cascade lake refresh

Tests for a future article. 2 x Intel Xeon Gold 5220R testing with a TYAN S7106 (V2.01.B40 BIOS) and ASPEED on Ubuntu 20.04 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 2212183-NE-CASCADELA10
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cascade lake refresh Suite 1.0.0 System Test suite extracted from cascade lake refresh. pts/brl-cad-1.3.0 VGR Performance Metric pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time pts/webp2-1.2.0 -q 100 -effort 9 Encode Settings: Quality 100, Lossless Compression pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=vgg16 Device: CPU - Batch Size: 64 - Model: VGG-16 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/lammps-1.4.0 benchmark_20k_atoms.in Model: 20k Atoms pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig 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/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=scalar Benchmark: vklBenchmark Scalar pts/build-python-1.0.0 --enable-optimizations --with-lto Build Configuration: Released Build, PGO + LTO Optimized 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=vgg16 Device: CPU - Batch Size: 32 - Model: VGG-16 pts/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/build-nodejs-1.2.0 Time To Compile pts/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic 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/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield 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=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 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/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/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/webp2-1.2.0 -q 95 -effort 7 Encode Settings: Quality 95, Compression Effort 7 pts/ncnn-1.4.0 -1 Target: CPU - Model: FastestDet pts/ncnn-1.4.0 -1 Target: CPU - Model: vision_transformer pts/ncnn-1.4.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.4.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.4.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.4.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.4.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.4.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.4.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.4.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.4.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.4.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.4.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.4.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.4.0 -1 Target: CPU - Model: mobilenet 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/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 2 2 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 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/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/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 50 Input: JPEG - Quality: 80 pts/stargate-1.1.0 192000 512 Sample Rate: 192000 - Buffer Size: 512 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 80 --num_reps 50 Input: PNG - Quality: 80 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/pgbench-1.12.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency 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 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency 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 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency 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 -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/pgbench-1.12.0 -s 100 -c 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 100 -c 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write pts/pgbench-1.12.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency 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 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 100 -c 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write pts/pgbench-1.12.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only pts/pgbench-1.12.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only pts/aom-av1-3.5.0 --cpu-used=4 Bosphorus_3840x2160.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K pts/numenta-nab-1.1.1 -d knncad Detector: KNN CAD 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/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/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time 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 1 1 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer pts/pgbench-1.12.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write pts/pgbench-1.12.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write pts/pgbench-1.12.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write pts/pgbench-1.12.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only pts/pgbench-1.12.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write pts/pgbench-1.12.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only pts/pgbench-1.12.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only pts/pgbench-1.12.0 -s 1 -c 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency pts/pgbench-1.12.0 -s 1 -c 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write pts/pgbench-1.12.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only pts/pgbench-1.12.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only 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/tensorflow-2.0.