threadripper eo 2022

AMD Ryzen Threadripper 3960X 24-Core testing with a MSI Creator TRX40 (MS-7C59) v1.0 (1.12N1 BIOS) and Gigabyte AMD Radeon RX 5500 XT 8GB 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 2212279-NE-THREADRIP40
Jump To Table - Results

View

Do Not Show Noisy Results
Do Not Show Results With Incomplete Data
Do Not Show Results With Little Change/Spread
List Notable Results

Limit displaying results to tests within:

Audio Encoding 2 Tests
AV1 4 Tests
C++ Boost Tests 2 Tests
Timed Code Compilation 6 Tests
C/C++ Compiler Tests 9 Tests
CPU Massive 17 Tests
Creator Workloads 19 Tests
Cryptography 2 Tests
Database Test Suite 7 Tests
Encoding 7 Tests
Game Development 3 Tests
HPC - High Performance Computing 12 Tests
Imaging 6 Tests
Common Kernel Benchmarks 3 Tests
Machine Learning 9 Tests
Multi-Core 19 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 3 Tests
OpenMPI Tests 3 Tests
Programmer / Developer System Benchmarks 6 Tests
Python 2 Tests
Renderers 2 Tests
Rust Tests 2 Tests
Server 8 Tests
Server CPU Tests 11 Tests
Single-Threaded 3 Tests
Video Encoding 5 Tests
Common Workstation Benchmarks 2 Tests

Statistics

Show Overall Harmonic Mean(s)
Show Overall Geometric Mean
Show Geometric Means Per-Suite/Category
Show Wins / Losses Counts (Pie Chart)
Normalize Results
Remove Outliers Before Calculating Averages

Graph Settings

Force Line Graphs Where Applicable
Convert To Scalar Where Applicable
Prefer Vertical Bar Graphs

Multi-Way Comparison

Condense Multi-Option Tests Into Single Result Graphs

Table

Show Detailed System Result Table

Run Management

Highlight
Result
Hide
Result
Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
a
December 26 2022
  7 Hours, 56 Minutes
b
December 26 2022
  8 Hours
Invert Hiding All Results Option
  7 Hours, 58 Minutes
Only show results matching title/arguments (delimit multiple options with a comma):
Do not show results matching title/arguments (delimit multiple options with a comma):


