.9901
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,132.9525,132.7356,131.5237
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (items/sec)",HIB,,133.5301,134.0016,133.4866
"Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,7.4741,7.4484,7.4767
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,2678.2382,2688.9567,2630.334
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,23.5039,23.4116,23.9126
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,,315.7450,312.4633,316.347
"Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,3.1508,3.1835,3.1449
"Neural Magic DeepSparse - Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,2.2602,2.2754,2.2836
"Neural Magic DeepSparse - Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,21332.8931,21231.5641,21169.2953
"Neural Magic DeepSparse - Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream (items/sec)",HIB,,12.9298,12.8977,12.9605
"Neural Magic DeepSparse - Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,77.3026,77.4941,77.119
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,476.3557,478.6418,478.3732
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,132.5425,131.9529,131.8594
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (items/sec)",HIB,,133.6218,133.6253,133.8853
"Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,7.4691,7.4692,7.454
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,202.6359,202.1471,201.0244
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,310.5129,311.1676,312.7909
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,,112.5334,112.8291,112.8521
"Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,8.8709,8.8483,8.8461
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,345.1080,346.6699,339.9
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,182.8789,181.8956,185.7196
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (items/sec)",HIB,,109.9523,109.4784,111.1578
"Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,9.0819,9.1198,8.982
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,46.6120,46.715,46.6799
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,1337.5918,1335.3456,1333.7177
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (items/sec)",HIB,,30.5961,30.7211,30.6675
"Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,32.6597,32.5272,32.5835
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,438.7131,439.6023,438.2501
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,143.8317,143.4837,143.6025
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (items/sec)",HIB,,50.5976,50.7328,50.649
"Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,19.7459,19.6933,19.7262
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,,33.5337,33.5843,33.6663
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,,1840.3677,1835.2572,1836.793
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (items/sec)",HIB,,26.2531,26.3468,26.2002
"Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream (ms/batch)",LIB,,38.0726,37.9374,38.1495
"oneDNN - Harness: IP Shapes 1D - Engine: CPU (ms)",LIB,,4.84065,4.88015,4.88858
"oneDNN - Harness: IP Shapes 3D - Engine: CPU (ms)",LIB,,2.15582,2.15178,2.14878
"oneDNN - Harness: Convolution Batch Shapes Auto - Engine: CPU (ms)",LIB,,4.29470,4.28036,4.28461
"oneDNN - Harness: Deconvolution Batch shapes_1d - Engine: CPU (ms)",LIB,,20.9255,20.4308,20.8925
"oneDNN - Harness: Deconvolution Batch shapes_3d - Engine: CPU (ms)",LIB,,2.79626,2.78238,2.80386
"oneDNN - Harness: Recurrent Neural Network Training - Engine: CPU (ms)",LIB,,3738.39,3737.15,3738.53
"oneDNN - Harness: Recurrent Neural Network Inference - Engine: CPU (ms)",LIB,,1460.94,1461,1469.65
"OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,,2.84,2.84,2.84
"OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,,10877.53,10891.93,10876.7
"OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,,14.77,14.77,14.73
"OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,,2150.30,2151.85,2157.45
"OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,,14.73,14.84,14.8
"OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,,2156.87,2140.2,2146.07
"OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,,222.86,223.85,222.78
"OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,,143.42,142.79,143.48
"OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,,2.74,2.75,2.75
"OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,,11232.43,11206.13,11196.54
"OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,,676.59,664.78,670.19
"OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,,47.28,48.1,47.72
"OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,,65.60,65.49,65.6
"OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,,486.11,486.9,486.11
"OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,,89.35,89.19,89.3
"OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,,357.86,358.5,357.41
"OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,,293.47,297.48,294.58
"OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (ms)",LIB,,108.97,107.5,108.56
"OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,,333.15,329.97,331.77
"OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (ms)",LIB,,95.98,96.9,96.38
"OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,,34.90,34.79,34.88
"OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (ms)",LIB,,913.41,915.17,913.21
"OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,,40.11,40.15,40.21
"OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (ms)",LIB,,794.06,793.31,792
"OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,,217.95,221.47,219.27
"OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (ms)",LIB,,146.71,144.38,145.82
"OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,,204.69,205.33,207.24
"OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (ms)",LIB,,156.22,155.74,154.3
"OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (FPS)",HIB,,164.82,164.75,164.79
"OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (ms)",LIB,,193.84,193.93,193.87
"OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,,163.95,163.98,164.13
"OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (ms)",LIB,,194.88,194.84,194.65
"OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (FPS)",HIB,,142.60,142.58,142.54
"OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (ms)",LIB,,224.22,224.22,224.31
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,,1402.51,1402.97,1403.65
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (ms)",LIB,,22.80,22.79,22.78
"OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,,147.76,147.08,146.9
"OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (ms)",LIB,,216.18,217.16,217.41
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,,1462.94,1473.23,1460.72
"OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (ms)",LIB,,21.86,21.71,21.89
"Primesieve - Length: 1e12 (sec)",LIB,,2.911,2.872,2.893
"Primesieve - Length: 1e13 (sec)",LIB,,42.305,42.441,42.294
"Stockfish - Chess Benchmark (Nodes/s)",HIB,59028775,59449725,51901853,53514996
"Parallel BZIP2 Compression - FreeBSD-13.0-RELEASE-amd64-memstick.img Compression (sec)",LIB,,2.413553,2.439338,2.438631
"Timed Linux Kernel Compilation - Build: defconfig (sec)",LIB,94.273,92.760,94.426,94.496
"Timed Linux Kernel Compilation - Build: allmodconfig (sec)",LIB,,348.018,350.294,349.915
"SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 4K (FPS)",HIB,2.652,2.644,2.65,2.65
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 4K (FPS)",HIB,24.945,24.927,25.006,24.952
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 4K (FPS)",HIB,74.682,74.469,75.167,75.015
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 4K (FPS)",HIB,74.896,74.900,74.958,74.604
"SVT-AV1 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p (FPS)",HIB,8.914,8.925,8.921,8.926
"SVT-AV1 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p (FPS)",HIB,56.897,57.135,57.027,56.789
"SVT-AV1 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p (FPS)",HIB,265.743,264.978,265.435,264.28
"SVT-AV1 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p (FPS)",HIB,364.399,363.354,365.102,363.612
"srsRAN Project - Test: PDSCH Processor Benchmark, Throughput Total (Mbps)",HIB,14099.8,13936.1,13999.6,
"srsRAN Project - Test: PUSCH Processor Benchmark, Throughput Total (Mbps)",HIB,1602.1,,,
"srsRAN Project - Test: PDSCH Processor Benchmark, Throughput Thread (Mbps)",HIB,175.8,175.7,,
"srsRAN Project - Test: PUSCH Processor Benchmark, Throughput Thread (Mbps)",HIB,46.7,,,