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:
Processor: ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores), Motherboard: GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS), Chipset: Ampere Computing LLC Altra PCI Root Complex A, Memory: 16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE, Disk: 800GB Micron_7450_MTFDKBA800TFS, Graphics: ASPEED, Monitor: VGA HDMI, Network: 2 x Intel I350
OS: Ubuntu 23.10, Kernel: 6.5.0-15-generic (aarch64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1024x768
aa:
Processor: ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores), Motherboard: GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS), Chipset: Ampere Computing LLC Altra PCI Root Complex A, Memory: 16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE, Disk: 800GB Micron_7450_MTFDKBA800TFS, Graphics: ASPEED, Monitor: VGA HDMI, Network: 2 x Intel I350
OS: Ubuntu 23.10, Kernel: 6.5.0-15-generic (aarch64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1024x768
b:
Processor: ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores), Motherboard: GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS), Chipset: Ampere Computing LLC Altra PCI Root Complex A, Memory: 16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE, Disk: 800GB Micron_7450_MTFDKBA800TFS, Graphics: ASPEED, Monitor: VGA HDMI, Network: 2 x Intel I350
OS: Ubuntu 23.10, Kernel: 6.5.0-15-generic (aarch64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1024x768
c:
Processor: ARMv8 Neoverse-N1 @ 3.00GHz (128 Cores), Motherboard: GIGABYTE G242-P36-00 MP32-AR2-00 v01000100 (F31k SCP: 2.10.20220531 BIOS), Chipset: Ampere Computing LLC Altra PCI Root Complex A, Memory: 16 x 32 GB DDR4-3200MT/s Samsung M393A4K40DB3-CWE, Disk: 800GB Micron_7450_MTFDKBA800TFS, Graphics: ASPEED, Monitor: VGA HDMI, Network: 2 x Intel I350
OS: Ubuntu 23.10, Kernel: 6.5.0-15-generic (aarch64), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 1024x768
WavPack Audio Encoding 5.7
WAV To WavPack
Seconds < Lower Is Better
aa . 25.20 |===================================================================
b .. 25.21 |===================================================================
c .. 25.20 |===================================================================
Google Draco 1.5.6
Model: Lion
ms < Lower Is Better
aa . 7351 |====================================================================
b .. 7320 |====================================================================
c .. 7332 |====================================================================
Google Draco 1.5.6
Model: Church Facade
ms < Lower Is Better
aa . 10100 |===================================================================
b .. 9847 |=================================================================
c .. 9848 |=================================================================
JPEG-XL Decoding libjxl 0.10.1
CPU Threads: 1
MP/s > Higher Is Better
a .. 27.24 |===================================================================
aa . 27.15 |==================================================================
b .. 27.42 |===================================================================
c .. 27.40 |===================================================================
JPEG-XL Decoding libjxl 0.10.1
CPU Threads: All
MP/s > Higher Is Better
a .. 558.57 |=================================================================
aa . 523.02 |=============================================================
b .. 564.89 |==================================================================
c .. 542.10 |===============================================================
JPEG-XL libjxl 0.10.1
Input: PNG - Quality: 80
MP/s > Higher Is Better
a .. 43.10 |===================================================================
aa . 40.28 |===============================================================
b .. 41.31 |================================================================
c .. 41.35 |================================================================
JPEG-XL libjxl 0.10.1
Input: PNG - Quality: 90
MP/s > Higher Is Better
a .. 39.25 |==================================================================
aa . 37.90 |================================================================
b .. 39.67 |===================================================================
c .. 39.25 |==================================================================
JPEG-XL libjxl 0.10.1
Input: JPEG - Quality: 80
MP/s > Higher Is Better
a .. 39.27 |===================================================================
aa . 38.92 |==================================================================
b .. 37.77 |================================================================
c .. 39.32 |===================================================================
JPEG-XL libjxl 0.10.1
Input: JPEG - Quality: 90
MP/s > Higher Is Better
a .. 37.59 |===================================================================
aa . 37.42 |==================================================================
b .. 37.79 |===================================================================
c .. 35.84 |================================================================
JPEG-XL libjxl 0.10.1
Input: PNG - Quality: 100
MP/s > Higher Is Better
a .. 29.60 |===================================================================
aa . 29.24 |==================================================================
b .. 29.49 |===================================================================
c .. 29.54 |===================================================================
JPEG-XL libjxl 0.10.1
Input: JPEG - Quality: 100
MP/s > Higher Is Better
a .. 31.67 |===================================================================
aa . 31.12 |==================================================================
b .. 31.62 |===================================================================
c .. 31.62 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 33.42 |==================================================================
b .. 33.71 |===================================================================
c .. 33.68 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 1844.12 |=================================================================
b .. 1834.83 |=================================================================
c .. 1833.45 |=================================================================
Neural Magic DeepSparse 1.7
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 26.09 |==================================================================
b .. 25.95 |==================================================================
c .. 26.33 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 38.32 |===================================================================
b .. 38.52 |===================================================================
c .. 37.96 |==================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 1149.47 |=================================================================
b .. 1144.80 |=================================================================
c .. 1144.77 |=================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 55.03 |===================================================================
b .. 55.26 |===================================================================
c .. 55.24 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 132.18 |==================================================================
b .. 132.99 |==================================================================
c .. 131.48 |=================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 7.5520 |==================================================================
b .. 7.5061 |=================================================================
c .. 7.5924 |==================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 474.90 |=================================================================
b .. 475.82 |=================================================================
c .. 479.99 |==================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 132.95 |==================================================================
b .. 132.74 |==================================================================
c .. 131.52 |=================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 133.53 |==================================================================
b .. 134.00 |==================================================================
c .. 133.49 |==================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 7.4741 |==================================================================
b .. 7.4484 |==================================================================
c .. 7.4767 |==================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 2678.24 |=================================================================
b .. 2688.96 |=================================================================
c .. 2630.33 |================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 23.50 |==================================================================
b .. 23.41 |==================================================================
c .. 23.91 |===================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 315.75 |==================================================================
b .. 312.46 |=================================================================
c .. 316.35 |==================================================================
Neural Magic DeepSparse 1.7
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 3.1508 |=================================================================
b .. 3.1835 |==================================================================
c .. 3.1449 |=================================================================
Neural Magic DeepSparse 1.7
Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 2.2602 |=================================================================
b .. 2.2754 |==================================================================
c .. 2.2836 |==================================================================
Neural Magic DeepSparse 1.7
Model: Llama2 Chat 7b Quantized - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 21332.89 |================================================================
b .. 21231.56 |================================================================
c .. 21169.30 |================================================================
Neural Magic DeepSparse 1.7
Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 12.93 |===================================================================
b .. 12.90 |===================================================================
c .. 12.96 |===================================================================
Neural Magic DeepSparse 1.7
Model: Llama2 Chat 7b Quantized - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 77.30 |===================================================================
b .. 77.49 |===================================================================
c .. 77.12 |===================================================================
Neural Magic DeepSparse 1.7
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 476.36 |==================================================================
b .. 478.64 |==================================================================
c .. 478.37 |==================================================================
Neural Magic DeepSparse 1.7
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 132.54 |==================================================================
b .. 131.95 |==================================================================
c .. 131.86 |==================================================================
Neural Magic DeepSparse 1.7
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 133.62 |==================================================================
b .. 133.63 |==================================================================
c .. 133.89 |==================================================================
Neural Magic DeepSparse 1.7
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 7.4691 |==================================================================
b .. 7.4692 |==================================================================
c .. 7.4540 |==================================================================
Neural Magic DeepSparse 1.7
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 202.64 |==================================================================
b .. 202.15 |==================================================================
c .. 201.02 |=================================================================
Neural Magic DeepSparse 1.7
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 310.51 |==================================================================
b .. 311.17 |==================================================================
c .. 312.79 |==================================================================
Neural Magic DeepSparse 1.7
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 112.53 |==================================================================
b .. 112.83 |==================================================================
