Benchmarks for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 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 2308099-NE-XEONPLATI49
Xeon Platinum 8380 AVX-512 Workloads
Benchmarks for a future article. 2 x Intel Xeon Platinum 8380 testing with a Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS) and ASPEED on Ubuntu 22.10 via the Phoronix Test Suite.
0xd000390:
Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.5.0-060500rc4daily20230804-generic (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
0xd0003a5:
Processor: 2 x Intel Xeon Platinum 8380 @ 3.40GHz (80 Cores / 160 Threads), Motherboard: Intel M50CYP2SB2U (SE5C6200.86B.0022.D08.2103221623 BIOS), Chipset: Intel Ice Lake IEH, Memory: 512GB, Disk: 7682GB INTEL SSDPF2KX076TZ, Graphics: ASPEED, Monitor: VE228, Network: 2 x Intel X710 for 10GBASE-T + 2 x Intel E810-C for QSFP
OS: Ubuntu 22.10, Kernel: 6.5.0-rc5-phx-tues (x86_64), Desktop: GNOME Shell 43.0, Display Server: X Server 1.21.1.3, Vulkan: 1.3.224, Compiler: GCC 12.2.0, File-System: ext4, Screen Resolution: 1920x1080
TensorFlow 2.12
Device: CPU - Batch Size: 512 - Model: ResNet-50
images/sec > Higher Is Better
0xd000390 . 85.97 |============================================================
0xd0003a5 . 84.84 |===========================================================
TensorFlow 2.12
Device: CPU - Batch Size: 256 - Model: ResNet-50
images/sec > Higher Is Better
0xd000390 . 83.89 |==========================================================
0xd0003a5 . 86.93 |============================================================
libxsmm 2-1.17-3645
M N K: 128
GFLOPS/s > Higher Is Better
0xd000390 . 1941.1 |==========================================================
0xd0003a5 . 1978.9 |===========================================================
ONNX Runtime 1.14
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 110.32 |=======================================================
0xd0003a5 . 117.55 |===========================================================
ONNX Runtime 1.14
Model: fcn-resnet101-11 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 9.08067 |==========================================================
0xd0003a5 . 8.59908 |=======================================================
OpenVKL 1.3.1
Benchmark: vklBenchmark ISPC
Items / Sec > Higher Is Better
0xd000390 . 912 |==============================================================
0xd0003a5 . 856 |==========================================================
oneDNN 3.1
Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
0xd000390 . 832.45 |===========================================================
0xd0003a5 . 816.51 |==========================================================
ONNX Runtime 1.14
Model: yolov4 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 85.80 |=========================================================
0xd0003a5 . 89.83 |============================================================
ONNX Runtime 1.14
Model: yolov4 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 11.66 |============================================================
0xd0003a5 . 11.15 |=========================================================
ONNX Runtime 1.14
Model: GPT-2 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 5.54783 |==========================================================
0xd0003a5 . 5.28095 |=======================================================
ONNX Runtime 1.14
Model: GPT-2 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 180.16 |========================================================
0xd0003a5 . 190.57 |===========================================================
ONNX Runtime 1.14
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 25.57 |===========================================================
0xd0003a5 . 25.86 |============================================================
ONNX Runtime 1.14
Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 39.12 |============================================================
0xd0003a5 . 38.72 |===========================================================
ONNX Runtime 1.14
Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 1.43407 |=======================================================
0xd0003a5 . 1.51029 |==========================================================
ONNX Runtime 1.14
Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 696.73 |===========================================================
0xd0003a5 . 664.54 |========================================================
ONNX Runtime 1.14
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 6.31428 |=======================================================
0xd0003a5 . 6.71968 |==========================================================
ONNX Runtime 1.14
Model: super-resolution-10 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 158.36 |===========================================================
0xd0003a5 . 158.01 |===========================================================
QMCPACK 3.16
Input: FeCO6_b3lyp_gms
Total Execution Time - Seconds < Lower Is Better
0xd000390 . 268.56 |===========================================================
0xd0003a5 . 263.23 |==========================================================
ONNX Runtime 1.14
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 59.84 |==========================================================
0xd0003a5 . 61.51 |============================================================
ONNX Runtime 1.14
Model: bertsquad-12 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 16.71 |============================================================
0xd0003a5 . 16.31 |===========================================================
libxsmm 2-1.17-3645
M N K: 256
GFLOPS/s > Higher Is Better
