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 miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM1 GFInst/s > Higher Is Better 0xd000390 . 2353.39 |========================================================== 0xd0003a5 . 2372.42 |========================================================== 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: BM2 GFInst/s > Higher Is Better 0xd000390 . 2526.89 |======================================================== 0xd0003a5 . 2601.21 |========================================================== miniBUDE 20210901 Implementation: OpenMP - Input Deck: BM2 Billion Interactions/s > Higher Is Better 0xd000390 . 101.08 |========================================================= 0xd0003a5 . 104.05 |=========================================================== CloverLeaf Lagrangian-Eulerian Hydrodynamics Seconds < Lower Is Better 0xd000390 . 12.04 |============================================================ 0xd0003a5 . 11.98 |============================================================ libxsmm 2-1.17-3645 M N K: 128 GFLOPS/s > Higher Is Better 0xd000390 . 1941.1 |========================================================== 0xd0003a5 . 1978.9 |=========================================================== libxsmm 2-1.17-3645 M N K: 256 GFLOPS/s > Higher Is Better 0xd000390 . 594.6 |=========================================================== 0xd0003a5 . 600.2 |============================================================ libxsmm 2-1.17-3645 M N K: 32 GFLOPS/s > Higher Is Better 0xd000390 . 604.7 |============================================================ 0xd0003a5 . 609.2 |============================================================ libxsmm 2-1.17-3645 M N K: 64 GFLOPS/s > Higher Is Better 0xd000390 . 1098.8 |======================================================= 0xd0003a5 . 1177.1 |=========================================================== Laghos 3.1 Test: Triple Point Problem Major Kernels Total Rate > Higher Is Better 0xd000390 . 256.27 |=========================================================== 0xd0003a5 . 256.87 |=========================================================== Laghos 3.1 Test: Sedov Blast Wave, ube_922_hex.mesh Major Kernels Total Rate > Higher Is Better 0xd000390 . 385.89 |=========================================================== 0xd0003a5 . 386.08 |=========================================================== 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: 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: 128 GFLOP/s > Higher Is Better 0xd000390 . 195.20 |========================================================== 0xd0003a5 . 198.87 |=========================================================== 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: 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: c2c - Backend: FFTW - Precision: double - X Y Z: 256 GFLOP/s > Higher Is Better 0xd000390 . 46.35 |=========================================================== 0xd0003a5 . 46.98 |============================================================ 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: r2c - Backend: FFTW - Precision: double - X Y Z: 256 GFLOP/s > Higher Is Better 0xd000390 . 93.75 |=========================================================== 0xd0003a5 . 94.86 |============================================================ Palabos 2.3 Grid Size: 100 Mega Site Updates Per Second > Higher Is Better 0xd000390 . 312.20 |=========================================================== 0xd0003a5 . 312.53 |=========================================================== Palabos 2.3 Grid Size: 400 Mega Site Updates Per Second > Higher Is Better 0xd000390 . 388.48 |========================================================== 0xd0003a5 . 393.84 |=========================================================== Palabos 2.3 Grid Size: 500 Mega Site Updates Per Second > Higher Is Better 0xd000390 . 413.21 |========================================================== 0xd0003a5 . 417.48 |=========================================================== Xcompact3d Incompact3d 2021-03-11 Input: input.i3d 193 Cells Per Direction Seconds < Lower Is Better 0xd000390 . 11.02 |============================================================ 0xd0003a5 . 11.00 |============================================================ Remhos 1.0 Test: Sample Remap Example Seconds < Lower Is Better 0xd000390 . 12.25 |=========================================================== 0xd0003a5 . 12.40 |============================================================ SPECFEM3D 4.0 Model: Mount St. Helens Seconds < Lower Is Better 0xd000390 . 13.15 |============================================================ 0xd0003a5 . 12.95 |=========================================================== SPECFEM3D 4.0 Model: Layered Halfspace Seconds < Lower Is Better 0xd000390 . 29.50 |============================================================ 0xd0003a5 . 29.37 |============================================================ SPECFEM3D 4.0 Model: Tomographic Model Seconds < Lower Is Better 0xd000390 . 14.57 |============================================================ 0xd0003a5 . 14.15 |========================================================== SPECFEM3D 4.0 Model: Homogeneous Halfspace Seconds < Lower Is Better 0xd000390 . 18.02 |============================================================ 0xd0003a5 . 17.75 |=========================================================== SPECFEM3D 4.0 Model: Water-layered Halfspace Seconds < Lower Is Better 0xd000390 . 31.15 |============================================================ 0xd0003a5 . 31.38 |============================================================ simdjson 2.0 Throughput Test: Kostya GB/s > Higher Is Better 0xd000390 . 2.61 |======================================================= 0xd0003a5 . 2.87 |============================================================= simdjson 2.0 Throughput Test: TopTweet GB/s > Higher Is Better 0xd000390 . 5.60 |=========================================================== 0xd0003a5 . 5.75 |============================================================= simdjson 2.0 Throughput Test: LargeRandom GB/s > Higher Is Better 0xd000390 . 0.85 |====================================================== 0xd0003a5 . 0.96 |============================================================= 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 |============================================================= Embree 4.1 Binary: Pathtracer ISPC - Model: Crown Frames Per Second > Higher Is Better 0xd000390 . 88.19 |============================================================ 0xd0003a5 . 83.46 |========================================================= Embree 4.1 Binary: Pathtracer ISPC - Model: Asian Dragon Frames Per Second > Higher Is Better 0xd000390 . 104.68 |=========================================================== 0xd0003a5 . 101.10 |========================================================= OpenVKL 1.3.1 Benchmark: vklBenchmark ISPC Items / Sec > Higher Is Better 0xd000390 . 912 |============================================================== 0xd0003a5 . 856 |========================================================== OSPRay 2.12 Benchmark: particle_volume/ao/real_time Items Per Second > Higher Is Better 0xd000390 . 24.75 |============================================================ 0xd0003a5 . 16.46 |======================================== OSPRay 2.12 Benchmark: particle_volume/scivis/real_time Items Per Second > Higher Is Better 0xd000390 . 24.95 |============================================================ 0xd0003a5 . 16.38 |======================================= OSPRay 2.12 Benchmark: particle_volume/pathtracer/real_time Items Per Second > Higher Is Better 0xd000390 . 150.28 |=========================================================== 0xd0003a5 . 136.85 |====================================================== OSPRay 2.12 Benchmark: gravity_spheres_volume/dim_512/ao/real_time Items Per Second > Higher Is Better 0xd000390 . 21.08 |============================================================ 0xd0003a5 . 18.89 |====================================================== OSPRay 2.12 Benchmark: gravity_spheres_volume/dim_512/scivis/real_time Items Per Second > Higher Is Better 0xd000390 . 20.58 |============================================================ 0xd0003a5 . 18.46 |====================================================== oneDNN 3.1 Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 0xd000390 . 2.59271 |========================================================== 0xd0003a5 . 2.57437 |========================================================== oneDNN 3.1 Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 0xd000390 . 2.06936 |========================================================== 0xd0003a5 . 2.06967 |========================================================== oneDNN 3.1 Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 0xd000390 . 3.90360 |========================================================== 0xd0003a5 . 3.91322 |========================================================== oneDNN 3.1 Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 0xd000390 . 3.62526 |========================================================== 0xd0003a5 . 3.62266 |========================================================== oneDNN 3.1 Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 0xd000390 . 832.45 |=========================================================== 0xd0003a5 . 816.51 |========================================================== oneDNN 3.1 Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU ms < Lower Is Better 0xd000390 . 524.38 |=========================================================== 0xd0003a5 . 521.74 |=========================================================== Cpuminer-Opt 3.20.3 Algorithm: Magi kH/s > Higher Is Better 0xd000390 . 2309.47 |========================================================== 0xd0003a5 . 2308.66 |========================================================== Cpuminer-Opt 3.20.3 Algorithm: x25x kH/s > Higher Is Better 0xd000390 . 2659.17 |========================================================== 0xd0003a5 . 2659.55 |========================================================== Cpuminer-Opt 3.20.3 Algorithm: scrypt kH/s > Higher Is Better 0xd000390 . 2319.31 |========================================================== 0xd0003a5 . 2321.74 |========================================================== Cpuminer-Opt 3.20.3 Algorithm: Deepcoin kH/s > Higher Is Better 0xd000390 . 64677 |============================================================ 0xd0003a5 . 64897 |============================================================ Cpuminer-Opt 3.20.3 Algorithm: Blake-2 S kH/s > Higher Is Better 0xd000390 . 4462327 |========================================================== 0xd0003a5 . 4466653 |========================================================== Cpuminer-Opt 3.20.3 Algorithm: Garlicoin kH/s > Higher Is Better 0xd000390 . 29203.00 |========================================================= 0xd0003a5 . 22086.25 |=========================================== Cpuminer-Opt 3.20.3 Algorithm: Skeincoin kH/s > Higher Is Better 0xd000390 . 613333 |=========================================================== 0xd0003a5 . 617130 |=========================================================== Cpuminer-Opt 3.20.3 Algorithm: Myriad-Groestl kH/s > Higher Is Better 0xd000390 . 43127 |============================================================ 0xd0003a5 . 43450 |============================================================ Cpuminer-Opt 3.20.3 Algorithm: LBC, LBRY Credits kH/s > Higher Is Better 0xd000390 . 421660 |=========================================================== 0xd0003a5 . 423130 |=========================================================== Cpuminer-Opt 3.20.3 Algorithm: Quad SHA-256, Pyrite kH/s > Higher Is Better 0xd000390 . 921730 |=========================================================== 0xd0003a5 . 926277 |=========================================================== Cpuminer-Opt 3.20.3 Algorithm: Triple SHA-256, Onecoin kH/s > Higher Is Better 0xd000390 . 1332237 |========================================================== 0xd0003a5 . 1333117 |========================================================== GROMACS 2023 Implementation: MPI CPU - Input: water_GMX50_bare Ns Per Day > Higher Is Better 0xd000390 . 9.234 |============================================================ 0xd0003a5 . 9.094 |=========================================================== TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: AlexNet images/sec > Higher Is Better 0xd000390 . 723.27 |======================================================= 0xd0003a5 . 781.25 |=========================================================== TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: AlexNet images/sec > Higher Is Better 0xd000390 . 760.15 |===================================================== 0xd0003a5 . 839.41 |=========================================================== TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: GoogLeNet images/sec > Higher Is Better 0xd000390 . 309.63 |========================================================= 0xd0003a5 . 321.72 |=========================================================== TensorFlow 2.12 Device: CPU - Batch Size: 256 - Model: ResNet-50 images/sec > Higher Is Better 0xd000390 . 83.89 |========================================================== 0xd0003a5 . 86.93 |============================================================ TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: GoogLeNet images/sec > Higher Is Better 0xd000390 . 317.27 |========================================================== 0xd0003a5 . 323.79 |=========================================================== TensorFlow 2.12 Device: CPU - Batch Size: 512 - Model: ResNet-50 images/sec > Higher Is Better 0xd000390 . 85.97 |============================================================ 0xd0003a5 . 84.84 |=========================================================== 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 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 Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream items/sec > Higher Is Better 0xd000390 . 2301.16 |========================================================== 0xd0003a5 . 2068.30 |==================================================== 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 Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream items/sec > Higher Is Better 0xd000390 . 922.93 |=========================================================== 0xd0003a5 . 930.75 |=========================================================== 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 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 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: 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, Baseline - Scenario: Asynchronous Multi-Stream ms/batch < Lower Is Better 0xd000390 . 39.70 |============================================================ 0xd0003a5 . 39.41 |============================================================ 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 |====================================================== 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: 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 Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream ms/batch < Lower Is Better 0xd000390 . 94.61 |=========================================================== 0xd0003a5 . 96.94 |============================================================ 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 |========================================================== 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: 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: 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 Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream items/sec > Higher Is Better 0xd000390 . 426.95 |=========================================================== 0xd0003a5 . 398.72 |======================================================= 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: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream items/sec > Higher Is Better 0xd000390 . 644.44 |=========================================================== 0xd0003a5 . 620.25 |========================================================= 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: 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: 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: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream items/sec > Higher Is Better 0xd000390 . 1051.79 |========================================================== 0xd0003a5 . 869.04 |================================================ 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: 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 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 Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream items/sec > Higher Is Better 0xd000390 . 72.18 |============================================================ 0xd0003a5 . 71.54 |=========================================================== 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 |=========================================================== 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 - Device: CPU ms < Lower Is Better 0xd000390 . 827.51 |=========================================================== 0xd0003a5 . 823.09 |=========================================================== 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 FP16 - Device: CPU ms < Lower Is Better 0xd000390 . 1490.76 |========================================================== 0xd0003a5 . 1496.19 |========================================================== OpenVINO 2022.3 Model: Person Detection FP32 - Device: CPU FPS > Higher Is Better 0xd000390 . 13.03 |============================================================ 0xd0003a5 . 13.04 |============================================================ OpenVINO 2022.3 Model: Person Detection FP32 - Device: CPU ms < Lower Is Better 0xd000390 . 1517.69 |========================================================== 0xd0003a5 . 1519.84 |========================================================== OpenVINO 2022.3 Model: Vehicle Detection FP16 - Device: CPU FPS > Higher Is Better 0xd000390 . 1121.56 |========================================================== 0xd0003a5 . 1117.09 |========================================================== OpenVINO 2022.3 Model: Vehicle Detection FP16 - Device: CPU ms < Lower Is Better 0xd000390 . 17.80 |============================================================ 0xd0003a5 . 17.87 |============================================================ OpenVINO 2022.3 Model: Face Detection FP16-INT8 - Device: CPU FPS > Higher Is Better 0xd000390 . 95.42 |============================================================ 0xd0003a5 . 95.54 |============================================================ OpenVINO 2022.3 Model: Face Detection FP16-INT8 - Device: CPU ms < Lower Is Better 0xd000390 . 209.31 |=========================================================== 0xd0003a5 . 209.02 |=========================================================== 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-INT8 - Device: CPU ms < Lower Is Better 0xd000390 . 4.51 |============================================================= 0xd0003a5 . 4.49 |============================================================= OpenVINO 2022.3 Model: Weld Porosity Detection FP16 - Device: CPU FPS > Higher Is Better 0xd000390 . 2344.97 |========================================================== 0xd0003a5 . 2362.84 |========================================================== OpenVINO 2022.3 Model: Weld Porosity Detection FP16 - Device: CPU ms < Lower Is Better 0xd000390 . 33.89 |============================================================ 0xd0003a5 . 33.63 |============================================================ 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: Machine Translation EN To DE FP16 - Device: CPU ms < Lower Is Better 0xd000390 . 79.47 |============================================================ 0xd0003a5 . 78.18 |=========================================================== 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: Weld Porosity Detection FP16-INT8 - Device: CPU ms < Lower Is Better 0xd000390 . 8.50 |============================================================= 0xd0003a5 . 8.48 |============================================================= OpenVINO 2022.3 Model: Person Vehicle Bike Detection FP16 - Device: CPU FPS > Higher Is Better 0xd000390 . 2039.63 |========================================================= 0xd0003a5 . 2070.72 |========================================================== 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: Age Gender Recognition Retail 0013 FP16 - Device: CPU FPS > Higher Is Better 0xd000390 . 59274.06 |========================================================= 0xd0003a5 . 59377.96 |========================================================= 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-INT8 - Device: CPU FPS > Higher Is Better 0xd000390 . 67604.00 |========================================================= 0xd0003a5 . 67754.34 |========================================================= OpenVINO 2022.3 Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU ms < Lower Is Better 0xd000390 . 1.16 |============================================================= 0xd0003a5 . 1.16 |============================================================= 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: GPT-2 - Device: CPU - Executor: Standard Inference Time Cost (ms) < Lower Is Better 0xd000390 . 5.54783 |========================================================== 0xd0003a5 . 5.28095 |======================================================= 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: yolov4 - Device: CPU - Executor: Standard Inference Time Cost (ms) < Lower Is Better 0xd000390 . 85.80 |========================================================= 0xd0003a5 . 89.83 |============================================================ ONNX Runtime 1.14 Model: bertsquad-12 - Device: CPU - Executor: Standard Inferences Per Second > Higher Is Better 0xd000390 . 16.71 |============================================================ 0xd0003a5 . 16.31 |=========================================================== 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: CaffeNet 12-int8 - Device: CPU - Executor: Standard Inferences Per Second > Higher Is Better 0xd000390 . 696.73 |=========================================================== 0xd0003a5 . 664.54 |======================================================== 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: fcn-resnet101-11 - Device: CPU - Executor: Standard Inferences Per Second > Higher Is Better 0xd000390 . 9.08067 |========================================================== 0xd0003a5 . 8.59908 |======================================================= 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: ArcFace ResNet-100 - Device: CPU - Executor: Standard Inferences Per Second > Higher Is Better 0xd000390 . 39.12 |============================================================ 0xd0003a5 . 38.72 |=========================================================== 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: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard Inferences Per Second > Higher Is Better 0xd000390 . 221.02 |=========================================================== 0xd0003a5 . 216.48 |========================================================== 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: super-resolution-10 - Device: CPU - Executor: Standard Inferences Per Second > Higher Is Better 0xd000390 . 158.36 |=========================================================== 0xd0003a5 . 158.01 |=========================================================== 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 |========================================================== Timed MrBayes Analysis 3.2.7 Primate Phylogeny Analysis Seconds < Lower Is Better 0xd000390 . 166.53 |=========================================================== 0xd0003a5 . 165.42 |=========================================================== QMCPACK 3.16 Input: Li2_STO_ae Total Execution Time - Seconds < Lower Is Better 0xd000390 . 124.23 |=========================================================== 0xd0003a5 . 123.26 |=========================================================== QMCPACK 3.16 Input: simple-H2O Total Execution Time - Seconds < Lower Is Better 0xd000390 . 39.56 |========================================================== 0xd0003a5 . 41.25 |============================================================ QMCPACK 3.16 Input: FeCO6_b3lyp_gms Total Execution Time - Seconds < Lower Is Better 0xd000390 . 147.51 |================================================= 0xd0003a5 . 178.19 |=========================================================== QMCPACK 3.16 Input: FeCO6_b3lyp_gms Total Execution Time - Seconds < Lower Is Better 0xd000390 . 268.56 |=========================================================== 0xd0003a5 . 263.23 |========================================================== dav1d 1.2.1 Video Input: Chimera 1080p FPS > Higher Is Better 0xd000390 . 515.81 |=========================================================== 0xd0003a5 . 514.58 |=========================================================== 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 |=========================================================== 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 |=========================================================== SVT-HEVC 1.5.0 Tuning: 1 - Input: Bosphorus 4K Frames Per Second > Higher Is Better 0xd000390 . 10.46 |============================================================ 0xd0003a5 . 10.45 |============================================================ SVT-HEVC 1.5.0 Tuning: 7 - Input: Bosphorus 4K Frames Per Second > Higher Is Better 0xd000390 . 138.75 |=========================================================== 0xd0003a5 . 138.49 |=========================================================== SVT-HEVC 1.5.0 Tuning: 10 - Input: Bosphorus 4K Frames Per Second > Higher Is Better 0xd000390 . 184.38 |=========================================================== 0xd0003a5 . 182.74 |========================================================== VP9 libvpx Encoding 1.13 Speed: Speed 5 - Input: Bosphorus 4K Frames Per Second > Higher Is Better 0xd000390 . 12.63 |============================================================ 0xd0003a5 . 12.33 |=========================================================== VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Fast Frames Per Second > Higher Is Better 0xd000390 . 5.722 |============================================================ 0xd0003a5 . 5.705 |============================================================ VVenC 1.9 Video Input: Bosphorus 4K - Video Preset: Faster Frames Per Second > Higher Is Better 0xd000390 . 10.36 |============================================================ 0xd0003a5 . 10.42 |============================================================ 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 |============================================================= Intel Open Image Denoise 2.0 Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only Images / Sec > Higher Is Better 0xd000390 . 1.46 |============================================================= 0xd0003a5 . 1.46 |============================================================= NCNN 20230517 Target: CPU - Model: mobilenet ms < Lower Is Better 0xd000390 . 15.46 |=========================================================== 0xd0003a5 . 15.66 |============================================================ NCNN 20230517 Target: CPU-v2-v2 - Model: mobilenet-v2 ms < Lower Is Better 0xd000390 . 8.03 |============================================================= 0xd0003a5 . 7.96 |============================================================ NCNN 20230517 Target: CPU-v3-v3 - Model: mobilenet-v3 ms < Lower Is Better 0xd000390 . 8.88 |============================================================= 0xd0003a5 . 8.76 |============================================================ NCNN 20230517 Target: CPU - Model: shufflenet-v2 ms < Lower Is Better 0xd000390 . 9.89 |============================================================= 0xd0003a5 . 9.76 |============================================================ NCNN 20230517 Target: CPU - Model: mnasnet ms < Lower Is Better 0xd000390 . 7.57 |============================================================= 0xd0003a5 . 7.41 |============================================================ NCNN 20230517 Target: CPU - Model: efficientnet-b0 ms < Lower Is Better 0xd000390 . 11.62 |============================================================ 0xd0003a5 . 11.71 |============================================================ NCNN 20230517 Target: CPU - Model: blazeface ms < Lower Is Better 0xd000390 . 4.35 |========================================================== 0xd0003a5 . 4.54 |============================================================= NCNN 20230517 Target: CPU - Model: googlenet ms < Lower Is Better 0xd000390 . 15.36 |======================================================== 0xd0003a5 . 16.58 |============================================================ NCNN 20230517 Target: CPU - Model: vgg16 ms < Lower Is Better 0xd000390 . 23.86 |======================================================== 0xd0003a5 . 25.71 |============================================================ NCNN 20230517 Target: CPU - Model: resnet18 ms < Lower Is Better 0xd000390 . 8.97 |========================================================== 0xd0003a5 . 9.42 |============================================================= NCNN 20230517 Target: CPU - Model: alexnet ms < Lower Is Better 0xd000390 . 5.39 |============================================================ 0xd0003a5 . 5.46 |============================================================= NCNN 20230517 Target: CPU - Model: resnet50 ms < Lower Is Better 0xd000390 . 17.15 |======================================================== 0xd0003a5 . 18.51 |============================================================ NCNN 20230517 Target: CPU - Model: yolov4-tiny ms < Lower Is Better 0xd000390 . 23.71 |=========================================================== 0xd0003a5 . 24.10 |============================================================ NCNN 20230517 Target: CPU - Model: squeezenet_ssd ms < Lower Is Better 0xd000390 . 15.34 |========================================================= 0xd0003a5 . 16.10 |============================================================ NCNN 20230517 Target: CPU - Model: regnety_400m ms < Lower Is Better 0xd000390 . 45.54 |============================================================ 0xd0003a5 . 38.85 |=================================================== NCNN 20230517 Target: CPU - Model: vision_transformer ms < Lower Is Better 0xd000390 . 46.92 |============================================================ 0xd0003a5 . 45.23 |========================================================== NCNN 20230517 Target: CPU - Model: FastestDet ms < Lower Is Better 0xd000390 . 10.20 |============================================================ 0xd0003a5 . 9.71 |========================================================= Blender 3.6 Blend File: BMW27 - Compute: CPU-Only Seconds < Lower Is Better 0xd000390 . 23.83 |============================================================ 0xd0003a5 . 23.72 |============================================================ Blender 3.6 Blend File: Fishy Cat - Compute: CPU-Only Seconds < Lower Is Better 0xd000390 . 30.74 |============================================================ 0xd0003a5 . 30.90 |============================================================