c .. 112.85 |==================================================================
Neural Magic DeepSparse 1.7
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 8.8709 |==================================================================
b .. 8.8483 |==================================================================
c .. 8.8461 |==================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 345.11 |==================================================================
b .. 346.67 |==================================================================
c .. 339.90 |=================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 182.88 |=================================================================
b .. 181.90 |=================================================================
c .. 185.72 |==================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 109.95 |=================================================================
b .. 109.48 |=================================================================
c .. 111.16 |==================================================================
Neural Magic DeepSparse 1.7
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 9.0819 |==================================================================
b .. 9.1198 |==================================================================
c .. 8.9820 |=================================================================
Neural Magic DeepSparse 1.7
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 46.61 |===================================================================
b .. 46.72 |===================================================================
c .. 46.68 |===================================================================
Neural Magic DeepSparse 1.7
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 1337.59 |=================================================================
b .. 1335.35 |=================================================================
c .. 1333.72 |=================================================================
Neural Magic DeepSparse 1.7
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 30.60 |===================================================================
b .. 30.72 |===================================================================
c .. 30.67 |===================================================================
Neural Magic DeepSparse 1.7
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 32.66 |===================================================================
b .. 32.53 |===================================================================
c .. 32.58 |===================================================================
Neural Magic DeepSparse 1.7
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 438.71 |==================================================================
b .. 439.60 |==================================================================
c .. 438.25 |==================================================================
Neural Magic DeepSparse 1.7
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 143.83 |==================================================================
b .. 143.48 |==================================================================
c .. 143.60 |==================================================================
Neural Magic DeepSparse 1.7
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 50.60 |===================================================================
b .. 50.73 |===================================================================
c .. 50.65 |===================================================================
Neural Magic DeepSparse 1.7
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 19.75 |===================================================================
b .. 19.69 |===================================================================
c .. 19.73 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
aa . 33.53 |===================================================================
b .. 33.58 |===================================================================
c .. 33.67 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
aa . 1840.37 |=================================================================
b .. 1835.26 |=================================================================
c .. 1836.79 |=================================================================
Neural Magic DeepSparse 1.7
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
items/sec > Higher Is Better
aa . 26.25 |===================================================================
b .. 26.35 |===================================================================
c .. 26.20 |===================================================================
Neural Magic DeepSparse 1.7
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
ms/batch < Lower Is Better
aa . 38.07 |===================================================================
b .. 37.94 |===================================================================
c .. 38.15 |===================================================================
oneDNN 3.4
Harness: IP Shapes 1D - Engine: CPU
ms < Lower Is Better
aa . 4.84065 |================================================================
b .. 4.88015 |=================================================================
c .. 4.88858 |=================================================================
oneDNN 3.4
Harness: IP Shapes 3D - Engine: CPU
ms < Lower Is Better
aa . 2.15582 |=================================================================
b .. 2.15178 |=================================================================
c .. 2.14878 |=================================================================
oneDNN 3.4
Harness: Convolution Batch Shapes Auto - Engine: CPU
ms < Lower Is Better
aa . 4.29470 |=================================================================
b .. 4.28036 |=================================================================
c .. 4.28461 |=================================================================
oneDNN 3.4
Harness: Deconvolution Batch shapes_1d - Engine: CPU
ms < Lower Is Better
aa . 20.93 |===================================================================
b .. 20.43 |=================================================================
c .. 20.89 |===================================================================
oneDNN 3.4
Harness: Deconvolution Batch shapes_3d - Engine: CPU
ms < Lower Is Better
aa . 2.79626 |=================================================================
b .. 2.78238 |=================================================================
c .. 2.80386 |=================================================================
oneDNN 3.4
Harness: Recurrent Neural Network Training - Engine: CPU
ms < Lower Is Better
aa . 3738.39 |=================================================================
b .. 3737.15 |=================================================================
c .. 3738.53 |=================================================================
oneDNN 3.4
Harness: Recurrent Neural Network Inference - Engine: CPU
ms < Lower Is Better
aa . 1460.94 |=================================================================
b .. 1461.00 |=================================================================
c .. 1469.65 |=================================================================
OpenVINO 2024.0
Model: Face Detection FP16 - Device: CPU
FPS > Higher Is Better
aa . 2.84 |====================================================================
b .. 2.84 |====================================================================
c .. 2.84 |====================================================================
OpenVINO 2024.0
Model: Face Detection FP16 - Device: CPU
ms < Lower Is Better
aa . 10877.53 |================================================================
b .. 10891.93 |================================================================
c .. 10876.70 |================================================================
OpenVINO 2024.0
Model: Person Detection FP16 - Device: CPU
FPS > Higher Is Better
aa . 14.77 |===================================================================
b .. 14.77 |===================================================================
c .. 14.73 |===================================================================
OpenVINO 2024.0
Model: Person Detection FP16 - Device: CPU
ms < Lower Is Better
aa . 2150.30 |=================================================================
b .. 2151.85 |=================================================================
c .. 2157.45 |=================================================================
OpenVINO 2024.0
Model: Person Detection FP32 - Device: CPU
FPS > Higher Is Better
aa . 14.73 |===================================================================
b .. 14.84 |===================================================================
c .. 14.80 |===================================================================
OpenVINO 2024.0
Model: Person Detection FP32 - Device: CPU
ms < Lower Is Better
aa . 2156.87 |=================================================================
b .. 2140.20 |================================================================
c .. 2146.07 |=================================================================
OpenVINO 2024.0
Model: Vehicle Detection FP16 - Device: CPU
FPS > Higher Is Better
aa . 222.86 |==================================================================
b .. 223.85 |==================================================================
c .. 222.78 |==================================================================
OpenVINO 2024.0
Model: Vehicle Detection FP16 - Device: CPU
ms < Lower Is Better
aa . 143.42 |==================================================================
b .. 142.79 |==================================================================
c .. 143.48 |==================================================================
OpenVINO 2024.0
Model: Face Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 2.74 |====================================================================
b .. 2.75 |====================================================================
c .. 2.75 |====================================================================
OpenVINO 2024.0
Model: Face Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 11232.43 |================================================================
b .. 11206.13 |================================================================
c .. 11196.54 |================================================================
OpenVINO 2024.0
Model: Face Detection Retail FP16 - Device: CPU
FPS > Higher Is Better
aa . 676.59 |==================================================================
b .. 664.78 |=================================================================
c .. 670.19 |=================================================================
OpenVINO 2024.0
Model: Face Detection Retail FP16 - Device: CPU
ms < Lower Is Better
aa . 47.28 |==================================================================
b .. 48.10 |===================================================================
c .. 47.72 |==================================================================
OpenVINO 2024.0
Model: Road Segmentation ADAS FP16 - Device: CPU
FPS > Higher Is Better
aa . 65.60 |===================================================================
b .. 65.49 |===================================================================
c .. 65.60 |===================================================================
OpenVINO 2024.0
Model: Road Segmentation ADAS FP16 - Device: CPU
ms < Lower Is Better
aa . 486.11 |==================================================================
b .. 486.90 |==================================================================
c .. 486.11 |==================================================================
OpenVINO 2024.0
Model: Vehicle Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 89.35 |===================================================================
b .. 89.19 |===================================================================
c .. 89.30 |===================================================================
OpenVINO 2024.0
Model: Vehicle Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 357.86 |==================================================================
b .. 358.50 |==================================================================
c .. 357.41 |==================================================================
OpenVINO 2024.0
Model: Weld Porosity Detection FP16 - Device: CPU
FPS > Higher Is Better
aa . 293.47 |=================================================================
b .. 297.48 |==================================================================
c .. 294.58 |=================================================================
OpenVINO 2024.0
Model: Weld Porosity Detection FP16 - Device: CPU
ms < Lower Is Better
aa . 108.97 |==================================================================
b .. 107.50 |=================================================================
c .. 108.56 |==================================================================
OpenVINO 2024.0
Model: Face Detection Retail FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 333.15 |==================================================================
b .. 329.97 |=================================================================
c .. 331.77 |==================================================================
OpenVINO 2024.0
Model: Face Detection Retail FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 95.98 |==================================================================
b .. 96.90 |===================================================================
c .. 96.38 |===================================================================
OpenVINO 2024.0
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 34.90 |===================================================================
b .. 34.79 |===================================================================
c .. 34.88 |===================================================================
OpenVINO 2024.0
Model: Road Segmentation ADAS FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 913.41 |==================================================================
b .. 915.17 |==================================================================
c .. 913.21 |==================================================================
OpenVINO 2024.0
Model: Machine Translation EN To DE FP16 - Device: CPU
FPS > Higher Is Better
aa . 40.11 |===================================================================
b .. 40.15 |===================================================================
c .. 40.21 |===================================================================
OpenVINO 2024.0
Model: Machine Translation EN To DE FP16 - Device: CPU
ms < Lower Is Better
aa . 794.06 |==================================================================
b .. 793.31 |==================================================================
c .. 792.00 |==================================================================
OpenVINO 2024.0
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 217.95 |=================================================================
b .. 221.47 |==================================================================
c .. 219.27 |=================================================================
OpenVINO 2024.0
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 146.71 |==================================================================
b .. 144.38 |=================================================================
c .. 145.82 |==================================================================
OpenVINO 2024.0
Model: Person Vehicle Bike Detection FP16 - Device: CPU
FPS > Higher Is Better
aa . 204.69 |=================================================================
b .. 205.33 |=================================================================
c .. 207.24 |==================================================================
OpenVINO 2024.0
Model: Person Vehicle Bike Detection FP16 - Device: CPU
ms < Lower Is Better
aa . 156.22 |==================================================================
b .. 155.74 |==================================================================
c .. 154.30 |=================================================================
OpenVINO 2024.0
Model: Noise Suppression Poconet-Like FP16 - Device: CPU
FPS > Higher Is Better
aa . 164.82 |==================================================================
b .. 164.75 |==================================================================
c .. 164.79 |==================================================================
OpenVINO 2024.0
Model: Noise Suppression Poconet-Like FP16 - Device: CPU
ms < Lower Is Better
aa . 193.84 |==================================================================
b .. 193.93 |==================================================================
c .. 193.87 |==================================================================
OpenVINO 2024.0
Model: Handwritten English Recognition FP16 - Device: CPU
FPS > Higher Is Better
aa . 163.95 |==================================================================
b .. 163.98 |==================================================================
c .. 164.13 |==================================================================
OpenVINO 2024.0
Model: Handwritten English Recognition FP16 - Device: CPU
ms < Lower Is Better
aa . 194.88 |==================================================================
b .. 194.84 |==================================================================
c .. 194.65 |==================================================================
OpenVINO 2024.0
Model: Person Re-Identification Retail FP16 - Device: CPU
FPS > Higher Is Better
aa . 142.60 |==================================================================
b .. 142.58 |==================================================================
c .. 142.54 |==================================================================
OpenVINO 2024.0
Model: Person Re-Identification Retail FP16 - Device: CPU
ms < Lower Is Better
aa . 224.22 |==================================================================
b .. 224.22 |==================================================================
c .. 224.31 |==================================================================
OpenVINO 2024.0
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
FPS > Higher Is Better
aa . 1402.51 |=================================================================
b .. 1402.97 |=================================================================
c .. 1403.65 |=================================================================
OpenVINO 2024.0
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
ms < Lower Is Better
aa . 22.80 |===================================================================
b .. 22.79 |===================================================================
c .. 22.78 |===================================================================
OpenVINO 2024.0
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 147.76 |==================================================================
b .. 147.08 |==================================================================
c .. 146.90 |==================================================================
OpenVINO 2024.0
Model: Handwritten English Recognition FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 216.18 |==================================================================
b .. 217.16 |==================================================================
c .. 217.41 |==================================================================
OpenVINO 2024.0
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
FPS > Higher Is Better
aa . 1462.94 |=================================================================
b .. 1473.23 |=================================================================
c .. 1460.72 |================================================================
OpenVINO 2024.0
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
ms < Lower Is Better
aa . 21.86 |===================================================================
b .. 21.71 |==================================================================
c .. 21.89 |===================================================================
Primesieve 12.1
Length: 1e12
Seconds < Lower Is Better
aa . 2.911 |===================================================================
b .. 2.872 |==================================================================
c .. 2.893 |===================================================================
Primesieve 12.1
Length: 1e13
Seconds < Lower Is Better
aa . 42.31 |===================================================================
b .. 42.44 |===================================================================
c .. 42.29 |===================================================================
Stockfish 16.1
Chess Benchmark
Nodes Per Second > Higher Is Better
a .. 59028775 |================================================================
aa . 59449725 |================================================================
b .. 51901853 |========================================================
c .. 53514996 |==========================================================
Parallel BZIP2 Compression 1.1.13
FreeBSD-13.0-RELEASE-amd64-memstick.img Compression
Seconds < Lower Is Better
aa . 2.413553 |===============================================================
b .. 2.439338 |================================================================
c .. 2.438631 |================================================================
Timed Linux Kernel Compilation 6.8
Build: defconfig
Seconds < Lower Is Better
a .. 94.27 |===================================================================
aa . 92.76 |==================================================================
b .. 94.43 |===================================================================
c .. 94.50 |===================================================================
Timed Linux Kernel Compilation 6.8
Build: allmodconfig
Seconds < Lower Is Better
aa . 348.02 |==================================================================
b .. 350.29 |==================================================================
c .. 349.92 |==================================================================
SVT-AV1 2.0
Encoder Mode: Preset 4 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a .. 2.652 |===================================================================
aa . 2.644 |===================================================================
b .. 2.650 |===================================================================
c .. 2.650 |===================================================================
SVT-AV1 2.0
Encoder Mode: Preset 8 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a .. 24.95 |===================================================================
aa . 24.93 |===================================================================
b .. 25.01 |===================================================================
c .. 24.95 |===================================================================
SVT-AV1 2.0
Encoder Mode: Preset 12 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a .. 74.68 |===================================================================
aa . 74.47 |==================================================================
b .. 75.17 |===================================================================
c .. 75.02 |===================================================================
SVT-AV1 2.0
Encoder Mode: Preset 13 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
a .. 74.90 |===================================================================
aa . 74.90 |===================================================================
b .. 74.96 |===================================================================
c .. 74.60 |===================================================================
SVT-AV1 2.0
Encoder Mode: Preset 4 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a .. 8.914 |===================================================================
aa . 8.925 |===================================================================
b .. 8.921 |===================================================================
c .. 8.926 |===================================================================
SVT-AV1 2.0
Encoder Mode: Preset 8 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a .. 56.90 |===================================================================
aa . 57.14 |===================================================================
b .. 57.03 |===================================================================
c .. 56.79 |===================================================================
SVT-AV1 2.0
Encoder Mode: Preset 12 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a .. 265.74 |==================================================================
aa . 264.98 |==================================================================
b .. 265.44 |==================================================================
c .. 264.28 |==================================================================
SVT-AV1 2.0
Encoder Mode: Preset 13 - Input: Bosphorus 1080p
Frames Per Second > Higher Is Better
a .. 364.40 |==================================================================
aa . 363.35 |==================================================================
b .. 365.10 |==================================================================
c .. 363.61 |==================================================================
srsRAN Project 23.10.1-20240219
Test: PDSCH Processor Benchmark, Throughput Total
Mbps > Higher Is Better
a .. 14099.8 |=================================================================
aa . 13936.1 |================================================================
b .. 13999.6 |=================================================================
srsRAN Project 23.10.1-20240219
Test: PUSCH Processor Benchmark, Throughput Total
Mbps > Higher Is Better
a . 1602.1 |===================================================================
srsRAN Project 23.10.1-20240219
Test: PDSCH Processor Benchmark, Throughput Thread
Mbps > Higher Is Better
a .. 175.8 |===================================================================
aa . 175.7 |===================================================================
srsRAN Project 23.10.1-20240219
Test: PUSCH Processor Benchmark, Throughput Thread
Mbps > Higher Is Better
a . 46.7 |=====================================================================