0xd000390 . 594.6 |===========================================================
0xd0003a5 . 600.2 |============================================================
TensorFlow 2.12
Device: CPU - Batch Size: 512 - Model: GoogLeNet
images/sec > Higher Is Better
0xd000390 . 317.27 |==========================================================
0xd0003a5 . 323.79 |===========================================================
OSPRay 2.12
Benchmark: particle_volume/pathtracer/real_time
Items Per Second > Higher Is Better
0xd000390 . 150.28 |===========================================================
0xd0003a5 . 136.85 |======================================================
Timed MrBayes Analysis 3.2.7
Primate Phylogeny Analysis
Seconds < Lower Is Better
0xd000390 . 166.53 |===========================================================
0xd0003a5 . 165.42 |===========================================================
ONNX Runtime 1.14
Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
Inference Time Cost (ms) < Lower Is Better
0xd000390 . 4.52403 |=========================================================
0xd0003a5 . 4.62085 |==========================================================
ONNX Runtime 1.14
Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard
Inferences Per Second > Higher Is Better
0xd000390 . 221.02 |===========================================================
0xd0003a5 . 216.48 |==========================================================
QMCPACK 3.16
Input: FeCO6_b3lyp_gms
Total Execution Time - Seconds < Lower Is Better
0xd000390 . 147.51 |=================================================
0xd0003a5 . 178.19 |===========================================================
Palabos 2.3
Grid Size: 100
Mega Site Updates Per Second > Higher Is Better
0xd000390 . 312.20 |===========================================================
0xd0003a5 . 312.53 |===========================================================
Cpuminer-Opt 3.20.3
Algorithm: Garlicoin
kH/s > Higher Is Better
0xd000390 . 29203.00 |=========================================================
0xd0003a5 . 22086.25 |===========================================
OSPRay 2.12
Benchmark: particle_volume/scivis/real_time
Items Per Second > Higher Is Better
0xd000390 . 24.95 |============================================================
0xd0003a5 . 16.38 |=======================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 405.97 |=========================================================
0xd0003a5 . 421.75 |===========================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 98.34 |============================================================
0xd0003a5 . 94.26 |==========================================================
Palabos 2.3
Grid Size: 400
Mega Site Updates Per Second > Higher Is Better
0xd000390 . 388.48 |==========================================================
0xd0003a5 . 393.84 |===========================================================
QMCPACK 3.16
Input: Li2_STO_ae
Total Execution Time - Seconds < Lower Is Better
0xd000390 . 124.23 |===========================================================
0xd0003a5 . 123.26 |===========================================================
Palabos 2.3
Grid Size: 500
Mega Site Updates Per Second > Higher Is Better
0xd000390 . 413.21 |==========================================================
0xd0003a5 . 417.48 |===========================================================
NCNN 20230517
Target: CPU - Model: FastestDet
ms < Lower Is Better
0xd000390 . 10.20 |============================================================
0xd0003a5 . 9.71 |=========================================================
NCNN 20230517
Target: CPU - Model: vision_transformer
ms < Lower Is Better
0xd000390 . 46.92 |============================================================
0xd0003a5 . 45.23 |==========================================================
NCNN 20230517
Target: CPU - Model: regnety_400m
ms < Lower Is Better
0xd000390 . 45.54 |============================================================
0xd0003a5 . 38.85 |===================================================
NCNN 20230517
Target: CPU - Model: squeezenet_ssd
ms < Lower Is Better
0xd000390 . 15.34 |=========================================================
0xd0003a5 . 16.10 |============================================================
NCNN 20230517
Target: CPU - Model: yolov4-tiny
ms < Lower Is Better
0xd000390 . 23.71 |===========================================================
0xd0003a5 . 24.10 |============================================================
NCNN 20230517
Target: CPU - Model: resnet50
ms < Lower Is Better
0xd000390 . 17.15 |========================================================
0xd0003a5 . 18.51 |============================================================
NCNN 20230517
Target: CPU - Model: alexnet
ms < Lower Is Better
0xd000390 . 5.39 |============================================================
0xd0003a5 . 5.46 |=============================================================
NCNN 20230517
Target: CPU - Model: resnet18
ms < Lower Is Better
0xd000390 . 8.97 |==========================================================
0xd0003a5 . 9.42 |=============================================================
NCNN 20230517
Target: CPU - Model: vgg16
ms < Lower Is Better
0xd000390 . 23.86 |========================================================
0xd0003a5 . 25.71 |============================================================
NCNN 20230517
Target: CPU - Model: googlenet
ms < Lower Is Better
0xd000390 . 15.36 |========================================================
0xd0003a5 . 16.58 |============================================================
NCNN 20230517
Target: CPU - Model: blazeface
ms < Lower Is Better
0xd000390 . 4.35 |==========================================================
0xd0003a5 . 4.54 |=============================================================
NCNN 20230517
Target: CPU - Model: efficientnet-b0
ms < Lower Is Better
0xd000390 . 11.62 |============================================================
0xd0003a5 . 11.71 |============================================================
NCNN 20230517
Target: CPU - Model: mnasnet
ms < Lower Is Better
0xd000390 . 7.57 |=============================================================
0xd0003a5 . 7.41 |============================================================
NCNN 20230517
Target: CPU - Model: shufflenet-v2
ms < Lower Is Better
0xd000390 . 9.89 |=============================================================
0xd0003a5 . 9.76 |============================================================
NCNN 20230517
Target: CPU-v3-v3 - Model: mobilenet-v3
ms < Lower Is Better
0xd000390 . 8.88 |=============================================================
0xd0003a5 . 8.76 |============================================================
NCNN 20230517
Target: CPU-v2-v2 - Model: mobilenet-v2
ms < Lower Is Better
0xd000390 . 8.03 |=============================================================
0xd0003a5 . 7.96 |============================================================
NCNN 20230517
Target: CPU - Model: mobilenet
ms < Lower Is Better
0xd000390 . 15.46 |===========================================================
0xd0003a5 . 15.66 |============================================================
VVenC 1.9
Video Input: Bosphorus 4K - Video Preset: Fast
Frames Per Second > Higher Is Better
0xd000390 . 5.722 |============================================================
0xd0003a5 . 5.705 |============================================================
OSPRay 2.12
Benchmark: particle_volume/ao/real_time
Items Per Second > Higher Is Better
0xd000390 . 24.75 |============================================================
0xd0003a5 . 16.46 |========================================
TensorFlow 2.12
Device: CPU - Batch Size: 256 - Model: GoogLeNet
images/sec > Higher Is Better
0xd000390 . 309.63 |=========================================================
0xd0003a5 . 321.72 |===========================================================
Cpuminer-Opt 3.20.3
Algorithm: Myriad-Groestl
kH/s > Higher Is Better
0xd000390 . 43127 |============================================================
0xd0003a5 . 43450 |============================================================
Laghos 3.1
Test: Sedov Blast Wave, ube_922_hex.mesh
Major Kernels Total Rate > Higher Is Better
0xd000390 . 385.89 |===========================================================
0xd0003a5 . 386.08 |===========================================================
oneDNN 3.1
Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
0xd000390 . 524.38 |===========================================================
0xd0003a5 . 521.74 |===========================================================
simdjson 2.0
Throughput Test: PartialTweets
GB/s > Higher Is Better
0xd000390 . 4.62 |===========================================================
0xd0003a5 . 4.77 |=============================================================
simdjson 2.0
Throughput Test: DistinctUserID
GB/s > Higher Is Better
0xd000390 . 5.52 |===========================================================
0xd0003a5 . 5.71 |=============================================================
simdjson 2.0
Throughput Test: TopTweet
GB/s > Higher Is Better
0xd000390 . 5.60 |===========================================================
0xd0003a5 . 5.75 |=============================================================
TensorFlow 2.12
Device: CPU - Batch Size: 512 - Model: AlexNet
images/sec > Higher Is Better
0xd000390 . 760.15 |=====================================================
0xd0003a5 . 839.41 |===========================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 93.54 |=======================================================
0xd0003a5 . 100.19 |===========================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 426.95 |===========================================================
0xd0003a5 . 398.72 |=======================================================
OpenVINO 2022.3
Model: Person Detection FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 1490.76 |==========================================================
0xd0003a5 . 1496.19 |==========================================================
OpenVINO 2022.3
Model: Person Detection FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 13.29 |============================================================
0xd0003a5 . 13.25 |============================================================
OpenVINO 2022.3
Model: Person Detection FP32 - Device: CPU
ms < Lower Is Better
0xd000390 . 1517.69 |==========================================================
0xd0003a5 . 1519.84 |==========================================================
OpenVINO 2022.3
Model: Person Detection FP32 - Device: CPU
FPS > Higher Is Better
0xd000390 . 13.03 |============================================================
0xd0003a5 . 13.04 |============================================================
OpenVINO 2022.3
Model: Face Detection FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 827.51 |===========================================================
0xd0003a5 . 823.09 |===========================================================
OpenVINO 2022.3
Model: Face Detection FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 24.04 |============================================================
0xd0003a5 . 24.18 |============================================================