threadripper eo 2022 Suite 1.0.0 System Test suite extracted from threadripper eo 2022. pts/brl-cad-1.3.0 VGR Performance Metric 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/build-linux-kernel-1.15.0 allmodconfig Build: allmodconfig pts/smhasher-1.1.0 --test=Speed sha3-256 Hash: SHA3-256 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/nekrs-1.0.0 turbPipePeriodic turbPipe.par Input: TurboPipe Periodic 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/openradioss-1.0.0 fsi_drop_container_0000.rad fsi_drop_container_0001.rad Model: INIVOL and Fluid Structure Interaction Drop Container pts/ffmpeg-3.0.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/ffmpeg-3.0.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/ffmpeg-3.0.0 --encoder=libx264 upload Encoder: libx264 - Scenario: Upload pts/build-python-1.0.0 --enable-optimizations --with-lto Build Configuration: Released Build, PGO + LTO Optimized pts/ffmpeg-3.0.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/build-nodejs-1.2.0 Time To Compile pts/ffmpeg-3.0.0 --encoder=libx264 platform Encoder: libx264 - Scenario: Platform pts/ffmpeg-3.0.0 --encoder=libx264 vod Encoder: libx264 - Scenario: Video On Demand 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/openradioss-1.0.0 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield 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/webp2-1.2.0 -q 95 -effort 7 Encode Settings: Quality 95, Compression Effort 7 pts/scikit-learn-1.2.0 random_projections.py --n-times 100 Benchmark: Sparse Random Projections, 100 Iterations 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/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/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 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 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 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 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 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/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 50 Input: JPEG - Quality: 80 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 80 --num_reps 50 Input: PNG - Quality: 80 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/stargate-1.1.0 192000 512 Sample Rate: 192000 - Buffer Size: 512 pts/numenta-nab-1.1.1 -d knncad Detector: KNN CAD pts/openfoam-1.2.0 incompressible/simpleFoam/motorBike/ Input: motorBike - Execution Time pts/openfoam-1.2.0 incompressible/simpleFoam/motorBike/ Input: motorBike - Mesh Time pts/stream-1.3.4 Copy Type: Copy pts/jpegxl-1.5.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 40 Input: JPEG - Quality: 90 pts/jpegxl-1.5.0 sample-4.png out.jxl -q 90 --num_reps 40 Input: PNG - Quality: 90 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 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 512 Workload: KV, 10% 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 95 --concurrency 512 Workload: KV, 95% Reads - Concurrency: 512 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 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 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 60 --concurrency 128 Workload: KV, 60% 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 95 --concurrency 128 Workload: KV, 95% Reads - Concurrency: 128 pts/scikit-learn-1.2.0 mnist.py Benchmark: MNIST Dataset pts/openradioss-1.0.0 Bumper_Beam_AP_meshed_0000.rad Bumper_Beam_AP_meshed_0001.rad Model: Bumper Beam 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 movr --concurrency 256 Workload: MoVR - Concurrency: 256 pts/cockroach-1.0.2 movr --concurrency 128 Workload: MoVR - Concurrency: 128 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/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/ffmpeg-3.0.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/avifenc-1.3.0 -s 0 Encoder Speed: 0 pts/nginx-3.0.0 -c 1000 Connections: 1000 pts/build-erlang-1.2.0 Time To Compile pts/nginx-3.0.0 -c 500 Connections: 500 pts/nginx-3.0.0 -c 200 Connections: 200 pts/nginx-3.0.0 -c 100 Connections: 100 pts/stargate-1.1.0 96000 512 Sample Rate: 96000 - Buffer Size: 512 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/openradioss-1.0.0 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation 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=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - 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/webp2-1.2.0 -q 75 -effort 7 Encode Settings: Quality 75, Compression Effort 7 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/numenta-nab-1.1.1 -d earthgeckoSkyline Detector: Earthgecko Skyline 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/stargate-1.1.0 192000 1024 Sample Rate: 192000 - Buffer Size: 1024 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/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/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/dragonflydb-1.0.0 -c 200 --ratio=1:5 Clients: 200 - Set To Get Ratio: 1:5 pts/dragonflydb-1.0.0 -c 200 --ratio=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/openvino-1.2.0 -m models/intel/person-detection-0106/FP16/person-detection-0106.xml -d CPU Model: Person Detection FP16 - Device: CPU 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=5:1 Clients: 50 - Set To Get Ratio: 5:1 pts/dragonflydb-1.0.0 -c 50 --ratio=1:5 Clients: 50 - Set To Get Ratio: 1:5 pts/openvino-1.2.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/xmrig-1.1.0 --bench=1M Variant: Monero - Hash Count: 1M pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU 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/openvino-1.2.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -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/openradioss-1.0.0 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test 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/rocksdb-1.3.0 --benchmarks="fillsync" Test: Random Fill Sync pts/openvino-1.2.0 -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/spacy-1.0.0 Model: en_core_web_trf pts/spacy-1.0.0 Model: en_core_web_lg pts/openvino-1.2.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.2.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.2.0 -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-1.2.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.2.0 -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-1.2.0 -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="fillrandom" Test: Random Fill 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/graphics-magick-2.1.0 -enhance Operation: Enhanced pts/graphics-magick-2.1.0 -sharpen 0x2.0 Operation: Sharpen pts/graphics-magick-2.1.0 -rotate 90 Operation: Rotate 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 -swirl 90 Operation: Swirl pts/graphics-magick-2.1.0 -operator all Noise-Gaussian 30% Operation: Noise-Gaussian pts/graphics-magick-2.1.0 -colorspace HWB Operation: HWB Color Space pts/aom-av1-3.5.0 --cpu-used=0 --limit=20 Bosphorus_3840x2160.y4m Encoder Mode: Speed 0 Two-Pass - Input: Bosphorus 4K pts/jpegxl-decode-1.5.0 --num_threads=1 --num_reps=100 CPU Threads: 1 pts/aom-av1-3.5.0 --cpu-used=6 Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Two-Pass - Input: Bosphorus 4K 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/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/rocksdb-1.3.0 --benchmarks="fillseq" Test: Sequential Fill pts/stargate-1.1.0 44100 512 Sample Rate: 44100 - Buffer Size: 512 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/deepsparse-1.0.1 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario sync Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream pts/stargate-1.1.0 48000 512 Sample Rate: 480000 - Buffer Size: 512 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/xmrig-1.1.0 -a rx/wow --bench=1M Variant: Wownero - Hash Count: 1M pts/avifenc-1.3.0 -s 2 Encoder Speed: 2 pts/build-wasmer-1.2.0 Time To Compile pts/stargate-1.1.0 96000 1024 Sample Rate: 96000 - Buffer Size: 1024 pts/ffmpeg-3.0.0 --encoder=libx264 live Encoder: libx264 - Scenario: Live pts/build-linux-kernel-1.15.0 defconfig Build: defconfig pts/build-php-1.6.0 Time To Compile pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet 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/encodec-1.0.1 -b 24 Target Bandwidth: 24 kbps pts/deepsparse-1.0.1 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection,YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.0.1 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/webp-1.2.0 -q 100 -lossless -m 6 Encode Settings: Quality 100, Lossless, Highest Compression pts/numenta-nab-1.1.1 -d contextOSE Detector: Contextual Anomaly Detector OSE 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/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/encodec-1.0.1 -b 3 Target Bandwidth: 3 kbps pts/encodec-1.0.1 -b 6 Target Bandwidth: 6 kbps 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 sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/unvanquished-1.7.0 +set r_customWidth 1920 +set r_customHeight 1080 +preset presets/graphics/ultra.cfg Resolution: 1920 x 1080 - Effects Quality: Ultra pts/unvanquished-1.7.0 +set r_customWidth 1920 +set r_customHeight 1080 +preset presets/graphics/high.cfg Resolution: 1920 x 1080 - Effects Quality: High pts/encodec-1.0.1 -b 1.5 Target Bandwidth: 1.5 kbps pts/redis-1.4.0 -t set -c 50 Test: SET - Parallel Connections: 50 pts/astcenc-1.4.0 -exhaustive -repeats 2 Preset: Exhaustive pts/redis-1.4.0 -t set -c 500 Test: SET - Parallel Connections: 500 pts/unvanquished-1.7.0 +set r_customWidth 1920 +set r_customHeight 1080 +preset presets/graphics/medium.cfg Resolution: 1920 x 1080 - Effects Quality: Medium pts/stargate-1.1.0 48000 1024 Sample Rate: 480000 - Buffer Size: 1024 pts/srsran-1.2.0 lib/src/phy/dft/test/ofdm_test -N 2048 -n 100 -r 500000 Test: OFDM_Test 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/stargate-1.1.0 44100 1024 Sample Rate: 44100 - Buffer Size: 1024 pts/scikit-learn-1.2.0 tsne_mnist.py Benchmark: TSNE MNIST Dataset pts/tensorflow-2.0.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/compress-7zip-1.10.0 Test: Decompression Rating pts/compress-7zip-1.10.0 Test: Compression Rating pts/stress-ng-1.6.0 --str -1 Test: Glibc C String Functions pts/stress-ng-1.6.0 --switch -1 Test: Context Switching pts/stress-ng-1.6.0 --atomic -1 Test: Atomic pts/stress-ng-1.6.0 --futex -1 Test: Futex pts/stress-ng-1.6.0 --memcpy -1 Test: Memory Copying pts/stress-ng-1.6.0 --io-uring -1 Test: IO_uring pts/stress-ng-1.6.0 --malloc -1 Test: Malloc pts/stress-ng-1.6.0 --memfd -1 Test: MEMFD pts/stress-ng-1.6.0 --cpu -1 --cpu-method all Test: CPU Stress pts/stress-ng-1.6.0 --fork -1 Test: Forking pts/stress-ng-1.6.0 --numa -1 Test: NUMA 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 --mmap -1 Test: MMAP pts/stress-ng-1.6.0 --qsort -1 Test: Glibc Qsort Data Sorting pts/stress-ng-1.6.0 --vecmath -1 Test: Vector Math pts/stress-ng-1.6.0 --cache -1 Test: CPU Cache pts/stress-ng-1.6.0 --sock -1 Test: Socket Activity 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 --matrix -1 Test: Matrix Math pts/stress-ng-1.6.0 --sendfile -1 Test: SENDFILE pts/redis-1.4.0 -t get -c 500 Test: GET - Parallel Connections: 500 pts/redis-1.4.0 -t get -c 50 Test: GET - Parallel Connections: 50 pts/natron-1.1.0 Natron_2.3.12_Spaceship/Natron_project/Spaceship_Natron.ntp Input: Spaceship pts/rav1e-1.7.0 -s 1 -l 20 Speed: 1 pts/y-cruncher-1.2.0 1b Pi Digits To Calculate: 1B pts/aom-av1-3.5.0 --cpu-used=6 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 6 Realtime - Input: Bosphorus 4K pts/jpegxl-decode-1.5.0 --num_reps=200 CPU Threads: All 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/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/rav1e-1.7.0 -s 5 -l 60 Speed: 5 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/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/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/astcenc-1.4.0 -thorough -repeats 10 Preset: Thorough pts/numenta-nab-1.1.1 -d bayesChangePt Detector: Bayesian Changepoint pts/aom-av1-3.5.0 --cpu-used=8 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 8 Realtime - Input: Bosphorus 4K 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/encode-flac-1.8.1 WAV To FLAC pts/rav1e-1.7.0 -s 6 -l 60 Speed: 6 pts/webp-1.2.0 -q 100 -lossless Encode Settings: Quality 100, Lossless pts/aom-av1-3.5.0 --cpu-used=10 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 10 Realtime - Input: Bosphorus 4K pts/aom-av1-3.5.0 --cpu-used=9 --rt Bosphorus_3840x2160.y4m Encoder Mode: Speed 9 Realtime - Input: Bosphorus 4K pts/build-python-1.0.0 Build Configuration: Default 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/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/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/svt-av1-2.7.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/y-cruncher-1.2.0 500m Pi Digits To Calculate: 500M pts/numenta-nab-1.1.1 -d relativeEntropy Detector: Relative Entropy pts/smhasher-1.1.0 --test=Speed FarmHash128 Hash: FarmHash128 pts/rav1e-1.7.0 -s 10 -l 100 Speed: 10 pts/astcenc-1.4.0 -fast -repeats 120 Preset: Fast 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/smhasher-1.1.0 --test=Speed MeowHash Hash: MeowHash x86_64 AES-NI pts/smhasher-1.1.0 --test=Speed Spooky32 Hash: Spooky32 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/smhasher-1.1.0 --test=Speed FarmHash32 Hash: FarmHash32 x86_64 AVX 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/smhasher-1.1.0 --test=Speed fasthash32 Hash: fasthash32 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/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/smhasher-1.1.0 --test=Speed t1ha2_atonce Hash: t1ha2_atonce pts/smhasher-1.1.0 --test=Speed t1ha0_aes_avx2 Hash: t1ha0_aes_avx2 x86_64 pts/astcenc-1.4.0 -medium -repeats 20 Preset: Medium pts/avifenc-1.3.0 -s 6 -l Encoder Speed: 6, Lossless pts/webp-1.2.0 -q 100 -m 6 Encode Settings: Quality 100, Highest Compression pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/unpack-linux-1.2.0 linux-5.19.tar.xz pts/smhasher-1.1.0 --test=Speed wyhash Hash: wyhash 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/numenta-nab-1.1.1 -d windowedGaussian Detector: Windowed Gaussian 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/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/svt-av1-2.7.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/avifenc-1.3.0 -s 10 -l Encoder Speed: 10, Lossless pts/avifenc-1.3.0 -s 6 Encoder Speed: 6 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/webp2-1.2.0 -q 100 -effort 5 Encode Settings: Quality 100, Compression Effort 5 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/webp2-1.2.0 Encode Settings: Default 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/webp-1.2.0 -q 100 Encode Settings: Quality 100 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/webp-1.2.0 Encode Settings: Default pts/nginx-3.0.0 -c 4000 Connections: 4000 pts/nginx-3.0.0 -c 1 Connections: 1 pts/stress-ng-1.6.0 --rdrand -1 Test: x86_64 RdRand 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/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 pts/nginx-3.0.0 -c 20 Connections: 20 pts/stream-1.3.4 Add Type: Add pts/stream-1.3.4 Triad Type: Triad pts/stream-1.3.4 Scale Type: Scale