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 ,
"WavPack Audio Encoding 5.7 - WAV To WavPack",
Lower Results Are Better
"aa",25.207,25.201,25.193,25.193,25.203
"b",
"c",
"Google Draco 1.5.6 - Model: Lion",
Lower Results Are Better
"aa",7350,7355,7349
"b",
"c",
"Google Draco 1.5.6 - Model: Church Facade",
Lower Results Are Better
"aa",10088,10109,10103
"b",
"c",
"JPEG-XL Decoding libjxl 0.10.1 - CPU Threads: 1",
Higher Results Are Better
"a",
"aa",27.132,27.144,27.181
"b",
"c",
"JPEG-XL Decoding libjxl 0.10.1 - CPU Threads: All",
Higher Results Are Better
"a",
"aa",525.517,519.158,524.381
"b",
"c",
"JPEG-XL libjxl 0.10.1 - Input: PNG - Quality: 80",
Higher Results Are Better
"a",
"aa",39.706,40.738,40.393
"b",
"c",
"JPEG-XL libjxl 0.10.1 - Input: PNG - Quality: 90",
Higher Results Are Better
"a",
"aa",38.915,38.941,32.816,38.754,33.331,38.752,39.128,39.066,38.939,39.103,38.582,35.772,38.855,38.868,38.608
"b",
"c",
"JPEG-XL libjxl 0.10.1 - Input: JPEG - Quality: 80",
Higher Results Are Better
"a",
"aa",39.152,38.862,38.749
"b",
"c",
"JPEG-XL libjxl 0.10.1 - Input: JPEG - Quality: 90",
Higher Results Are Better
"a",
"aa",35.451,38.118,39.039,38.751,37.711,36.519,39.667,35.446,36.613,35.162,37.975,39.049,40.38,35.796,35.551
"b",
"c",
"JPEG-XL libjxl 0.10.1 - Input: PNG - Quality: 100",
Higher Results Are Better
"a",
"aa",29.269,29.295,29.151
"b",
"c",
"JPEG-XL libjxl 0.10.1 - Input: JPEG - Quality: 100",
Higher Results Are Better
"a",
"aa",31.129,31.117,31.117
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",33.4478,33.3717,33.4367
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",1840.803,1846.8904,1844.6805
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",25.9217,26.2941,26.0456
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",38.5596,38.0133,38.3758
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",1150.5278,1144.1049,1153.7846
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",55.0269,55.2084,54.843
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",132.0089,132.7117,131.8342
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",7.5621,7.5218,7.5721
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",475.3001,472.4256,476.9672
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",132.7014,133.7216,132.4346
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",133.6249,133.6617,133.3036
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",7.469,7.4665,7.4869
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",2680.2349,2688.4148,2666.065
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",23.5203,23.4003,23.5911
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",316.1404,316.8063,314.2882
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",3.1469,3.1404,3.1651
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",2.2489,2.2575,2.2742
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",21417.907,21352.672,21228.1003
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",12.9703,12.9053,12.9138
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",77.0606,77.4489,77.3983
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",475.6256,474.7699,478.6717
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",132.7019,133.0297,131.8958
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",133.78,133.8013,133.2841
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",7.4607,7.4585,7.4881
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",203.0745,201.9769,202.8562
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",310.0856,311.5314,309.9217
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",112.2511,112.8215,112.5275
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",8.8931,8.8485,8.8711
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",345.5208,345.1388,344.6644
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",182.7388,182.871,183.0269
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",108.2672,110.5076,111.0821
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",9.2219,9.0352,8.9885
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",46.474,46.831,46.5309
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",1340.0294,1331.2606,1341.4853
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",30.5973,30.583,30.6081
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",32.6588,32.6736,32.6468
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",438.3678,439.5519,438.2195
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",143.9565,143.7015,143.8372
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",50.4684,50.5848,50.7396
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",19.7964,19.7505,19.6908
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Higher Results Are Better
"aa",33.606,33.4867,33.5084
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream",
Lower Results Are Better
"aa",1841.7245,1840.9711,1838.4074
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream",
Higher Results Are Better
"aa",26.2951,26.2545,26.2097
"b",
"c",
"Neural Magic DeepSparse 1.7 - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream",
Lower Results Are Better
"aa",38.0118,38.0704,38.1357
"b",
"c",
"oneDNN 3.4 - Harness: IP Shapes 1D - Engine: CPU",
Lower Results Are Better
"aa",4.86044,4.83517,4.82634
"b",
"c",
"oneDNN 3.4 - Harness: IP Shapes 3D - Engine: CPU",
Lower Results Are Better
"aa",2.15843,2.15522,2.15381
"b",
"c",
"oneDNN 3.4 - Harness: Convolution Batch Shapes Auto - Engine: CPU",
Lower Results Are Better
"aa",4.32434,4.29197,4.2678
"b",
"c",
"oneDNN 3.4 - Harness: Deconvolution Batch shapes_1d - Engine: CPU",
Lower Results Are Better
"aa",21.0625,20.5372,21.1769
"b",
"c",
"oneDNN 3.4 - Harness: Deconvolution Batch shapes_3d - Engine: CPU",
Lower Results Are Better
"aa",2.75255,2.77722,3.00306,2.7737,2.76656,2.77428,2.79424,2.77245,2.77091,2.79051,2.78974,2.78994
"b",
"c",
"oneDNN 3.4 - Harness: Recurrent Neural Network Training - Engine: CPU",
Lower Results Are Better
"aa",3736.24,3735.94,3742.99
"b",
"c",
"oneDNN 3.4 - Harness: Recurrent Neural Network Inference - Engine: CPU",
Lower Results Are Better
"aa",1459.45,1468,1455.38
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection FP16 - Device: CPU",
Higher Results Are Better
"aa",2.84,2.85,2.83
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection FP16 - Device: CPU",
Lower Results Are Better
"aa",10892.13,10842.87,10897.58
"b",
"c",
"OpenVINO 2024.0 - Model: Person Detection FP16 - Device: CPU",
Higher Results Are Better
"aa",14.77,14.79,14.76
"b",
"c",
"OpenVINO 2024.0 - Model: Person Detection FP16 - Device: CPU",
Lower Results Are Better
"aa",2150.44,2148.17,2152.28
"b",
"c",
"OpenVINO 2024.0 - Model: Person Detection FP32 - Device: CPU",
Higher Results Are Better
"aa",14.76,14.73,14.7
"b",
"c",
"OpenVINO 2024.0 - Model: Person Detection FP32 - Device: CPU",
Lower Results Are Better
"aa",2152.53,2157.31,2160.77
"b",
"c",
"OpenVINO 2024.0 - Model: Vehicle Detection FP16 - Device: CPU",
Higher Results Are Better
"aa",222.67,222.93,222.98
"b",
"c",
"OpenVINO 2024.0 - Model: Vehicle Detection FP16 - Device: CPU",
Lower Results Are Better
"aa",143.54,143.37,143.35
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",2.75,2.74,2.74
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",11218.49,11250.12,11228.67
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection Retail FP16 - Device: CPU",
Higher Results Are Better
"aa",689.94,679.09,660.75
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection Retail FP16 - Device: CPU",
Lower Results Are Better
"aa",46.35,47.09,48.4
"b",
"c",
"OpenVINO 2024.0 - Model: Road Segmentation ADAS FP16 - Device: CPU",
Higher Results Are Better
"aa",65.8,65.39,65.6
"b",
"c",
"OpenVINO 2024.0 - Model: Road Segmentation ADAS FP16 - Device: CPU",
Lower Results Are Better
"aa",484.59,487.65,486.1
"b",
"c",
"OpenVINO 2024.0 - Model: Vehicle Detection FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",89.39,89.36,89.3
"b",
"c",
"OpenVINO 2024.0 - Model: Vehicle Detection FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",357.69,357.84,358.06
"b",
"c",
"OpenVINO 2024.0 - Model: Weld Porosity Detection FP16 - Device: CPU",
Higher Results Are Better
"aa",292.87,293.81,293.73
"b",
"c",
"OpenVINO 2024.0 - Model: Weld Porosity Detection FP16 - Device: CPU",
Lower Results Are Better
"aa",109.19,108.84,108.87
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection Retail FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",331.94,333.57,333.94
"b",
"c",
"OpenVINO 2024.0 - Model: Face Detection Retail FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",96.33,95.87,95.74
"b",
"c",
"OpenVINO 2024.0 - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",34.87,34.92,34.92
"b",
"c",
"OpenVINO 2024.0 - Model: Road Segmentation ADAS FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",914.38,912.89,912.96
"b",
"c",
"OpenVINO 2024.0 - Model: Machine Translation EN To DE FP16 - Device: CPU",
Higher Results Are Better
"aa",40.01,40.16,40.16
"b",
"c",
"OpenVINO 2024.0 - Model: Machine Translation EN To DE FP16 - Device: CPU",
Lower Results Are Better
"aa",795.93,793.11,793.15
"b",
"c",
"OpenVINO 2024.0 - Model: Weld Porosity Detection FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",218.19,218.06,217.6
"b",
"c",
"OpenVINO 2024.0 - Model: Weld Porosity Detection FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",146.56,146.63,146.95
"b",
"c",
"OpenVINO 2024.0 - Model: Person Vehicle Bike Detection FP16 - Device: CPU",
Higher Results Are Better
"aa",205.99,204.18,203.9
"b",
"c",
"OpenVINO 2024.0 - Model: Person Vehicle Bike Detection FP16 - Device: CPU",
Lower Results Are Better
"aa",155.24,156.6,156.82
"b",
"c",
"OpenVINO 2024.0 - Model: Noise Suppression Poconet-Like FP16 - Device: CPU",
Higher Results Are Better
"aa",164.8,164.78,164.87
"b",
"c",
"OpenVINO 2024.0 - Model: Noise Suppression Poconet-Like FP16 - Device: CPU",
Lower Results Are Better
"aa",193.81,193.78,193.92
"b",
"c",
"OpenVINO 2024.0 - Model: Handwritten English Recognition FP16 - Device: CPU",
Higher Results Are Better
"aa",163.95,164.06,163.85
"b",
"c",
"OpenVINO 2024.0 - Model: Handwritten English Recognition FP16 - Device: CPU",
Lower Results Are Better
"aa",194.87,194.76,195.02
"b",
"c",
"OpenVINO 2024.0 - Model: Person Re-Identification Retail FP16 - Device: CPU",
Higher Results Are Better
"aa",142.45,143.25,142.1
"b",
"c",
"OpenVINO 2024.0 - Model: Person Re-Identification Retail FP16 - Device: CPU",
Lower Results Are Better
"aa",224.44,223.22,225
"b",
"c",
"OpenVINO 2024.0 - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU",
Higher Results Are Better
"aa",1408.52,1400.57,1398.45
"b",
"c",
"OpenVINO 2024.0 - Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU",
Lower Results Are Better
"aa",22.71,22.83,22.87
"b",
"c",
"OpenVINO 2024.0 - Model: Handwritten English Recognition FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",146.95,149.42,146.9
"b",
"c",
"OpenVINO 2024.0 - Model: Handwritten English Recognition FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",217.33,213.75,217.45
"b",
"c",
"OpenVINO 2024.0 - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU",
Higher Results Are Better
"aa",1463.59,1465.12,1460.11
"b",
"c",
"OpenVINO 2024.0 - Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU",
Lower Results Are Better
"aa",21.85,21.83,21.9
"b",
"c",
"Primesieve 12.1 - Length: 1e12",
Lower Results Are Better
"aa",2.916,2.905,2.912
"b",
"c",
"Primesieve 12.1 - Length: 1e13",
Lower Results Are Better
"aa",42.201,42.438,42.275
"b",
"c",
"Stockfish 16.1 - Chess Benchmark",
Higher Results Are Better
"a",
"aa",69125864,59571638,59338275,57221418,53054878,54439774,60239452,55353821,65066627,62363322,52770867,64850761
"b",
"c",
"Parallel BZIP2 Compression 1.1.13 - FreeBSD-13.0-RELEASE-amd64-memstick.img Compression",
Lower Results Are Better
"aa",2.413644,2.416125,2.41089
"b",
"c",
"Timed Linux Kernel Compilation 6.8 - Build: defconfig",
Lower Results Are Better
"a",
"aa",94.569,91.858,91.854
"b",
"c",
"Timed Linux Kernel Compilation 6.8 - Build: allmodconfig",
Lower Results Are Better
"aa",349.356,347.555,347.144
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 4 - Input: Bosphorus 4K",
Higher Results Are Better
"a",
"aa",2.648,2.636,2.647
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 8 - Input: Bosphorus 4K",
Higher Results Are Better
"a",
"aa",24.922,24.912,24.947
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 12 - Input: Bosphorus 4K",
Higher Results Are Better
"a",
"aa",74.597,74.885,73.924
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 13 - Input: Bosphorus 4K",
Higher Results Are Better
"a",
"aa",74.567,75.219,74.915
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 4 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",
"aa",8.926,8.907,8.943
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 8 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",
"aa",57.215,57.175,57.016
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 12 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",
"aa",264.887,264.991,265.056
"b",
"c",
"SVT-AV1 2.0 - Encoder Mode: Preset 13 - Input: Bosphorus 1080p",
Higher Results Are Better
"a",
"aa",362.211,363.99,363.86
"b",
"c",
"srsRAN Project 23.10.1-20240219 - Test: PDSCH Processor Benchmark, Throughput Total",
Higher Results Are Better
"a",
"aa",13954.2,13854.9,13999.1
"b",
"srsRAN Project 23.10.1-20240219 - Test: PUSCH Processor Benchmark, Throughput Total",
Higher Results Are Better
"a",
"srsRAN Project 23.10.1-20240219 - Test: PDSCH Processor Benchmark, Throughput Thread",
Higher Results Are Better
"a",
"aa",175.6,175.7,175.7
"srsRAN Project 23.10.1-20240219 - Test: PUSCH Processor Benchmark, Throughput Thread",
Higher Results Are Better
"a",