OpenVINO 2022.3
Model: Face Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
0xd000390 . 209.31 |===========================================================
0xd0003a5 . 209.02 |===========================================================
OpenVINO 2022.3
Model: Face Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
0xd000390 . 95.42 |============================================================
0xd0003a5 . 95.54 |============================================================
OpenVINO 2022.3
Model: Machine Translation EN To DE FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 79.47 |============================================================
0xd0003a5 . 78.18 |===========================================================
OpenVINO 2022.3
Model: Machine Translation EN To DE FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 251.00 |==========================================================
0xd0003a5 . 255.09 |===========================================================
OpenVINO 2022.3
Model: Person Vehicle Bike Detection FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 9.77 |=============================================================
0xd0003a5 . 9.63 |============================================================
OpenVINO 2022.3
Model: Person Vehicle Bike Detection FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 2039.63 |=========================================================
0xd0003a5 . 2070.72 |==========================================================
VVenC 1.9
Video Input: Bosphorus 4K - Video Preset: Faster
Frames Per Second > Higher Is Better
0xd000390 . 10.36 |============================================================
0xd0003a5 . 10.42 |============================================================
OpenVINO 2022.3
Model: Weld Porosity Detection FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 33.89 |============================================================
0xd0003a5 . 33.63 |============================================================
OpenVINO 2022.3
Model: Weld Porosity Detection FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 2344.97 |==========================================================
0xd0003a5 . 2362.84 |==========================================================
OpenVINO 2022.3
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
ms < Lower Is Better
0xd000390 . 1.16 |=============================================================
0xd0003a5 . 1.16 |=============================================================
OpenVINO 2022.3
Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU
FPS > Higher Is Better
0xd000390 . 67604.00 |=========================================================
0xd0003a5 . 67754.34 |=========================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 37.99 |==================================================
0xd0003a5 . 45.96 |============================================================
Neural Magic DeepSparse 1.5
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 1051.79 |==========================================================
0xd0003a5 . 869.04 |================================================
OpenVINO 2022.3
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
0xd000390 . 8.50 |=============================================================
0xd0003a5 . 8.48 |=============================================================
OpenVINO 2022.3
Model: Weld Porosity Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
0xd000390 . 9396.52 |==========================================================
0xd0003a5 . 9419.77 |==========================================================
OpenVINO 2022.3
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 1.33 |=============================================================
0xd0003a5 . 1.33 |=============================================================
OpenVINO 2022.3
Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 59274.06 |=========================================================
0xd0003a5 . 59377.96 |=========================================================
OpenVINO 2022.3
Model: Vehicle Detection FP16-INT8 - Device: CPU
ms < Lower Is Better
0xd000390 . 4.51 |=============================================================
0xd0003a5 . 4.49 |=============================================================
OpenVINO 2022.3
Model: Vehicle Detection FP16-INT8 - Device: CPU
FPS > Higher Is Better
0xd000390 . 4419.17 |==========================================================
0xd0003a5 . 4442.98 |==========================================================
OpenVINO 2022.3
Model: Vehicle Detection FP16 - Device: CPU
ms < Lower Is Better
0xd000390 . 17.80 |============================================================
0xd0003a5 . 17.87 |============================================================
OpenVINO 2022.3
Model: Vehicle Detection FP16 - Device: CPU
FPS > Higher Is Better
0xd000390 . 1121.56 |==========================================================
0xd0003a5 . 1117.09 |==========================================================
SVT-HEVC 1.5.0
Tuning: 1 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 10.46 |============================================================
0xd0003a5 . 10.45 |============================================================
simdjson 2.0
Throughput Test: Kostya
GB/s > Higher Is Better
0xd000390 . 2.61 |=======================================================
0xd0003a5 . 2.87 |=============================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 474.69 |==========================================================
0xd0003a5 . 478.85 |===========================================================
Neural Magic DeepSparse 1.5
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 84.02 |============================================================
0xd0003a5 . 83.20 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 17.35 |======================================================
0xd0003a5 . 19.31 |============================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 2301.16 |==========================================================
0xd0003a5 . 2068.30 |====================================================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/scivis/real_time
Items Per Second > Higher Is Better
0xd000390 . 20.58 |============================================================
0xd0003a5 . 18.46 |======================================================
OSPRay 2.12
Benchmark: gravity_spheres_volume/dim_512/ao/real_time
Items Per Second > Higher Is Better
0xd000390 . 21.08 |============================================================
0xd0003a5 . 18.89 |======================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 129.81 |========================================================
0xd0003a5 . 136.18 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 307.87 |===========================================================
0xd0003a5 . 293.12 |========================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 551.12 |===========================================================
0xd0003a5 . 553.27 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 72.07 |============================================================
0xd0003a5 . 71.89 |============================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 551.82 |===========================================================
0xd0003a5 . 555.09 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 72.18 |============================================================
0xd0003a5 . 71.54 |===========================================================
simdjson 2.0
Throughput Test: LargeRandom
GB/s > Higher Is Better
0xd000390 . 0.85 |======================================================
0xd0003a5 . 0.96 |=============================================================
VP9 libvpx Encoding 1.13
Speed: Speed 5 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 12.63 |============================================================
0xd0003a5 . 12.33 |===========================================================
miniBUDE 20210901
Implementation: OpenMP - Input Deck: BM2
Billion Interactions/s > Higher Is Better
0xd000390 . 101.08 |=========================================================
0xd0003a5 . 104.05 |===========================================================
miniBUDE 20210901
Implementation: OpenMP - Input Deck: BM2
GFInst/s > Higher Is Better
0xd000390 . 2526.89 |========================================================
0xd0003a5 . 2601.21 |==========================================================
SPECFEM3D 4.0
Model: Water-layered Halfspace
Seconds < Lower Is Better
0xd000390 . 31.15 |============================================================
0xd0003a5 . 31.38 |============================================================
oneDNN 3.1
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
0xd000390 . 2.59271 |==========================================================
0xd0003a5 . 2.57437 |==========================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 173.79 |===========================================================
0xd0003a5 . 172.56 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 230.05 |===========================================================
0xd0003a5 . 231.43 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 43.30 |============================================================
0xd0003a5 . 42.94 |============================================================
Neural Magic DeepSparse 1.5
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 922.93 |===========================================================
0xd0003a5 . 930.75 |===========================================================
Laghos 3.1
Test: Triple Point Problem
Major Kernels Total Rate > Higher Is Better
0xd000390 . 256.27 |===========================================================
0xd0003a5 . 256.87 |===========================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 62.01 |==========================================================
0xd0003a5 . 64.44 |============================================================
Neural Magic DeepSparse 1.5
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 644.44 |===========================================================
0xd0003a5 . 620.25 |=========================================================
QMCPACK 3.16
Input: simple-H2O
Total Execution Time - Seconds < Lower Is Better
0xd000390 . 39.56 |==========================================================
0xd0003a5 . 41.25 |============================================================
TensorFlow 2.12
Device: CPU - Batch Size: 256 - Model: AlexNet
images/sec > Higher Is Better
0xd000390 . 723.27 |=======================================================
0xd0003a5 . 781.25 |===========================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 94.61 |===========================================================
0xd0003a5 . 96.94 |============================================================
Neural Magic DeepSparse 1.5
Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 422.12 |===========================================================
0xd0003a5 . 412.05 |==========================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 39.73 |===========================================================
0xd0003a5 . 40.45 |============================================================
Neural Magic DeepSparse 1.5
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 1005.58 |==========================================================
0xd0003a5 . 987.55 |=========================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 39.70 |============================================================
0xd0003a5 . 39.41 |============================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 1006.34 |==========================================================
0xd0003a5 . 1013.82 |==========================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
ms/batch < Lower Is Better
0xd000390 . 5.2210 |=======================================================
0xd0003a5 . 5.6210 |===========================================================
Neural Magic DeepSparse 1.5
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
items/sec > Higher Is Better
0xd000390 . 7633.26 |==========================================================
0xd0003a5 . 7092.50 |======================================================
libxsmm 2-1.17-3645
M N K: 64
GFLOPS/s > Higher Is Better
0xd000390 . 1098.8 |=======================================================
0xd0003a5 . 1177.1 |===========================================================
SPECFEM3D 4.0
Model: Layered Halfspace
Seconds < Lower Is Better
0xd000390 . 29.50 |============================================================
0xd0003a5 . 29.37 |============================================================
Blender 3.6
Blend File: Fishy Cat - Compute: CPU-Only
Seconds < Lower Is Better
0xd000390 . 30.74 |============================================================
0xd0003a5 . 30.90 |============================================================
libxsmm 2-1.17-3645
M N K: 32
GFLOPS/s > Higher Is Better
0xd000390 . 604.7 |============================================================
0xd0003a5 . 609.2 |============================================================
Cpuminer-Opt 3.20.3
Algorithm: LBC, LBRY Credits
kH/s > Higher Is Better
0xd000390 . 421660 |===========================================================
0xd0003a5 . 423130 |===========================================================
Cpuminer-Opt 3.20.3
Algorithm: scrypt
kH/s > Higher Is Better
0xd000390 . 2319.31 |==========================================================
0xd0003a5 . 2321.74 |==========================================================
Cpuminer-Opt 3.20.3
Algorithm: Skeincoin
kH/s > Higher Is Better
0xd000390 . 613333 |===========================================================
0xd0003a5 . 617130 |===========================================================
Cpuminer-Opt 3.20.3
Algorithm: Blake-2 S
kH/s > Higher Is Better
0xd000390 . 4462327 |==========================================================
0xd0003a5 . 4466653 |==========================================================
Cpuminer-Opt 3.20.3
Algorithm: Magi
kH/s > Higher Is Better
0xd000390 . 2309.47 |==========================================================
0xd0003a5 . 2308.66 |==========================================================
Cpuminer-Opt 3.20.3
Algorithm: Deepcoin
kH/s > Higher Is Better
0xd000390 . 64677 |============================================================
0xd0003a5 . 64897 |============================================================
Cpuminer-Opt 3.20.3
Algorithm: Triple SHA-256, Onecoin
kH/s > Higher Is Better
0xd000390 . 1332237 |==========================================================
0xd0003a5 . 1333117 |==========================================================
Cpuminer-Opt 3.20.3
Algorithm: x25x
kH/s > Higher Is Better
0xd000390 . 2659.17 |==========================================================
0xd0003a5 . 2659.55 |==========================================================
Cpuminer-Opt 3.20.3
Algorithm: Quad SHA-256, Pyrite
kH/s > Higher Is Better
0xd000390 . 921730 |===========================================================
0xd0003a5 . 926277 |===========================================================
GROMACS 2023
Implementation: MPI CPU - Input: water_GMX50_bare
Ns Per Day > Higher Is Better
0xd000390 . 9.234 |============================================================
0xd0003a5 . 9.094 |===========================================================
Blender 3.6
Blend File: BMW27 - Compute: CPU-Only
Seconds < Lower Is Better
0xd000390 . 23.83 |============================================================
0xd0003a5 . 23.72 |============================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
0xd000390 . 3.90360 |==========================================================
0xd0003a5 . 3.91322 |==========================================================
SPECFEM3D 4.0
Model: Homogeneous Halfspace
Seconds < Lower Is Better
0xd000390 . 18.02 |============================================================
0xd0003a5 . 17.75 |===========================================================
dav1d 1.2.1
Video Input: Chimera 1080p
FPS > Higher Is Better
0xd000390 . 515.81 |===========================================================
0xd0003a5 . 514.58 |===========================================================
SPECFEM3D 4.0
Model: Tomographic Model
Seconds < Lower Is Better
0xd000390 . 14.57 |============================================================
0xd0003a5 . 14.15 |==========================================================
SPECFEM3D 4.0
Model: Mount St. Helens
Seconds < Lower Is Better
0xd000390 . 13.15 |============================================================
0xd0003a5 . 12.95 |===========================================================
Intel Open Image Denoise 2.0
Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only
Images / Sec > Higher Is Better
0xd000390 . 1.46 |=============================================================
0xd0003a5 . 1.46 |=============================================================
dav1d 1.2.1
Video Input: Summer Nature 4K
FPS > Higher Is Better
0xd000390 . 281.36 |===========================================================
0xd0003a5 . 280.84 |===========================================================
SVT-AV1 1.6
Encoder Mode: Preset 8 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 67.17 |============================================================
0xd0003a5 . 66.46 |===========================================================
Remhos 1.0
Test: Sample Remap Example
Seconds < Lower Is Better
0xd000390 . 12.25 |===========================================================
0xd0003a5 . 12.40 |============================================================
CloverLeaf
Lagrangian-Eulerian Hydrodynamics
Seconds < Lower Is Better
0xd000390 . 12.04 |============================================================
0xd0003a5 . 11.98 |============================================================
Xcompact3d Incompact3d 2021-03-11
Input: input.i3d 193 Cells Per Direction
Seconds < Lower Is Better
0xd000390 . 11.02 |============================================================
0xd0003a5 . 11.00 |============================================================
miniBUDE 20210901
Implementation: OpenMP - Input Deck: BM1
Billion Interactions/s > Higher Is Better
0xd000390 . 94.14 |============================================================
0xd0003a5 . 94.90 |============================================================
miniBUDE 20210901
Implementation: OpenMP - Input Deck: BM1
GFInst/s > Higher Is Better
0xd000390 . 2353.39 |==========================================================
0xd0003a5 . 2372.42 |==========================================================
Embree 4.1
Binary: Pathtracer ISPC - Model: Crown
Frames Per Second > Higher Is Better
0xd000390 . 88.19 |============================================================
0xd0003a5 . 83.46 |=========================================================
Intel Open Image Denoise 2.0
Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only
Images / Sec > Higher Is Better
0xd000390 . 3.03 |=============================================================
0xd0003a5 . 3.03 |=============================================================
Embree 4.1
Binary: Pathtracer ISPC - Model: Asian Dragon
Frames Per Second > Higher Is Better
0xd000390 . 104.68 |===========================================================
0xd0003a5 . 101.10 |=========================================================
SVT-HEVC 1.5.0
Tuning: 7 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 138.75 |===========================================================
0xd0003a5 . 138.49 |===========================================================
oneDNN 3.1
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
0xd000390 . 2.06936 |==========================================================
0xd0003a5 . 2.06967 |==========================================================
SVT-HEVC 1.5.0
Tuning: 10 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 184.38 |===========================================================
0xd0003a5 . 182.74 |==========================================================
SVT-AV1 1.6
Encoder Mode: Preset 12 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 180.97 |===========================================================
0xd0003a5 . 177.72 |==========================================================
SVT-AV1 1.6
Encoder Mode: Preset 13 - Input: Bosphorus 4K
Frames Per Second > Higher Is Better
0xd000390 . 175.10 |==========================================================
0xd0003a5 . 177.20 |===========================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256
GFLOP/s > Higher Is Better
0xd000390 . 46.35 |===========================================================
0xd0003a5 . 46.98 |============================================================
oneDNN 3.1
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
ms < Lower Is Better
0xd000390 . 3.62526 |==========================================================
0xd0003a5 . 3.62266 |==========================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256
GFLOP/s > Higher Is Better
0xd000390 . 93.75 |===========================================================
0xd0003a5 . 94.86 |============================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256
GFLOP/s > Higher Is Better
0xd000390 . 101.98 |==========================================================
0xd0003a5 . 102.92 |===========================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256
GFLOP/s > Higher Is Better
0xd000390 . 224.42 |==========================================================
0xd0003a5 . 226.78 |===========================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128
GFLOP/s > Higher Is Better
0xd000390 . 154.73 |=========================================================
0xd0003a5 . 159.10 |===========================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128
GFLOP/s > Higher Is Better
0xd000390 . 144.94 |========================================================
0xd0003a5 . 153.79 |===========================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128
GFLOP/s > Higher Is Better
0xd000390 . 93.09 |===========================================================
0xd0003a5 . 93.92 |============================================================
HeFFTe - Highly Efficient FFT for Exascale 2.3
Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128
GFLOP/s > Higher Is Better
0xd000390 . 195.20 |==========================================================
0xd0003a5 . 198.87 |===========================================================