eptc-7f32

AMD EPYC 7F32 8-Core testing with a ASRockRack EPYCD8 (P2.40 BIOS) and ASPEED on Debian 11 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 2211207-NE-EPTC7F32776
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AV1 2 Tests
C++ Boost Tests 2 Tests
Timed Code Compilation 4 Tests
C/C++ Compiler Tests 7 Tests
Compression Tests 2 Tests
CPU Massive 12 Tests
Creator Workloads 14 Tests
Cryptocurrency Benchmarks, CPU Mining Tests 2 Tests
Cryptography 3 Tests
Encoding 4 Tests
HPC - High Performance Computing 11 Tests
Imaging 6 Tests
Common Kernel Benchmarks 2 Tests
Machine Learning 7 Tests
Multi-Core 14 Tests
Intel oneAPI 2 Tests
OpenMPI Tests 3 Tests
Programmer / Developer System Benchmarks 5 Tests
Python Tests 8 Tests
Renderers 2 Tests
Server 2 Tests
Server CPU Tests 7 Tests
Single-Threaded 2 Tests
Video Encoding 3 Tests
Common Workstation Benchmarks 2 Tests

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EPYC 7F32
November 20 2022
  6 Hours, 7 Minutes
AMD EPYC 7F32
November 20 2022
  6 Hours, 34 Minutes
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eptc-7f32 Suite 1.0.0 System Test suite extracted from eptc-7f32 . pts/jpegxl-decode-1.5.0 --num_reps=200 CPU Threads: All 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 --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 S Input: drivaerFastback, Small Mesh Size - Execution Time pts/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/y-cruncher-1.2.0 500m Pi Digits To Calculate: 500M 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/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p pts/blosc-1.2.0 blosclz bitshuffle Test: blosclz bitshuffle pts/cpuminer-opt-1.6.0 -a x25x Algorithm: x25x pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless 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 sample-4.png out.jxl -q 90 --num_reps 40 Input: PNG - Quality: 90 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 80 --num_reps 50 Input: PNG - Quality: 80 pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/aom-av1-3.5.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=9 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p pts/cpuminer-opt-1.6.0 -a minotaur Algorithm: Ringcoin pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40 Input: JPEG - Quality: 90 pts/compress-7zip-1.10.0 Test: Compression Rating pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p pts/cpuminer-opt-1.6.0 -a m7m Algorithm: Magi pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K pts/y-cruncher-1.2.0 1b Pi Digits To Calculate: 1B pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time pts/aom-av1-3.5.0 --cpu-used=4 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=4 Bosphorus_3840x2160.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K pts/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/graphics-magick-2.1.0 -resize 50% Operation: Resizing pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container 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=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K 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/build-erlang-1.2.0 Time To Compile pts/ffmpeg-3.0.0 --encoder=libx264 live Encoder: libx264 - Scenario: Live pts/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless pts/webp2-1.2.0 Encode Settings: Default pts/graphics-magick-2.1.0 -colorspace HWB Operation: HWB Color Space 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/jpegxl-decode-1.5.0 --num_threads=1 --num_reps=100 CPU Threads: 1 pts/graphics-magick-2.1.0 -sharpen 0x2.0 Operation: Sharpen pts/cpuminer-opt-1.6.0 -a allium Algorithm: Garlicoin pts/build-python-1.0.0 Build Configuration: Default pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p pts/cpuminer-opt-1.6.0 -a lbry Algorithm: LBC, LBRY Credits 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/ffmpeg-3.0.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/graphics-magick-2.1.0 -enhance Operation: Enhanced pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 pts/ffmpeg-3.0.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/smhasher-1.1.0 --test=Speed FarmHash32 Hash: FarmHash32 x86_64 AVX pts/srsran-1.2.0 lib/src/phy/dft/test/ofdm_test -N 2048 -n 100 -r 500000 Test: OFDM_Test pts/ffmpeg-3.0.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 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/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 pts/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation 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/build-python-1.0.0 --enable-optimizations --with-lto Build Configuration: Released Build, PGO + LTO Optimized pts/smhasher-1.1.0 --test=Speed t1ha0_aes_avx2 Hash: t1ha0_aes_avx2 x86_64 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/ffmpeg-3.0.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/encode-flac-1.8.1 WAV To FLAC pts/smhasher-1.1.0 --test=Speed MeowHash Hash: MeowHash x86_64 AES-NI 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/webp-1.2.0 -q 100 Encode Settings: Quality 100 pts/build-php-1.6.0 Time To Compile pts/cpuminer-opt-1.6.0 -a skein Algorithm: Skeincoin pts/unpack-linux-1.2.0 linux-5.19.tar.xz pts/graphics-magick-2.1.0 -swirl 90 Operation: Swirl pts/ffmpeg-3.0.0 --encoder=libx264 platform Encoder: libx264 - Scenario: Platform pts/ffmpeg-3.0.0 --encoder=libx264 upload Encoder: libx264 - Scenario: Upload pts/cpuminer-opt-1.6.0 -a blake2s Algorithm: Blake-2 S 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/webp-1.2.0 Encode Settings: Default pts/build-nodejs-1.2.0 Time To Compile pts/cpuminer-opt-1.6.0 -a scrypt Algorithm: scrypt pts/avifenc-1.3.0 -s 0 Encoder Speed: 0 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/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/avifenc-1.3.0 -s 2 Encoder Speed: 2 pts/cpuminer-opt-1.6.0 -a deep Algorithm: Deepcoin pts/smhasher-1.1.0 --test=Speed t1ha2_atonce Hash: t1ha2_atonce pts/compress-7zip-1.10.0 Test: Decompression Rating pts/ffmpeg-3.0.0 --encoder=libx264 vod Encoder: libx264 - Scenario: Video On Demand pts/blosc-1.2.0 blosclz shuffle Test: blosclz shuffle pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/cpuminer-opt-1.6.0 -a sha256t Algorithm: Triple SHA-256, Onecoin pts/smhasher-1.1.0 --test=Speed sha3-256 Hash: SHA3-256 pts/smhasher-1.1.0 --test=Speed wyhash Hash: wyhash pts/smhasher-1.1.0 --test=Speed Spooky32 Hash: Spooky32 pts/smhasher-1.1.0 --test=Speed FarmHash128 Hash: FarmHash128 pts/smhasher-1.1.0 --test=Speed fasthash32 Hash: fasthash32 pts/brl-cad-1.3.0 VGR Performance Metric pts/scikit-learn-1.2.0 random_projections.py --n-times 100 Benchmark: Sparse Random Projections, 100 Iterations pts/scikit-learn-1.2.0 tsne_mnist.py Benchmark: TSNE MNIST Dataset pts/scikit-learn-1.2.0 mnist.py Benchmark: MNIST Dataset 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 6 Target Bandwidth: 6 kbps pts/encodec-1.0.1 -b 3 Target Bandwidth: 3 kbps pts/natron-1.1.0 Natron_2.3.12_Spaceship/Natron_project/Spaceship_Natron.ntp Input: Spaceship pts/nginx-3.0.0 -c 1000 Connections: 1000 pts/nginx-3.0.0 -c 500 Connections: 500 pts/nginx-3.0.0 -c 200 Connections: 200 pts/nginx-3.0.0 -c 100 Connections: 100 pts/nginx-3.0.0 -c 20 Connections: 20 pts/rocksdb-1.3.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/rocksdb-1.3.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.3.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.3.0 --benchmarks="readrandom" Test: Random Read 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/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-1.1.0 -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/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/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/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/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/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/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: 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/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - 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/blender-3.3.1 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/mnn-2.1.0 Model: inception-v3 pts/mnn-2.1.0 Model: mobilenet-v1-1.0 pts/mnn-2.1.0 Model: MobileNetV2_224 pts/mnn-2.1.0 Model: SqueezeNetV1.0 pts/mnn-2.1.0 Model: resnet-v2-50 pts/mnn-2.1.0 Model: squeezenetv1.1 pts/mnn-2.1.0 Model: mobilenetV3 pts/mnn-2.1.0 Model: nasnet pts/spacy-1.0.0 Model: en_core_web_trf pts/spacy-1.0.0 Model: en_core_web_lg pts/stress-ng-1.6.0 --msg -1 Test: System V Message Passing pts/stress-ng-1.6.0 --qsort -1 Test: Glibc Qsort Data Sorting pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/stress-ng-1.6.0 --switch -1 Test: Context Switching pts/stress-ng-1.6.0 --sock -1 Test: Socket Activity pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/stress-ng-1.6.0 --vecmath -1 Test: Vector Math pts/stress-ng-1.6.0 --matrix -1 Test: Matrix Math pts/stress-ng-1.6.0 --sem -1 Test: Semaphores pts/stress-ng-1.6.0 --cpu -1 --cpu-method all Test: CPU Stress pts/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/stress-ng-1.6.0 --sendfile -1 Test: SENDFILE pts/stress-ng-1.6.0 --io-uring -1 Test: IO_uring pts/stress-ng-1.6.0 --fork -1 Test: Forking pts/stress-ng-1.6.0 --malloc -1 Test: Malloc pts/stress-ng-1.6.0 --crypt -1 Test: Crypto pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/stress-ng-1.6.0 --mutex -1 Test: Mutex pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/stress-ng-1.6.0 --futex -1 Test: Futex pts/stress-ng-1.6.0 --numa -1 Test: NUMA pts/stress-ng-1.6.0 --mmap -1 Test: MMAP 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/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream 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/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/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream 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/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/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:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream 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/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: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/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/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=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet 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=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - 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=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/tensorflow-2.0.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet 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=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/cpuminer-opt-1.6.0 -a sha256q Algorithm: Quad SHA-256, Pyrite pts/cpuminer-opt-1.6.0 -a myr-gr Algorithm: Myriad-Groestl 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/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/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/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/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/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/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/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/onednn-2.7.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU 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/aom-av1-3.5.0 --cpu-used=0 --limit=20 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p pts/aom-av1-3.5.0 --cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K pts/graphics-magick-2.1.0 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian pts/graphics-magick-2.1.0 -rotate 90 Operation: Rotate pts/webp2-1.2.0 -q 100 -effort 9 Encode Settings: Quality 100, Lossless Compression pts/webp2-1.2.0 -q 100 -effort 5 Encode Settings: Quality 100, Compression Effort 5 pts/webp2-1.2.0 -q 95 -effort 7 Encode Settings: Quality 95, Compression Effort 7 pts/webp2-1.2.0 -q 75 -effort 7 Encode Settings: Quality 75, Compression Effort 7 pts/webp-1.2.0 -q 100 -lossless -m 6 Encode Settings: Quality 100, Lossless, Highest Compression pts/webp-1.2.0 -q 100 -m 6 Encode Settings: Quality 100, Highest Compression pts/webp-1.2.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 10 Input: JPEG - Quality: 100 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 100 --num_reps 10 Input: PNG - Quality: 100 pts/nginx-3.0.0 -c 4000 Connections: 4000 pts/nginx-3.0.0 -c 1 Connections: 1 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/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/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/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_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - 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/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic