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.

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
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AV1 2 Tests
Bioinformatics 2 Tests
C/C++ Compiler Tests 5 Tests
CPU Massive 10 Tests
Creator Workloads 12 Tests
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Fortran Tests 4 Tests
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HPC - High Performance Computing 12 Tests
Machine Learning 6 Tests
Molecular Dynamics 3 Tests
MPI Benchmarks 4 Tests
Multi-Core 14 Tests
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Intel oneAPI 6 Tests
OpenMPI Tests 10 Tests
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0xd000390
August 06 2023
  11 Hours, 56 Minutes
0xd0003a5
August 08 2023
  15 Hours, 40 Minutes
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  13 Hours, 48 Minutes
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Xeon Platinum 8380 AVX-512 Workloads Suite 1.0.0 System Test suite extracted from Xeon Platinum 8380 AVX-512 Workloads. pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/weld-porosity-detection-0001/FP16-INT8/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/openvino-1.2.0 -m models/intel/age-gender-recognition-retail-0013/FP16-INT8/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU pts/dav1d-1.14.0 -i chimera_8b_1080p.ivf Video Input: Chimera 1080p pts/dav1d-1.14.0 -i summer_nature_4k.ivf Video Input: Summer Nature 4K pts/embree-1.5.0 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/embree-1.5.0 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/svt-av1-2.9.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/svt-av1-2.9.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/svt-av1-2.9.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/svt-hevc-1.2.1 -encMode 1 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: 1 - Input: Bosphorus 4K pts/svt-hevc-1.2.1 -encMode 7 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: 7 - Input: Bosphorus 4K pts/svt-hevc-1.2.1 -encMode 10 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: 10 - Input: Bosphorus 4K pts/vpxenc-3.2.0 --cpu-used=5 ~/Bosphorus_3840x2160.y4m --width=3840 --height=2160 Speed: Speed 5 - Input: Bosphorus 4K pts/vvenc-1.9.1 -i Bosphorus_3840x2160.y4m --preset fast Video Input: Bosphorus 4K - Video Preset: Fast pts/vvenc-1.9.1 -i Bosphorus_3840x2160.y4m --preset faster Video Input: Bosphorus 4K - Video Preset: Faster pts/simdjson-2.0.1 kostya Throughput Test: Kostya pts/simdjson-2.0.1 top_tweet Throughput Test: TopTweet pts/simdjson-2.0.1 large_random Throughput Test: LargeRandom pts/simdjson-2.0.1 partial_tweets Throughput Test: PartialTweets pts/simdjson-2.0.1 distinct_user_id Throughput Test: DistinctUserID pts/heffte-1.0.0 c2c fftw float 128 128 128 Test: c2c - Backend: FFTW - Precision: float - X Y Z: 128 pts/heffte-1.0.0 c2c fftw float 256 256 256 Test: c2c - Backend: FFTW - Precision: float - X Y Z: 256 pts/heffte-1.0.0 r2c fftw float 128 128 128 Test: r2c - Backend: FFTW - Precision: float - X Y Z: 128 pts/heffte-1.0.0 r2c fftw float 256 256 256 Test: r2c - Backend: FFTW - Precision: float - X Y Z: 256 pts/heffte-1.0.0 c2c fftw double 128 128 128 Test: c2c - Backend: FFTW - Precision: double - X Y Z: 128 pts/heffte-1.0.0 c2c fftw double 256 256 256 Test: c2c - Backend: FFTW - Precision: double - X Y Z: 256 pts/heffte-1.0.0 r2c fftw double 128 128 128 Test: r2c - Backend: FFTW - Precision: double - X Y Z: 128 pts/heffte-1.0.0 r2c fftw double 256 256 256 Test: r2c - Backend: FFTW - Precision: double - X Y Z: 256 pts/libxsmm-1.0.1 128 128 128 M N K: 128 pts/libxsmm-1.0.1 256 256 256 M N K: 256 pts/libxsmm-1.0.1 32 32 32 M N K: 32 pts/libxsmm-1.0.1 64 64 64 M N K: 64 pts/oidn-2.0.0 -r RT.ldr_alb_nrm.3840x2160 -d cpu Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only pts/oidn-2.0.0 -r RTLightmap.hdr.4096x4096 -d cpu Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/onnx-1.6.0 GPT2/model.onnx -e cpu Model: GPT-2 - Device: CPU - Executor: Standard pts/onnx-1.6.0 yolov4/yolov4.onnx -e cpu Model: yolov4 - Device: CPU - Executor: Standard pts/onnx-1.6.0 bertsquad-12/bertsquad-12.onnx -e cpu Model: bertsquad-12 - Device: CPU - Executor: Standard pts/onnx-1.6.0 caffenet-12-int8/caffenet-12-int8.onnx -e cpu Model: CaffeNet 12-int8 - Device: CPU - Executor: Standard pts/onnx-1.6.0 fcn-resnet101-11/model.onnx -e cpu Model: fcn-resnet101-11 - Device: CPU - Executor: Standard pts/onnx-1.6.0 resnet100/resnet100.onnx -e cpu Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard pts/onnx-1.6.0 resnet50-v1-12-int8/resnet50-v1-12-int8.onnx -e cpu Model: ResNet50 v1-12-int8 - Device: CPU - Executor: Standard pts/onnx-1.6.0 super_resolution/super_resolution.onnx -e cpu Model: super-resolution-10 - Device: CPU - Executor: Standard pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC pts/ospray-2.12.0 --benchmark_filter=particle_volume/ao/real_time Benchmark: particle_volume/ao/real_time pts/ospray-2.12.0 --benchmark_filter=particle_volume/scivis/real_time Benchmark: particle_volume/scivis/real_time pts/ospray-2.12.0 --benchmark_filter=particle_volume/pathtracer/real_time Benchmark: particle_volume/pathtracer/real_time pts/ospray-2.12.0 --benchmark_filter=gravity_spheres_volume/dim_512/ao/real_time Benchmark: gravity_spheres_volume/dim_512/ao/real_time pts/ospray-2.12.0 --benchmark_filter=gravity_spheres_volume/dim_512/scivis/real_time Benchmark: gravity_spheres_volume/dim_512/scivis/real_time pts/deepsparse-1.5.2 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario async Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario async Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/cpuminer-opt-1.6.0 -a m7m Algorithm: Magi pts/cpuminer-opt-1.6.0 -a x25x Algorithm: x25x pts/cpuminer-opt-1.6.0 -a scrypt Algorithm: scrypt pts/cpuminer-opt-1.6.0 -a deep Algorithm: Deepcoin pts/cpuminer-opt-1.6.0 -a blake2s Algorithm: Blake-2 S pts/cpuminer-opt-1.6.0 -a allium Algorithm: Garlicoin pts/cpuminer-opt-1.6.0 -a skein Algorithm: Skeincoin pts/cpuminer-opt-1.6.0 -a myr-gr Algorithm: Myriad-Groestl pts/cpuminer-opt-1.6.0 -a lbry Algorithm: LBC, LBRY Credits pts/cpuminer-opt-1.6.0 -a sha256q Algorithm: Quad SHA-256, Pyrite pts/cpuminer-opt-1.6.0 -a sha256t Algorithm: Triple SHA-256, Onecoin pts/laghos-1.0.0 -p 3 -m data/box01_hex.mesh -rs 2 -tf 5.0 -vis -pa Test: Triple Point Problem pts/laghos-1.0.0 -p 1 -m data/cube_922_hex.mesh -rs 2 -tf 0.6 -no-vis -pa Test: Sedov Blast Wave, ube_922_hex.mesh pts/palabos-1.0.0 100 Grid Size: 100 pts/palabos-1.0.0 400 Grid Size: 400 pts/palabos-1.0.0 500 Grid Size: 500 pts/gromacs-1.8.0 mpi-build water-cut1.0_GMX50_bare/1536 Implementation: MPI CPU - Input: water_GMX50_bare pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/ncnn-1.5.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.5.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.5.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.5.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.5.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.5.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.5.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.5.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.5.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.5.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.5.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.5.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.5.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.5.0 -1 Target: CPU - Model: vision_transformer pts/ncnn-1.5.0 -1 Target: CPU - Model: FastestDet pts/cloverleaf-1.1.0 Lagrangian-Eulerian Hydrodynamics pts/incompact3d-2.0.2 input_193_nodes.i3d Input: input.i3d 193 Cells Per Direction pts/remhos-1.0.0 -m ./data/inline-quad.mesh -p 14 -rs 2 -rp 1 -dt 0.0005 -tf 0.6 -ho 1 -lo 2 -fct 3 Test: Sample Remap Example pts/specfem3d-1.0.0 Mount_StHelens Model: Mount St. Helens pts/specfem3d-1.0.0 layered_halfspace Model: Layered Halfspace pts/specfem3d-1.0.0 tomographic_model Model: Tomographic Model pts/specfem3d-1.0.0 homogeneous_halfspace Model: Homogeneous Halfspace pts/specfem3d-1.0.0 waterlayered_halfspace Model: Water-layered Halfspace pts/mrbayes-1.5.0 Primate Phylogeny Analysis pts/blender-3.6.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/blender-3.6.0 -b ../fishy_cat_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Fishy Cat - Compute: CPU-Only pts/qmcpack-1.6.0 tests/molecules/Li2_STO_ae Li2.STO.long.in.xml Input: Li2_STO_ae pts/qmcpack-1.6.0 build/examples/molecules/H2O/example_H2O-1-1 simple-H2O.xml Input: simple-H2O pts/qmcpack-1.6.0 tests/molecules/FeCO6_b3lyp_gms vmc_long_noj.in.xml Input: FeCO6_b3lyp_gms pts/qmcpack-1.6.0 tests/molecules/O_ae_pyscf_UHF vmc_long_noj.in.xml Input: FeCO6_b3lyp_gms