Xeon Gold Cascade Lake Refresh LTS Linux Benchmarks

Intel Xeon Gold 6226R testing with a Supermicro X11SPL-F v1.02 (3.1 BIOS) and ASPEED on Ubuntu 20.10 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 2212171-PTS-XEONGOLD08
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Audio Encoding 2 Tests
AV1 3 Tests
C++ Boost Tests 2 Tests
Timed Code Compilation 6 Tests
C/C++ Compiler Tests 9 Tests
Compression Tests 2 Tests
CPU Massive 17 Tests
Creator Workloads 17 Tests
Cryptocurrency Benchmarks, CPU Mining Tests 2 Tests
Cryptography 3 Tests
Database Test Suite 4 Tests
Encoding 5 Tests
Game Development 3 Tests
HPC - High Performance Computing 13 Tests
Imaging 5 Tests
Common Kernel Benchmarks 4 Tests
Machine Learning 8 Tests
Molecular Dynamics 2 Tests
Multi-Core 21 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 3 Tests
OpenMPI Tests 4 Tests
Programmer / Developer System Benchmarks 7 Tests
Python Tests 9 Tests
Renderers 3 Tests
Scientific Computing 2 Tests
Server 4 Tests
Server CPU Tests 10 Tests
Video Encoding 3 Tests
Common Workstation Benchmarks 2 Tests

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Linux 5.10.130
December 14 2022
  1 Day, 44 Minutes
Linux 5.15.83
December 15 2022
  1 Day, 1 Hour, 38 Minutes
Linux 6.1
December 16 2022
  1 Day, 2 Hours, 3 Minutes
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  1 Day, 1 Hour, 28 Minutes

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Xeon Gold Cascade Lake Refresh LTS Linux Benchmarks Suite 1.0.0 System Test suite extracted from Xeon Gold Cascade Lake Refresh LTS Linux Benchmarks. pts/stress-ng-1.6.0 --switch -1 Test: Context Switching pts/stress-ng-1.6.0 --msg -1 Test: System V Message Passing pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/stress-ng-1.6.0 --malloc -1 Test: Malloc pts/stress-ng-1.6.0 --numa -1 Test: NUMA pts/ctx-clock-1.0.0 Context Switch Time pts/pgbench-1.12.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only pts/pgbench-1.12.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency pts/stress-ng-1.6.0 --mmap -1 Test: MMAP pts/stress-ng-1.6.0 --mutex -1 Test: Mutex pts/stress-ng-1.6.0 --sendfile -1 Test: SENDFILE pts/pgbench-1.12.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only pts/pgbench-1.12.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency pts/jpegxl-decode-1.5.0 --num_reps=200 CPU Threads: All pts/pgbench-1.12.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write pts/graphics-magick-2.1.0 -rotate 90 Operation: Rotate pts/dragonflydb-1.0.0 -c 50 --ratio=1:5 Clients: 50 - Set To Get Ratio: 1:5 pts/pgbench-1.12.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only pts/stress-ng-1.6.0 --fork -1 Test: Forking pts/graphics-magick-2.1.0 -colorspace HWB Operation: HWB Color Space pts/stress-ng-1.6.0 --io-uring -1 Test: IO_uring pts/dragonflydb-1.0.0 -c 200 --ratio=1:5 Clients: 200 - Set To Get Ratio: 1:5 pts/dragonflydb-1.0.0 -c 200 --ratio=1:1 Clients: 200 - Set To Get Ratio: 1:1 pts/dragonflydb-1.0.0 -c 50 --ratio=5:1 Clients: 50 - Set To Get Ratio: 5:1 pts/encodec-1.0.1 -b 6 Target Bandwidth: 6 kbps pts/rocksdb-1.3.0 --benchmarks="fillsync" Test: Random Fill Sync pts/dragonflydb-1.0.0 -c 200 --ratio=5:1 Clients: 200 - Set To Get Ratio: 5:1 pts/encodec-1.0.1 -b 1.5 Target Bandwidth: 1.5 kbps pts/encodec-1.0.1 -b 24 Target Bandwidth: 24 kbps pts/encodec-1.0.1 -b 3 Target Bandwidth: 3 kbps pts/rocksdb-1.3.0 --benchmarks="fillseq" Test: Sequential Fill 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/aom-av1-3.5.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/pgbench-1.12.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write pts/pgbench-1.12.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency pts/stargate-1.1.0 192000 1024 Sample Rate: 192000 - Buffer Size: 1024 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 90 --num_reps 40 Input: PNG - Quality: 90 pts/rocksdb-1.3.0 --benchmarks="updaterandom" Test: Update Random pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 50 Input: JPEG - Quality: 80 pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40 Input: JPEG - Quality: 90 pts/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/rocksdb-1.3.0 --benchmarks="fillrandom" Test: Random Fill pts/blosc-1.2.0 blosclz shuffle Test: blosclz shuffle pts/jpegxl-decode-1.5.0 --num_threads=1 --num_reps=100 CPU Threads: 1 pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K pts/pgbench-1.12.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write pts/pgbench-1.12.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency pts/jpegxl-1.5.0 sample-4.png out.jxl -q 80 --num_reps 50 Input: PNG - Quality: 80 pts/spacy-1.0.0 Model: en_core_web_trf pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless pts/stargate-1.1.0 44100 512 Sample Rate: 44100 - Buffer Size: 512 pts/graphics-magick-2.1.0 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Second Run pts/ncnn-1.4.0 -1 Target: CPU - Model: blazeface pts/blosc-1.2.0 blosclz bitshuffle Test: blosclz bitshuffle pts/rocksdb-1.3.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, First Run / Cold Cache pts/build-linux-kernel-1.15.0 defconfig Build: defconfig 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/stargate-1.1.0 96000 512 Sample Rate: 96000 - Buffer Size: 512 pts/stress-ng-1.6.0 --sem -1 Test: Semaphores pts/stargate-1.1.0 192000 512 Sample Rate: 192000 - Buffer Size: 512 pts/mnn-2.1.0 Model: resnet-v2-50 pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/stargate-1.1.0 44100 1024 Sample Rate: 44100 - Buffer Size: 1024 pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet50 pts/mnn-2.1.0 Model: MobileNetV2_224 pts/mnn-2.1.0 Model: squeezenetv1.1 pts/numenta-nab-1.1.1 -d knncad Detector: KNN CAD pts/graphics-magick-2.1.0 -resize 50% Operation: Resizing 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/build-erlang-1.2.0 Time To Compile 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/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/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/ncnn-1.4.0 -1 Target: CPU - Model: yolov4-tiny pts/graphics-magick-2.1.0 -swirl 90 Operation: Swirl pts/build-godot-1.0.0 Time To Compile pts/ncnn-1.4.0 -1 Target: CPU - Model: squeezenet_ssd pts/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 27 -t 4 -q Test: 4G PHY_DL_Test 100 PRB MIMO 256-QAM pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/stargate-1.1.0 96000 1024 Sample Rate: 96000 - Buffer Size: 1024 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/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/openvino-1.1.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 10 Input: JPEG - Quality: 100 pts/stargate-1.1.0 48000 512 Sample Rate: 480000 - Buffer Size: 512 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 100 --num_reps 10 Input: PNG - Quality: 100 pts/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/ncnn-1.4.0 -1 Target: CPU - Model: mobilenet pts/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/stargate-1.1.0 48000 1024 Sample Rate: 480000 - Buffer Size: 1024 pts/build-python-1.0.0 Build Configuration: Default pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 pts/mnn-2.1.0 Model: nasnet pts/compress-7zip-1.10.0 Test: Compression Rating pts/ncnn-1.4.0 -1 Target: CPU - Model: FastestDet pts/mnn-2.1.0 Model: SqueezeNetV1.0 pts/srsran-1.2.0 lib/src/phy/dft/test/ofdm_test -N 2048 -n 100 -r 500000 Test: OFDM_Test pts/mnn-2.1.0 Model: inception-v3 pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer 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/mnn-2.1.0 Model: mobilenet-v1-1.0 pts/cpuminer-opt-1.6.0 -a myr-gr Algorithm: Myriad-Groestl pts/build-nodejs-1.2.0 Time To Compile 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/encode-flac-1.8.1 WAV To FLAC pts/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/graphics-magick-2.1.0 -enhance Operation: Enhanced 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/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/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/build-php-1.6.0 Time To Compile pts/ncnn-1.4.0 -1 Target: CPU - Model: shufflenet-v2 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 - Model: mnasnet pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Third Run pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/cpuminer-opt-1.6.0 -a minotaur Algorithm: Ringcoin pts/cpuminer-opt-1.6.0 -a blake2s Algorithm: Blake-2 S pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 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/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/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 28 -t 4 Test: 4G PHY_DL_Test 100 PRB MIMO 64-QAM pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K pts/graphics-magick-2.1.0 -sharpen 0x2.0 Operation: Sharpen pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/ncnn-1.4.0 -1 Target: CPU - Model: efficientnet-b0 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/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 27 -t 1 -q Test: 4G PHY_DL_Test 100 PRB SISO 256-QAM 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/avifenc-1.3.0 -s 2 Encoder Speed: 2 pts/mnn-2.1.0 Model: mobilenetV3 pts/ncnn-1.4.0 -1 Target: CPU - Model: googlenet pts/svt-av1-2.7.0 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M 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/brl-cad-1.3.0 VGR Performance Metric pts/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline 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/svt-av1-2.7.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/svt-av1-2.7.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/ncnn-1.4.0 -1 Target: CPU - Model: regnety_400m pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/cpuminer-opt-1.6.0 -a lbry Algorithm: LBC, LBRY Credits pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time 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/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/webp-1.2.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/srsran-1.2.0 lib/test/phy/phy_dl_test -p 100 -s 20000 -m 28 -t 1 Test: 4G PHY_DL_Test 100 PRB SISO 64-QAM pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time pts/webp-1.2.0 -q 100 Encode Settings: Quality 100 pts/spacy-1.0.0 Model: en_core_web_lg pts/cpuminer-opt-1.6.0 -a sha256q Algorithm: Quad SHA-256, Pyrite 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/ncnn-1.4.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/webp-1.2.0 -q 100 -m 6 Encode Settings: Quality 100, Highest Compression pts/y-cruncher-1.2.0 500m Pi Digits To Calculate: 500M pts/cpuminer-opt-1.6.0 -a sha256t Algorithm: Triple SHA-256, Onecoin 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/stress-ng-1.6.0 --cpu -1 --cpu-method all Test: CPU Stress pts/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic 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/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian pts/ncnn-1.4.0 -1 Target: CPU - Model: vision_transformer 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/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time pts/cpuminer-opt-1.6.0 -a skein Algorithm: Skeincoin pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/cpuminer-opt-1.6.0 -a deep Algorithm: Deepcoin pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/build-python-1.0.0 --enable-optimizations --with-lto Build Configuration: Released Build, PGO + LTO Optimized pts/blender-3.4.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only 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/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/lammps-1.4.0 in.rhodo Model: Rhodopsin Protein pts/svt-av1-2.7.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/cpuminer-opt-1.6.0 -a x25x Algorithm: x25x pts/y-cruncher-1.2.0 1b Pi Digits To Calculate: 1B 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/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream 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 pts/cpuminer-opt-1.6.0 -a scrypt Algorithm: scrypt pts/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/cpuminer-opt-1.6.0 -a m7m Algorithm: Magi pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 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/rocksdb-1.3.0 --benchmarks="readrandom" Test: Random Read 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/ncnn-1.4.0 -1 Target: CPU - Model: alexnet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/compress-7zip-1.10.0 Test: Decompression Rating pts/cpuminer-opt-1.6.0 -a allium Algorithm: Garlicoin 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/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/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/blender-3.4.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/srsran-1.2.0 lib/test/phy/phy_dl_nr_test -P 52 -p 52 -m 28 -n 20000 Test: 5G PHY_DL_NR Test 52 PRB SISO 64-QAM 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/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/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/tensorflow-2.0.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/stress-ng-1.6.0 --qsort -1 Test: Glibc Qsort Data Sorting pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE pts/lammps-1.4.0 benchmark_20k_atoms.in Model: 20k Atoms 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/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/avifenc-1.3.0 -s 0 Encoder Speed: 0 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/tensorflow-2.0.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/webp-1.2.0 Encode Settings: Default pts/ospray-studio-1.1.0 --cameras 2 2 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/rocksdb-1.3.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/primesieve-1.9.0 1e13 Length: 1e13 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/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/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough 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/blender-3.4.0 -b ../pavillon_barcelone_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Pabellon Barcelona - Compute: CPU-Only 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/ospray-studio-1.1.0 --cameras 2 2 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/primesieve-1.9.0 1e12 Length: 1e12 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/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/stress-ng-1.6.0 --vecmath -1 Test: Vector Math 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/ospray-studio-1.1.0 --cameras 2 2 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test 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/blender-3.4.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/astcenc-1.4.0 -medium -repeats 20 Preset: Medium 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/stress-ng-1.6.0 --crypt -1 Test: Crypto pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast pts/stress-ng-1.6.0 --matrix -1 Test: Matrix Math 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/blender-3.4.0 -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/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/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/ospray-studio-1.1.0 --cameras 2 2 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer pts/stress-ng-1.6.0 --rdrand -1 Test: x86_64 RdRand pts/aircrack-ng-1.3.0 pts/natron-1.1.0 Natron_2.3.12_Spaceship/Natron_project/Spaceship_Natron.ntp Input: Spaceship pts/ncnn-1.4.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/webp-1.2.0 -q 100 -lossless -m 6 Encode Settings: Quality 100, Lossless, Highest Compression pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.4.0 -1 Target: CPU - Model: vgg16 pts/stress-ng-1.6.0 --sock -1 Test: Socket Activity pts/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/stress-ng-1.6.0 --futex -1 Test: Futex pts/dragonflydb-1.0.0 -c 50 --ratio=1:1 Clients: 50 - Set To Get Ratio: 1:1