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40 Input: JPEG - Quality: 90 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/build-erlang-1.2.0 Time To Compile pts/jpegxl-1.5.0 sample-4.png out.jxl -q 90 --num_reps 40 Input: PNG - Quality: 90 pts/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation 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 2 2 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Repartition Test Time pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Group By Test Time pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Calculate Pi Benchmark pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time 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/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 1024 Workload: KV, 50% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 1024 Workload: KV, 60% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 1024 Workload: KV, 95% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 1024 Workload: KV, 10% Reads - Concurrency: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 512 Workload: KV, 50% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 512 Workload: KV, 95% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 512 Workload: KV, 60% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 512 Workload: KV, 10% Reads - Concurrency: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 256 Workload: KV, 60% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 256 Workload: KV, 10% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 256 Workload: KV, 95% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 256 Workload: KV, 50% Reads - Concurrency: 256 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 128 Workload: KV, 10% Reads - Concurrency: 128 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/stargate-1.1.0 192000 1024 Sample Rate: 192000 - Buffer Size: 1024 pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Broadcast Inner Join Test Time pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Inner Join Test Time pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Repartition Test Time pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Group By Test Time pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/cockroach-1.0.2 movr --concurrency 512 Workload: MoVR - Concurrency: 512 pts/cockroach-1.0.2 movr --concurrency 1024 Workload: MoVR - Concurrency: 1024 pts/cockroach-1.0.2 movr --concurrency 256 Workload: MoVR - Concurrency: 256 pts/cockroach-1.0.2 movr --concurrency 128 Workload: MoVR - Concurrency: 128 pts/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K 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/stargate-1.1.0 96000 512 Sample Rate: 96000 - Buffer Size: 512 pts/webp2-1.2.0 -q 75 -effort 7 Encode Settings: Quality 75, Compression Effort 7 pts/node-web-tooling-1.0.1 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/jpegxl-decode-1.5.0 --num_threads=1 --num_reps=100 CPU Threads: 1 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 --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 --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 3840 2160 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer 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/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/aom-av1-3.5.0 --cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K 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_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - 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/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Third Run pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Second Run pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, First Run / Cold Cache 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/stargate-1.1.0 96000 1024 Sample Rate: 96000 - Buffer Size: 1024 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/memtier-benchmark-1.4.1 -P redis -c 50 --ratio=10:1 Protocol: Redis - Clients: 50 - Set To Get Ratio: 10:1 pts/dragonflydb-1.0.0 -c 200 --ratio=1:1 Clients: 200 - Set To Get Ratio: 1:1 pts/dragonflydb-1.0.0 -c 200 --ratio=5:1 Clients: 200 - Set To Get Ratio: 5:1 pts/dragonflydb-1.0.0 -c 200 --ratio=1:5 Clients: 200 - Set To Get Ratio: 1:5 pts/dragonflydb-1.0.0 -c 50 --ratio=5:1 Clients: 50 - Set To Get Ratio: 5:1 pts/dragonflydb-1.0.0 -c 50 --ratio=1:1 Clients: 50 - Set To Get Ratio: 1:1 pts/dragonflydb-1.0.0 -c 50 --ratio=1:5 Clients: 50 - Set To Get Ratio: 1:5 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/jpegxl-decode-1.5.0 --num_reps=200 CPU Threads: All 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/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/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test 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/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/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M 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/tensorflow-2.0.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet 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/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/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU 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/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/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/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/rocksdb-1.3.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.3.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/graphics-magick-2.1.0 -resize 50% Operation: Resizing 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/rocksdb-1.3.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.3.0 --benchmarks="readrandom" Test: Random Read pts/graphics-magick-2.1.0 -sharpen 0x2.0 Operation: Sharpen pts/graphics-magick-2.1.0 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian pts/graphics-magick-2.1.0 -swirl 90 Operation: Swirl pts/graphics-magick-2.1.0 -enhance Operation: Enhanced pts/graphics-magick-2.1.0 -rotate 90 Operation: Rotate pts/graphics-magick-2.1.0 -colorspace HWB Operation: HWB Color Space 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/avifenc-1.3.0 -s 2 Encoder Speed: 2 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/build-php-1.6.0 Time To Compile 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/stargate-1.1.0 48000 512 Sample Rate: 480000 - Buffer Size: 512 pts/spacy-1.0.0 Model: en_core_web_trf pts/spacy-1.0.0 Model: en_core_web_lg pts/webp-1.2.0 -q 100 -lossless -m 6 Encode Settings: Quality 100, Lossless, Highest Compression pts/stargate-1.1.0 48000 1024 Sample Rate: 480000 - Buffer Size: 1024 pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/stargate-1.1.0 44100 512 Sample Rate: 44100 - Buffer Size: 512 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/encodec-1.0.1 -b 24 Target Bandwidth: 24 kbps pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE 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/compress-7zip-1.10.0 Test: Decompression Rating pts/compress-7zip-1.10.0 Test: Compression Rating pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K 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/stargate-1.1.0 44100 1024 Sample Rate: 44100 - Buffer Size: 1024 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/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/encodec-1.0.1 -b 1.5 Target Bandwidth: 1.5 kbps pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet 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/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:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/srsran-1.2.0 lib/src/phy/dft/test/ofdm_test -N 2048 -n 100 -r 500000 Test: OFDM_Test pts/encodec-1.0.1 -b 6 Target Bandwidth: 6 kbps pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K 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/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/encodec-1.0.1 -b 3 Target Bandwidth: 3 kbps 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/tensorflow-2.0.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/stress-ng-1.6.0 --futex -1 Test: Futex pts/cpuminer-opt-1.6.0 -a skein Algorithm: Skeincoin pts/cpuminer-opt-1.6.0 -a m7m Algorithm: Magi pts/stress-ng-1.6.0 --switch -1 Test: Context Switching pts/stress-ng-1.6.0 --rdrand -1 Test: x86_64 RdRand pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/aircrack-ng-1.3.0 pts/stress-ng-1.6.0 --msg -1 Test: System V Message Passing pts/stress-ng-1.6.0 --sem -1 Test: Semaphores pts/stress-ng-1.6.0 --malloc -1 Test: Malloc pts/stress-ng-1.6.0 --numa -1 Test: NUMA pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/stress-ng-1.6.0 --fork -1 Test: Forking pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/stress-ng-1.6.0 --matrix -1 Test: Matrix Math pts/stress-ng-1.6.0 --crypt -1 Test: Crypto pts/stress-ng-1.6.0 --qsort -1 Test: Glibc Qsort Data Sorting pts/cpuminer-opt-1.6.0 -a allium Algorithm: Garlicoin pts/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/stress-ng-1.6.0 --mmap -1 Test: MMAP pts/stress-ng-1.6.0 --sock -1 Test: Socket Activity 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 --mutex -1 Test: Mutex pts/stress-ng-1.6.0 --vecmath -1 Test: Vector Math pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/cpuminer-opt-1.6.0 -a myr-gr Algorithm: Myriad-Groestl pts/cpuminer-opt-1.6.0 -a x25x Algorithm: x25x pts/cpuminer-opt-1.6.0 -a minotaur Algorithm: Ringcoin 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 sha256q Algorithm: Quad SHA-256, Pyrite pts/cpuminer-opt-1.6.0 -a scrypt Algorithm: scrypt pts/natron-1.1.0 Natron_2.3.12_Spaceship/Natron_project/Spaceship_Natron.ntp Input: Spaceship pts/redis-1.4.0 -t set -c 1000 Test: SET - Parallel Connections: 1000 pts/cpuminer-opt-1.6.0 -a sha256t Algorithm: Triple SHA-256, Onecoin pts/cpuminer-opt-1.6.0 -a lbry Algorithm: LBC, LBRY Credits pts/redis-1.4.0 -t set -c 500 Test: SET - Parallel Connections: 500 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=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=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/redis-1.4.0 -t set -c 50 Test: SET - Parallel Connections: 50 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/y-cruncher-1.2.0 1b Pi Digits To Calculate: 1B pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/redis-1.4.0 -t get -c 50 Test: GET - Parallel Connections: 50 pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet 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/redis-1.4.0 -t get -c 1000 Test: GET - Parallel Connections: 1000 pts/redis-1.4.0 -t get -c 500 Test: GET - Parallel Connections: 500 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/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/svt-av1-2.7.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/encode-flac-1.8.1 WAV To FLAC 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/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 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU 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 Build Configuration: Default pts/webp-1.2.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough 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/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - 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/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/y-cruncher-1.2.0 500m Pi Digits To Calculate: 500M pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast 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/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy 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/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 --matmul --batch=inputs/matmul/shapes_transformer --cfg=f32 --engine=cpu Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU 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 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless 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-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 --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_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/unpack-linux-1.2.0 linux-5.19.tar.xz pts/webp-1.2.0 -q 100 -m 6 Encode Settings: Quality 100, Highest Compression pts/astcenc-1.4.0 -medium -repeats 20 Preset: Medium pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless 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/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 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 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/webp2-1.2.0 Encode Settings: Default pts/webp2-1.2.0 -q 100 -effort 5 Encode Settings: Quality 100, Compression Effort 5 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_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - 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/webp-1.2.0 -q 100 Encode Settings: Quality 100 pts/lammps-1.4.0 in.rhodo Model: Rhodopsin Protein pts/webp-1.2.0 Encode Settings: Default pts/stress-ng-1.6.0 --io-uring -1 Test: IO_uring