AVX-512 Core i9 Intel Rocket Lake

Benchmarks for a future article. Intel Core i9-11900K testing with a ASUS ROG MAXIMUS XIII HERO (1402 BIOS) and ASUS Intel RKL GT1 31GB 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 2210210-NE-AVX512ROC09
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i9-11900K: AVX-512 On
October 18 2022
  1 Day, 1 Hour, 48 Minutes
i9-11900K: AVX-512 Off
October 19 2022
  1 Day, 2 Hours, 28 Minutes
i9-11900K: AVX-512 On 512
October 20 2022
  1 Day, 1 Hour, 45 Minutes
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AVX-512 Core i9 Intel Rocket Lake Suite 1.0.0 System Test suite extracted from AVX-512 Core i9 Intel Rocket Lake. pts/cpuminer-opt-1.5.1 -a lbry Algorithm: LBC, LBRY Credits pts/cpuminer-opt-1.5.1 -a sha256q Algorithm: Quad SHA-256, Pyrite pts/cpuminer-opt-1.5.1 -a myr-gr Algorithm: Myriad-Groestl pts/openvino-1.1.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.1.0 -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/cpuminer-opt-1.5.1 -a blake2s Algorithm: Blake-2 S pts/cpuminer-opt-1.5.1 -a sha256t Algorithm: Triple SHA-256, Onecoin pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/openvino-1.1.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/cpuminer-opt-1.5.1 -a allium Algorithm: Garlicoin pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/mnn-2.1.0 Model: resnet-v2-50 pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/deepsparse-1.0.1 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario sync Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/ai-benchmark-1.0.2 Device Training Score pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/ospray-2.10.0 --benchmark_filter=gravity_spheres_volume/dim_512/scivis/real_time Benchmark: gravity_spheres_volume/dim_512/scivis/real_time pts/cpuminer-opt-1.5.1 -a skein Algorithm: Skeincoin pts/ospray-2.10.0 --benchmark_filter=gravity_spheres_volume/dim_512/ao/real_time Benchmark: gravity_spheres_volume/dim_512/ao/real_time pts/openvino-1.1.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/ncnn-1.4.0 -1 Target: CPU - Model: vision_transformer pts/deepsparse-1.0.1 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=vgg16 Device: CPU - Batch Size: 256 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=vgg16 Device: CPU - Batch Size: 64 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=vgg16 Device: CPU - Batch Size: 32 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/deepsparse-1.0.1 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/simdjson-2.0.1 top_tweet Throughput Test: TopTweet pts/simdjson-2.0.1 distinct_user_id Throughput Test: DistinctUserID pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/simdjson-2.0.1 partial_tweets Throughput Test: PartialTweets pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/ospray-2.10.0 --benchmark_filter=gravity_spheres_volume/dim_512/pathtracer/real_time Benchmark: gravity_spheres_volume/dim_512/pathtracer/real_time pts/openvino-1.1.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvkl-1.1.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/ai-benchmark-1.0.2 Device AI Score pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/ncnn-1.4.0 -1 Target: CPU - Model: squeezenet_ssd pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-2.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/deepsparse-1.0.1 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario sync Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream pts/ospray-2.10.0 --benchmark_filter=particle_volume/ao/real_time Benchmark: particle_volume/ao/real_time pts/deepsparse-1.0.1 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/mnn-2.1.0 Model: squeezenetv1.1 pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/mnn-2.1.0 Model: inception-v3 pts/mnn-2.1.0 Model: SqueezeNetV1.0 pts/ospray-2.10.0 --benchmark_filter=particle_volume/scivis/real_time Benchmark: particle_volume/scivis/real_time pts/openvino-1.1.0 -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/deepsparse-1.0.1 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/simdjson-2.0.1 kostya Throughput Test: Kostya pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/deepsparse-1.0.1 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/ncnn-1.4.0 -1 Target: CPU - Model: yolov4-tiny pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/openvino-1.1.0 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/deepsparse-1.0.1 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection,YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/openvino-1.1.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.1.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/onednn-2.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/openvino-1.1.0 -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/ncnn-1.4.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/lczero-1.6.0 -b eigen Backend: Eigen pts/openvino-1.1.0 -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/deepsparse-1.0.1 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/ncnn-1.4.0 -1 Target: CPU - Model: efficientnet-b0 pts/deepsparse-1.0.1 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/mnn-2.1.0 Model: mobilenet-v1-1.0 pts/ncnn-1.4.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.4.0 -1 Target: CPU - Model: alexnet pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer pts/mnn-2.1.0 Model: MobileNetV2_224 pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/numpy-1.2.1 pts/onnx-1.5.0 super_resolution/super_resolution.onnx -e cpu -P Model: super-resolution-10 - Device: CPU - Executor: Parallel pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/onednn-2.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/ncnn-1.4.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/onnx-1.5.0 resnet100/resnet100.onnx -e cpu -P Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel pts/dav1d-1.12.0 -i summer_nature_4k.ivf Video Input: Summer Nature 4K pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer pts/simdjson-2.0.1 large_random Throughput Test: LargeRandom pts/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/onnx-1.5.0 super_resolution/super_resolution.onnx -e cpu Model: super-resolution-10 - Device: CPU - Executor: Standard pts/ncnn-1.4.0 -1 Target: CPU - Model: mobilenet pts/onednn-2.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/dav1d-1.12.0 -i chimera_10b_1080p.ivf Video Input: Chimera 1080p 10-bit pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/ncnn-1.4.0 -1 Target: CPU - Model: googlenet pts/dav1d-1.12.0 -i chimera_8b_1080p.ivf Video Input: Chimera 1080p pts/ncnn-1.4.0 -1 Target: CPU - Model: regnety_400m pts/embree-1.2.1 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet50 pts/mnn-2.1.0 Model: nasnet pts/ospray-2.10.0 --benchmark_filter=particle_volume/pathtracer/real_time Benchmark: particle_volume/pathtracer/real_time pts/ncnn-1.4.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.4.0 -1 Target: CPU - Model: FastestDet pts/onednn-2.7.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/embree-1.2.1 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/ai-benchmark-1.0.2 Device Inference Score pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onnx-1.5.0 bertsquad-12/bertsquad-12.onnx -e cpu Model: bertsquad-12 - Device: CPU - Executor: Standard pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/shufflenet_v2.tnnproto Target: CPU - Model: SqueezeNet v2 pts/onnx-1.5.0 fcn-resnet101-11/model.onnx -e cpu Model: fcn-resnet101-11 - Device: CPU - Executor: Standard pts/embree-1.2.1 pathtracer_ispc -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon Obj pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/dav1d-1.12.0 -i summer_nature_1080p.ivf Video Input: Summer Nature 1080p pts/mnn-2.1.0 Model: mobilenetV3 pts/lczero-1.6.0 -b blas Backend: BLAS pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time pts/ncnn-1.4.0 -1 Target: CPU - Model: vgg16 pts/onnx-1.5.0 yolov4/yolov4.onnx -e cpu Model: yolov4 - Device: CPU - Executor: Standard pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/mobilenet_v2.tnnproto Target: CPU - Model: MobileNet v2 pts/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onnx-1.5.0 bertsquad-12/bertsquad-12.onnx -e cpu -P Model: bertsquad-12 - Device: CPU - Executor: Parallel pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/xmrig-1.0.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/gromacs-1.7.0 mpi-build water-cut1.0_GMX50_bare/1536 Implementation: MPI CPU - Input: water_GMX50_bare pts/onnx-1.5.0 fcn-resnet101-11/model.onnx -e cpu -P Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel pts/onnx-1.5.0 yolov4/yolov4.onnx -e cpu -P Model: yolov4 - Device: CPU - Executor: Parallel pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/squeezenet_v1.1.tnnproto Target: CPU - Model: SqueezeNet v1.1 pts/cpuminer-opt-1.5.1 -a m7m Algorithm: Magi pts/xmrig-1.0.0 --bench=1M Variant: Monero - Hash Count: 1M pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/tnn-1.1.0 -dt NAIVE -mp ../benchmark/benchmark-model/densenet.tnnproto Target: CPU - Model: DenseNet pts/onednn-2.7.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onnx-1.5.0 GPT2/model.onnx -e cpu -P Model: GPT-2 - Device: CPU - Executor: Parallel pts/onnx-1.5.0 GPT2/model.onnx -e cpu Model: GPT-2 - Device: CPU - Executor: Standard pts/onednn-2.7.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/ncnn-1.4.0 -1 Target: CPU - Model: shufflenet-v2 pts/oidn-1.4.0 -r RTLightmap.hdr.4096x4096 Run: RTLightmap.hdr.4096x4096 pts/oidn-1.4.0 -r RT.ldr_alb_nrm.3840x2160 Run: RT.ldr_alb_nrm.3840x2160 pts/oidn-1.4.0 -r RT.hdr_alb_nrm.3840x2160 Run: RT.hdr_alb_nrm.3840x2160 pts/onnx-1.5.0 resnet100/resnet100.onnx -e cpu Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard pts/openvino-1.1.0 -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/tensorflow-2.0.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=vgg16 Device: CPU - Batch Size: 512 - Model: VGG-16 pts/cpuminer-opt-1.5.1 -a deep Algorithm: Deepcoin pts/cpuminer-opt-1.5.1 -a minotaur Algorithm: Ringcoin pts/cpuminer-opt-1.5.1 -a x25x Algorithm: x25x pts/onednn-2.7.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU