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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Audio Encoding 2 Tests
AV1 3 Tests
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Timed Code Compilation 5 Tests
C/C++ Compiler Tests 9 Tests
CPU Massive 16 Tests
Creator Workloads 18 Tests
Cryptocurrency Benchmarks, CPU Mining Tests 2 Tests
Cryptography 3 Tests
Database Test Suite 8 Tests
Encoding 5 Tests
Game Development 3 Tests
HPC - High Performance Computing 13 Tests
Imaging 6 Tests
Common Kernel Benchmarks 3 Tests
Machine Learning 8 Tests
Molecular Dynamics 2 Tests
Multi-Core 20 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 4 Tests
OpenMPI Tests 4 Tests
Programmer / Developer System Benchmarks 6 Tests
Python Tests 9 Tests
Renderers 3 Tests
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Server 9 Tests
Server CPU Tests 10 Tests
Single-Threaded 2 Tests
Video Encoding 3 Tests
Common Workstation Benchmarks 2 Tests

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cascade lake refresh Suite 1.0.0 System Test suite extracted from cascade lake refresh. 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 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency 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/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 - Repartition Test Time 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 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Broadcast Inner Join Test Time 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 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, First Run / Cold Cache pts/lammps-1.4.0 in.rhodo Model: Rhodopsin Protein pts/stress-ng-1.6.0 --sock -1 Test: Socket Activity pts/stargate-1.1.0 48000 512 Sample Rate: 480000 - Buffer Size: 512 pts/dragonflydb-1.0.0 -c 50 --ratio=1:1 Clients: 50 - Set To Get Ratio: 1:1 pts/stargate-1.1.0 96000 1024 Sample Rate: 96000 - Buffer Size: 1024 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 256 Workload: KV, 10% Reads - Concurrency: 256 pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Inner Join Test Time 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/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 512 Workload: KV, 10% Reads - Concurrency: 512 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/encodec-1.0.1 -b 3 Target Bandwidth: 3 kbps pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 512 Workload: KV, 60% Reads - Concurrency: 512 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/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Inner Join Test Time pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 128 Workload: KV, 50% Reads - Concurrency: 128 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 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency pts/cockroach-1.0.2 movr --concurrency 256 Workload: MoVR - Concurrency: 256 pts/stress-ng-1.6.0 --switch -1 Test: Context Switching 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=1:1 Clients: 200 - Set To Get Ratio: 1:1 pts/rocksdb-1.3.0 --benchmarks="readrandom" Test: Random Read pts/dragonflydb-1.0.0 -c 200 --ratio=1:5 Clients: 200 - Set To Get Ratio: 1:5 pts/stargate-1.1.0 48000 1024 Sample Rate: 480000 - Buffer Size: 1024 pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Third Run 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/stargate-1.1.0 44100 512 Sample Rate: 44100 - Buffer Size: 512 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 256 Workload: KV, 60% Reads - Concurrency: 256 pts/stress-ng-1.6.0 --futex -1 Test: Futex pts/encodec-1.0.1 -b 24 Target Bandwidth: 24 kbps pts/ncnn-1.4.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.4.0 -1 Target: CPU - Model: vgg16 pts/numenta-nab-1.1.1 -d knncad Detector: KNN CAD pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/ncnn-1.4.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/redis-1.4.0 -t get -c 50 Test: GET - Parallel Connections: 50 pts/ncnn-1.4.0 -1 Target: CPU - Model: FastestDet pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Repartition Test Time pts/ncnn-1.4.0 -1 Target: CPU - Model: efficientnet-b0 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 60 --concurrency 128 Workload: KV, 60% Reads - Concurrency: 128 pts/ncnn-1.4.0 -1 Target: CPU - Model: shufflenet-v2 pts/redis-1.4.0 -t get -c 500 Test: GET - Parallel Connections: 500 pts/ncnn-1.4.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - Group By Test Time pts/encodec-1.0.1 -b 1.5 Target Bandwidth: 1.5 kbps pts/ncnn-1.4.0 -1 Target: CPU - Model: googlenet pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 10 Input: JPEG - Quality: 100 pts/mnn-2.1.0 Model: mobilenet-v1-1.0 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 256 Workload: KV, 50% Reads - Concurrency: 256 pts/ncnn-1.4.0 -1 Target: CPU - Model: mnasnet pts/aom-av1-3.5.0 --cpu-used=4 Bosphorus_3840x2160.y4m Encoder Mode: Speed 4 Two-Pass - Input: Bosphorus 4K pts/cockroach-1.0.2 kv --ramp 10s --read-percent 50 --concurrency 512 Workload: KV, 50% Reads - Concurrency: 512 pts/ncnn-1.4.0 -1 Target: CPU - Model: squeezenet_ssd pts/dragonflydb-1.0.0 -c 50 --ratio=1:5 Clients: 50 - Set To Get Ratio: 1:5 pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/stargate-1.1.0 192000 512 Sample Rate: 192000 - Buffer Size: 512 pts/ncnn-1.4.0 -1 Target: CPU - Model: regnety_400m 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/stargate-1.1.0 96000 512 Sample Rate: 96000 - Buffer Size: 512 pts/ncnn-1.4.0 -1 Target: CPU - Model: blazeface pts/spark-1.0.0 -r 1000000 -p 2000 Row Count: 1000000 - Partitions: 2000 - SHA-512 Benchmark Time pts/clickhouse-1.1.0 100M Rows Web Analytics Dataset, Second Run pts/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline pts/mnn-2.1.0 Model: resnet-v2-50 pts/ncnn-1.4.0 -1 Target: CPU - Model: yolov4-tiny pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 128 Workload: KV, 95% Reads - Concurrency: 128 pts/cpuminer-opt-1.6.0 -a myr-gr Algorithm: Myriad-Groestl pts/webp2-1.2.0 -q 75 -effort 7 Encode Settings: Quality 75, Compression Effort 7 pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Group By Test Time pts/pgbench-1.12.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K 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/ncnn-1.4.0 -1 Target: CPU - Model: resnet18 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/build-php-1.6.0 Time To Compile 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/avifenc-1.3.0 -s 0 Encoder Speed: 0 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 128 Workload: KV, 10% Reads - Concurrency: 128 pts/graphics-magick-2.1.0 -rotate 90 Operation: Rotate pts/cockroach-1.0.2 kv --ramp 10s --read-percent 10 --concurrency 1024 Workload: KV, 10% Reads - Concurrency: 1024 pts/stress-ng-1.6.0 --msg -1 Test: System V Message Passing pts/cockroach-1.0.2 movr --concurrency 512 Workload: MoVR - Concurrency: 512 pts/stargate-1.1.0 192000 1024 Sample Rate: 192000 - Buffer Size: 1024 pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=scalar Benchmark: vklBenchmark Scalar pts/spacy-1.0.0 Model: en_core_web_trf pts/avifenc-1.3.0 -s 2 Encoder Speed: 2 pts/minibude-1.0.0 --deck ../data/bm1 --iterations 500 Implementation: OpenMP - Input Deck: BM1 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 80 --num_reps 50 Input: PNG - Quality: 80 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/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/webp2-1.2.0 Encode Settings: Default pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K pts/openvkl-1.3.0 vklBenchmark --benchmark_filter=ispc Benchmark: vklBenchmark ISPC 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/webp2-1.2.0 -q 100 -effort 5 Encode Settings: Quality 100, Compression Effort 5 pts/pgbench-1.12.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency pts/graphics-magick-2.1.0 -swirl 90 Operation: Swirl 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/graphics-magick-2.1.0 -colorspace HWB Operation: HWB Color Space pts/pgbench-1.12.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency 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/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/node-web-tooling-1.0.1 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 100 --num_reps 10 Input: PNG - Quality: 100 pts/brl-cad-1.3.0 VGR Performance Metric 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/ncnn-1.4.0 -1 Target: CPU - Model: mobilenet pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K 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/rocksdb-1.3.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/jpegxl-decode-1.5.0 --num_threads=1 --num_reps=100 CPU Threads: 1 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/pgbench-1.12.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write pts/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield 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 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only pts/pgbench-1.12.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency pts/y-cruncher-1.2.0 500m Pi Digits To Calculate: 500M pts/jpegxl-1.5.0 sample-4.png out.jxl -q 90 --num_reps 40 Input: PNG - Quality: 90 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/cockroach-1.0.2 movr --concurrency 1024 Workload: MoVR - Concurrency: 1024 pts/stress-ng-1.6.0 --qsort -1 Test: Glibc Qsort Data Sorting pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40 Input: JPEG - Quality: 90 pts/redis-1.4.0 -t get -c 1000 Test: GET - Parallel Connections: 1000 pts/encodec-1.0.1 -b 6 Target Bandwidth: 6 kbps pts/mnn-2.1.0 Model: inception-v3 pts/deepsparse-1.0.1 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/mnn-2.1.0 Model: mobilenetV3 pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 1024 Workload: KV, 95% Reads - Concurrency: 1024 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/stress-ng-1.6.0 --sendfile -1 Test: SENDFILE pts/mnn-2.1.0 Model: MobileNetV2_224 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 - Calculate Pi Benchmark Using Dataframe pts/minibude-1.0.0 --deck ../data/bm2 --iterations 10 Implementation: OpenMP - Input Deck: BM2 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/stargate-1.1.0 44100 1024 Sample Rate: 44100 - Buffer Size: 1024 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/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/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/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 50 Input: JPEG - Quality: 80 pts/stress-ng-1.6.0 --cpu -1 --cpu-method