5800X3D 2023

AMD Ryzen 7 5800X3D 8-Core testing with a ASUS ROG CROSSHAIR VIII HERO (4201 BIOS) and AMD Radeon RX 6700 XT 12GB on Ubuntu 22.04 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 2302220-NE-5800X3D2009
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AV1 3 Tests
C/C++ Compiler Tests 8 Tests
CPU Massive 11 Tests
Creator Workloads 13 Tests
Database Test Suite 3 Tests
Encoding 8 Tests
Game Development 2 Tests
HPC - High Performance Computing 6 Tests
Common Kernel Benchmarks 2 Tests
Machine Learning 3 Tests
Multi-Core 14 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 3 Tests
OpenMPI Tests 2 Tests
Programmer / Developer System Benchmarks 2 Tests
Python Tests 3 Tests
Server 4 Tests
Server CPU Tests 8 Tests
Video Encoding 7 Tests
Common Workstation Benchmarks 2 Tests

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February 22 2023
  3 Hours, 31 Minutes
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February 22 2023
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5800X3D 2023 Suite 1.0.0 System Test suite extracted from 5800X3D 2023. pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic pts/brl-cad-1.4.0 VGR Performance Metric pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=scalar Benchmark: vklBenchmark Scalar pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC 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/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, Second Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, First Run / Cold Cache pts/vvenc-1.0.0 -i Bosphorus_3840x2160.y4m --preset fast Video Input: Bosphorus 4K - Video Preset: Fast pts/numenta-nab-1.1.1 -d knncad Detector: KNN CAD pts/gromacs-1.8.0 mpi-build water-cut1.0_GMX50_bare/1536 Implementation: MPI CPU - Input: water_GMX50_bare 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/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 128 Workload: KV, 50% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 128 Workload: KV, 60% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 128 Workload: KV, 95% Reads - Concurrency: 128 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 128 Workload: KV, 10% Reads - Concurrency: 128 pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/aom-av1-3.6.0 --cpu-used=4 Bosphorus_3840x2160.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K pts/stargate-1.1.0 192000 512 Sample Rate: 192000 - Buffer Size: 512 pts/cockroach-1.0.2 movr --concurrency 128 Workload: MoVR - Concurrency: 128 pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/stargate-1.1.0 192000 1024 Sample Rate: 192000 - Buffer Size: 1024 pts/aom-av1-3.6.0 --cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K pts/nginx-3.0.0 -c 500 Connections: 500 pts/nginx-3.0.0 -c 200 Connections: 200 pts/nginx-3.0.0 -c 1000 Connections: 1000 pts/nginx-3.0.0 -c 100 Connections: 100 pts/nginx-3.0.0 -c 20 Connections: 20 pts/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/vvenc-1.0.0 -i Bosphorus_3840x2160.y4m --preset faster Video Input: Bosphorus 4K - Video Preset: Faster pts/vpxenc-3.2.0 --cpu-used=0 ~/Bosphorus_3840x2160.y4m --width=3840 --height=2160 Speed: Speed 0 - Input: Bosphorus 4K pts/compress-zstd-1.6.0 -b19 --long Compression Level: 19, Long Mode - Decompression Speed pts/compress-zstd-1.6.0 -b19 --long Compression Level: 19, Long Mode - Compression Speed pts/stargate-1.1.0 96000 512 Sample Rate: 96000 - Buffer Size: 512 pts/compress-zstd-1.6.0 -b19 Compression Level: 19 - Decompression Speed pts/compress-zstd-1.6.0 -b19 Compression Level: 19 - Compression Speed pts/vvenc-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset fast Video Input: Bosphorus 1080p - Video Preset: Fast pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/stargate-1.1.0 96000 1024 Sample Rate: 96000 - Buffer Size: 1024 pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/compress-zstd-1.6.0 -b3 Compression Level: 3 - Decompression Speed pts/compress-zstd-1.6.0 -b3 Compression Level: 3 - Compression Speed pts/compress-zstd-1.6.0 -b12 Compression Level: 12 - Decompression Speed pts/compress-zstd-1.6.0 -b12 Compression Level: 12 - Compression Speed pts/compress-zstd-1.6.0 -b8 --long Compression Level: 8, Long Mode - Decompression Speed pts/compress-zstd-1.6.0 -b8 --long Compression Level: 8, Long Mode - Compression Speed pts/compress-zstd-1.6.0 -b3 --long Compression Level: 3, Long Mode - Decompression Speed pts/compress-zstd-1.6.0 -b3 --long Compression Level: 3, Long Mode - Compression Speed pts/compress-zstd-1.6.0 -b8 Compression Level: 8 - Decompression Speed pts/compress-zstd-1.6.0 -b8 Compression Level: 8 - Compression Speed pts/rocksdb-1.4.0 --benchmarks="fillsync" Test: Random Fill Sync pts/rocksdb-1.4.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.4.0 --benchmarks="fillrandom" Test: Random Fill pts/rocksdb-1.4.0 --benchmarks="readrandom" Test: Random Read pts/rocksdb-1.4.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/rocksdb-1.4.0 --benchmarks="readwhilewriting" Test: Read While Writing 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/aom-av1-3.6.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K pts/deepsparse-1.3.2 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-1.3.2 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-1.3.2 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/stargate-1.1.0 48000 512 Sample Rate: 480000 - Buffer Size: 512 pts/stargate-1.1.0 44100 512 Sample Rate: 44100 - Buffer Size: 512 pts/deepsparse-1.3.2 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/stargate-1.1.0 48000 1024 Sample Rate: 480000 - Buffer Size: 1024 pts/deepsparse-1.3.2 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/embree-1.3.0 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/stargate-1.1.0 44100 1024 Sample Rate: 44100 - Buffer Size: 1024 pts/deepsparse-1.3.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/embree-1.3.0 pathtracer -c crown/crown.ecs Binary: Pathtracer - Model: Crown pts/deepsparse-1.3.2 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-1.3.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario sync Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 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-1.3.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario sync Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 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-1.3.2 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/aom-av1-3.6.0 --cpu-used=4 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 1080p pts/embree-1.3.0 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/deepsparse-1.3.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/embree-1.3.0 pathtracer -c asian_dragon/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon pts/deepsparse-1.3.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/vpxenc-3.2.0 --cpu-used=0 ~/Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv --width=1920 --height=1080 Speed: Speed 0 - Input: Bosphorus 1080p pts/deepsparse-1.3.2 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-1.3.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE pts/deepsparse-1.3.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/vpxenc-3.2.0 --cpu-used=5 ~/Bosphorus_3840x2160.y4m --width=3840 --height=2160 Speed: Speed 5 - Input: Bosphorus 4K pts/aom-av1-3.6.0 --cpu-used=0 --limit=20 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 1080p pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/stress-ng-1.6.0 --switch -1 Test: Context Switching pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/stress-ng-1.6.0 --sem -1 Test: Semaphores pts/stress-ng-1.6.0 --numa -1 Test: NUMA pts/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/stress-ng-1.6.0 --malloc -1 Test: Malloc pts/stress-ng-1.6.0 --mmap -1 Test: MMAP pts/stress-ng-1.6.0 --futex -1 Test: Futex 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 --sock -1 Test: Socket Activity 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 --cpu -1 --cpu-method all Test: CPU Stress 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 --crypt -1 Test: Crypto pts/stress-ng-1.6.0 --mutex -1 Test: Mutex pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/vvenc-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset faster Video Input: Bosphorus 1080p - Video Preset: Faster pts/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow 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/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/aom-av1-3.6.0 --cpu-used=6 Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 1080p pts/vpxenc-3.2.0 --cpu-used=5 ~/Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv --width=1920 --height=1080 Speed: Speed 5 - Input: Bosphorus 1080p pts/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/dav1d-1.13.0 -i summer_nature_4k.ivf Video Input: Summer Nature 4K pts/dav1d-1.13.0 -i chimera_10b_1080p.ivf Video Input: Chimera 1080p 10-bit pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=u8s8f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/dav1d-1.13.0 -i chimera_8b_1080p.ivf Video Input: Chimera 1080p pts/rocksdb-1.4.0 --benchmarks="fillseq" Test: Sequential Fill pts/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/aom-av1-3.6.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K pts/aom-av1-3.6.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/aom-av1-3.6.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K pts/aom-av1-3.6.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast 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/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast pts/dav1d-1.13.0 -i summer_nature_1080p.ivf Video Input: Summer Nature 1080p pts/aom-av1-3.6.0 --cpu-used=8 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 1080p pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/aom-av1-3.6.0 --cpu-used=10 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 1080p pts/aom-av1-3.6.0 --cpu-used=6 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 1080p pts/aom-av1-3.6.0 --cpu-used=9 --rt Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 1080p 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/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/nginx-3.0.0 -c 1 Connections: 1 pts/onednn-3.0.0 --matmul --batch=inputs/matmul/shapes_transformer --cfg=bf16bf16bf16 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/stress-ng-1.6.0 --rdrand -1 Test: x86_64 RdRand pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU