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.

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March 17
  15 Minutes
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March 17
  7 Hours, 43 Minutes
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March 17
  2 Hours, 32 Minutes
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March 17
  2 Hours, 15 Minutes
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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" "OpenVINO - Model: Face Detection FP16 - Device: CPU (FPS)",HIB,,2.84,2.84,2.84 "OpenVINO - Model: Person Detection FP16 - Device: CPU (FPS)",HIB,,14.77,14.77,14.73 "OpenVINO - Model: Person Detection FP32 - Device: CPU (FPS)",HIB,,14.73,14.84,14.8 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (FPS)",HIB,,222.86,223.85,222.78 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (FPS)",HIB,,2.74,2.75,2.75 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (FPS)",HIB,,676.59,664.78,670.19 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (FPS)",HIB,,65.60,65.49,65.6 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (FPS)",HIB,,89.35,89.19,89.3 "OpenVINO - Model: Weld Porosity Detection FP16 - Device: CPU (FPS)",HIB,,293.47,297.48,294.58 "OpenVINO - Model: Face Detection Retail FP16-INT8 - Device: CPU (FPS)",HIB,,333.15,329.97,331.77 "OpenVINO - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU (FPS)",HIB,,34.90,34.79,34.88 "OpenVINO - Model: Machine Translation EN To DE FP16 - Device: CPU (FPS)",HIB,,40.11,40.15,40.21 "OpenVINO - Model: Weld Porosity Detection FP16-INT8 - Device: CPU (FPS)",HIB,,217.95,221.47,219.27 "OpenVINO - Model: Person Vehicle Bike Detection FP16 - Device: CPU (FPS)",HIB,,204.69,205.33,207.24 "OpenVINO - Model: Noise Suppression Poconet-Like FP16 - Device: CPU (FPS)",HIB,,164.82,164.75,164.79 "OpenVINO - Model: Handwritten English Recognition FP16 - Device: CPU (FPS)",HIB,,163.95,163.98,164.13 "OpenVINO - Model: Person Re-Identification Retail FP16 - Device: CPU (FPS)",HIB,,142.60,142.58,142.54 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU (FPS)",HIB,,1402.51,1402.97,1403.65 "OpenVINO - Model: Handwritten English Recognition FP16-INT8 - Device: CPU (FPS)",HIB,,147.76,147.08,146.9 "OpenVINO - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU (FPS)",HIB,,1462.94,1473.23,1460.72 "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 "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: Synchronous Single-Stream (items/sec)",HIB,,26.0871,25.9453,26.3315 "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: Synchronous Single-Stream (items/sec)",HIB,,132.1849,132.9867,131.4797 "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: Synchronous Single-Stream (items/sec)",HIB,,133.5301,134.0016,133.4866 "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: Synchronous Single-Stream (items/sec)",HIB,,315.7450,312.4633,316.347 "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: Synchronous Single-Stream (items/sec)",HIB,,12.9298,12.8977,12.9605 "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: Synchronous Single-Stream (items/sec)",HIB,,133.6218,133.6253,133.8853 "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: Synchronous Single-Stream (items/sec)",HIB,,112.5334,112.8291,112.8521 "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: Synchronous Single-Stream (items/sec)",HIB,,109.9523,109.4784,111.1578 "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: Synchronous Single-Stream (items/sec)",HIB,,30.5961,30.7211,30.6675 "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: Synchronous Single-Stream (items/sec)",HIB,,50.5976,50.7328,50.649 "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: Synchronous Single-Stream (items/sec)",HIB,,26.2531,26.3468,26.2002 "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,,, "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 "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 "Stockfish - Chess Benchmark (Nodes/s)",HIB,59028775,59449725,51901853,53514996 "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 "Google Draco - Model: Lion (ms)",LIB,,7351,7320,7332 "Google Draco - Model: Church Facade (ms)",LIB,,10100,9847,9848 "OpenVINO - Model: Face Detection FP16 - Device: CPU (ms)",LIB,,10877.53,10891.93,10876.7 "OpenVINO - Model: Person Detection FP16 - Device: CPU (ms)",LIB,,2150.30,2151.85,2157.45 "OpenVINO - Model: Person Detection FP32 - Device: CPU (ms)",LIB,,2156.87,2140.2,2146.07 "OpenVINO - Model: Vehicle Detection FP16 - Device: CPU (ms)",LIB,,143.42,142.79,143.48 "OpenVINO - Model: Face Detection FP16-INT8 - Device: CPU (ms)",LIB,,11232.43,11206.13,11196.54 "OpenVINO - Model: Face Detection Retail FP16 - Device: CPU (ms)",LIB,,47.28,48.1,47.72 "OpenVINO - Model: Road Segmentation ADAS FP16 - Device: CPU (ms)",LIB,,486.11,486.9,486.11 "OpenVINO - Model: Vehicle Detection FP16-INT8 - Device: CPU (ms)",LIB,,357.86,358.5,357.41 "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 (ms)",LIB,,95.98,96.9,96.38 "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 (ms)",LIB,,794.06,793.31,792 "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 (ms)",LIB,,156.22,155.74,154.3 "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 (ms)",LIB,,194.88,194.84,194.65 "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 (ms)",LIB,,22.80,22.79,22.78 "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 (ms)",LIB,,21.86,21.71,21.89 "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 (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 (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 (ms/batch)",LIB,,7.5520,7.5061,7.5924 "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 (ms/batch)",LIB,,7.4741,7.4484,7.4767 "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 (ms/batch)",LIB,,3.1508,3.1835,3.1449 "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 (ms/batch)",LIB,,77.3026,77.4941,77.119 "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 (ms/batch)",LIB,,7.4691,7.4692,7.454 "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 (ms/batch)",LIB,,8.8709,8.8483,8.8461 "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 (ms/batch)",LIB,,9.0819,9.1198,8.982 "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 (ms/batch)",LIB,,32.6597,32.5272,32.5835 "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 (ms/batch)",LIB,,19.7459,19.6933,19.7262 "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 (ms/batch)",LIB,,38.0726,37.9374,38.1495 "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 "Parallel BZIP2 Compression - FreeBSD-13.0-RELEASE-amd64-memstick.img Compression (sec)",LIB,,2.413553,2.439338,2.438631 "Primesieve - Length: 1e12 (sec)",LIB,,2.911,2.872,2.893 "Primesieve - Length: 1e13 (sec)",LIB,,42.305,42.441,42.294 "WavPack Audio Encoding - WAV To WavPack (sec)",LIB,,25.199,25.205,25.2