all Test: CPU Stress 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/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet 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/compress-7zip-1.10.0 Test: Compression Rating pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 256 Workload: KV, 95% Reads - Concurrency: 256 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/astcenc-1.4.0 -medium -repeats 20 Preset: Medium pts/build-erlang-1.2.0 Time To Compile pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/tensorflow-2.0.0 --device cpu --batch_size=64 --model=vgg16 Device: CPU - Batch Size: 64 - Model: VGG-16 pts/cpuminer-opt-1.6.0 -a blake2s Algorithm: Blake-2 S 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/cpuminer-opt-1.6.0 -a minotaur Algorithm: Ringcoin pts/pgbench-1.12.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write 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/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/tensorflow-2.0.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/aircrack-ng-1.3.0 pts/graphics-magick-2.1.0 -sharpen 0x2.0 Operation: Sharpen 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/stress-ng-1.6.0 --numa -1 Test: NUMA 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/pgbench-1.12.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency 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/jpegxl-decode-1.5.0 --num_reps=200 CPU Threads: All pts/rocksdb-1.3.0 --benchmarks="updaterandom" Test: Update Random pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/webp-1.2.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/graphics-magick-2.1.0 -enhance Operation: Enhanced 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/encode-flac-1.8.1 WAV To FLAC 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 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency pts/mnn-2.1.0 Model: SqueezeNetV1.0 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/mnn-2.1.0 Model: nasnet pts/pgbench-1.12.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian pts/redis-1.4.0 -t set -c 500 Test: SET - Parallel Connections: 500 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 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: 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/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless pts/webp-1.2.0 Encode Settings: Default pts/stress-ng-1.6.0 --matrix -1 Test: Matrix Math pts/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE 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 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency pts/svt-av1-2.7.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/pgbench-1.12.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only pts/graphics-magick-2.1.0 -resize 50% Operation: Resizing pts/cpuminer-opt-1.6.0 -a sha256q Algorithm: Quad SHA-256, Pyrite 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/cpuminer-opt-1.6.0 -a scrypt Algorithm: scrypt 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/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/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/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/openvino-1.1.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Execution Time pts/stress-ng-1.6.0 --mmap -1 Test: MMAP 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/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/build-python-1.0.0 Build Configuration: Default 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/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/cpuminer-opt-1.6.0 -a lbry Algorithm: LBC, LBRY Credits pts/rocksdb-1.3.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark Using Dataframe pts/pgbench-1.12.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only pts/stress-ng-1.6.0 --fork -1 Test: Forking pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/ncnn-1.4.0 -1 Target: CPU - Model: vision_transformer pts/lammps-1.4.0 benchmark_20k_atoms.in Model: 20k Atoms 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/pgbench-1.12.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=vgg16 Device: CPU - Batch Size: 32 - Model: VGG-16 pts/pgbench-1.12.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency pts/tensorflow-2.0.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m S Input: drivaerFastback, Small Mesh Size - Mesh Time 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/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/build-nodejs-1.2.0 Time To Compile pts/pgbench-1.12.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 pts/pgbench-1.12.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only pts/spacy-1.0.0 Model: en_core_web_lg pts/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic pts/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig 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/pgbench-1.12.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only pts/stress-ng-1.6.0 --mutex -1 Test: Mutex 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/cpuminer-opt-1.6.0 -a m7m Algorithm: Magi pts/compress-7zip-1.10.0 Test: Decompression Rating 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/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/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/stress-ng-1.6.0 --sem -1 Test: Semaphores 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/dragonflydb-1.0.0 -c 50 --ratio=5:1 Clients: 50 - Set To Get Ratio: 5:1 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/graphics-magick-2.1.0 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian 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/cpuminer-opt-1.6.0 -a allium Algorithm: Garlicoin pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - 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/tensorflow-2.0.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam 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/deepsparse-1.0.1 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario sync Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/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/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/mnn-2.1.0 Model: squeezenetv1.1 pts/memtier-benchmark-1.4.1 -P redis -c 50 --ratio=10:1 Protocol: Redis - Clients: 50 - Set To Get Ratio: 10:1 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/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/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - Calculate Pi Benchmark pts/y-cruncher-1.2.0 1b Pi Digits To Calculate: 1B pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Execution Time 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/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container 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/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/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/cpuminer-opt-1.6.0 -a skein Algorithm: Skeincoin pts/stress-ng-1.6.0 --vecmath -1 Test: Vector Math 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 3 3 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer 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/webp-1.2.0 -q 100 Encode Settings: Quality 100 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 2 2 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=vgg16 Device: CPU - Batch Size: 16 - Model: VGG-16 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/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/stress-ng-1.6.0 --malloc -1 Test: Malloc 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/cpuminer-opt-1.6.0 -a x25x Algorithm: x25x pts/srsran-1.2.0 lib/src/phy/dft/test/ofdm_test -N 2048 -n 100 -r 500000 Test: OFDM_Test pts/redis-1.4.0 -t set -c 50 Test: SET - Parallel Connections: 50 pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/ncnn-1.4.0 -1 Target: CPU - Model: resnet50 pts/redis-1.4.0 -t set -c 1000 Test: SET - Parallel Connections: 1000 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/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/onednn-2.7.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/cpuminer-opt-1.6.0 -a sha256t Algorithm: Triple SHA-256, Onecoin 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 ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/unpack-linux-1.2.0 linux-5.19.tar.xz pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive 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/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/cockroach-1.0.2 movr --concurrency 128 Workload: MoVR - Concurrency: 128 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 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: 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/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/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/cpuminer-opt-1.6.0 -a deep Algorithm: Deepcoin pts/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/build-python-1.0.0 --enable-optimizations --with-lto Build Configuration: Released Build, PGO + LTO Optimized pts/ospray-studio-1.1.0 --cameras 3 3 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer pts/ospray-studio-1.1.0 --cameras 1 1 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer 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/stress-ng-1.6.0 --rdrand -1 Test: x86_64 RdRand pts/pgbench-1.12.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless pts/stress-ng-1.6.0 --crypt -1 Test: Crypto pts/openfoam-1.2.0 incompressible/simpleFoam/drivaerFastback/ -m M Input: drivaerFastback, Medium Mesh Size - Mesh Time pts/cockroach-1.0.2 kv --ramp 10s --read-percent 95 --concurrency 512 Workload: KV, 95% Reads - Concurrency: 512 pts/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy pts/natron-1.1.0 Natron_2.3.12_Spaceship/Natron_project/Spaceship_Natron.ntp Input: Spaceship 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 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.12.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency pts/spark-1.0.0 -r 1000000 -p 100 Row Count: 1000000 - Partitions: 100 - SHA-512 Benchmark Time 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/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=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K pts/webp2-1.2.0 -q 100 -effort 9 Encode Settings: Quality 100, Lossless Compression pts/webp2-1.2.0 -q 95 -effort 7 Encode Settings: Quality 95, 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/stress-ng-1.6.0 --io-uring -1 Test: IO_uring