Ryzen 9 7900

AMD Ryzen 9 7900 12-Core testing with a Gigabyte B650M DS3H (F4h BIOS) and Gigabyte AMD Raphael 512MB on Ubuntu 22.10 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 2301319-NE-RYZEN979047
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C/C++ Compiler Tests 5 Tests
CPU Massive 7 Tests
Creator Workloads 9 Tests
Database Test Suite 3 Tests
Encoding 5 Tests
HPC - High Performance Computing 3 Tests
Common Kernel Benchmarks 2 Tests
Machine Learning 3 Tests
Multi-Core 9 Tests
Intel oneAPI 2 Tests
Python Tests 3 Tests
Server 3 Tests
Server CPU Tests 5 Tests
Video Encoding 5 Tests

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Ryzen 9 7900
January 29 2023
  3 Hours, 58 Minutes
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January 31 2023
  3 Hours, 47 Minutes
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January 31 2023
  3 Hours, 46 Minutes
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Ryzen 9 7900 Suite 1.0.0 System Test suite extracted from Ryzen 9 7900. pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/kvazaar-1.2.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast pts/kvazaar-1.2.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast 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/svt-av1-2.7.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/svt-av1-2.7.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/svt-av1-2.7.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/svt-av1-2.7.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/svt-av1-2.7.0 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/svt-hevc-1.2.1 -encMode 1 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: 1 - Input: Bosphorus 4K pts/svt-hevc-1.2.1 -encMode 7 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: 7 - Input: Bosphorus 4K pts/svt-hevc-1.2.1 -encMode 10 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: 10 - Input: Bosphorus 4K pts/svt-hevc-1.2.1 -encMode 1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 1 - Input: Bosphorus 1080p pts/svt-hevc-1.2.1 -encMode 7 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 7 - Input: Bosphorus 1080p pts/svt-hevc-1.2.1 -encMode 10 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: 10 - Input: Bosphorus 1080p pts/svt-vp9-1.3.1 -tune 2 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: VMAF Optimized - Input: Bosphorus 4K pts/svt-vp9-1.3.1 -tune 2 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: VMAF Optimized - Input: Bosphorus 1080p pts/svt-vp9-1.3.1 -tune 1 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 4K pts/svt-vp9-1.3.1 -tune 1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: PSNR/SSIM Optimized - Input: Bosphorus 1080p pts/svt-vp9-1.3.1 -tune 0 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Tuning: Visual Quality Optimized - Input: Bosphorus 4K pts/svt-vp9-1.3.1 -tune 0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Tuning: Visual Quality Optimized - Input: Bosphorus 1080p pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast pts/compress-7zip-1.10.0 Test: Compression Rating pts/compress-7zip-1.10.0 Test: Decompression Rating 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_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - 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/onednn-3.0.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - 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/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 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --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_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.0.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/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/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/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_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_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - 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 --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 --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 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU 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 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.0.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - 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/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/clickhouse-1.2.0 100M Rows Hits Dataset, First Run / Cold Cache pts/clickhouse-1.2.0 100M Rows Hits Dataset, Second Run pts/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run pts/pgbench-1.13.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 1 -S Scaling Factor: 1 - Clients: 1 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 1 Scaling Factor: 1 - Clients: 1 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 100 -S Scaling Factor: 1 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 250 -S Scaling Factor: 1 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 500 -S Scaling Factor: 1 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 800 -S Scaling Factor: 1 - Clients: 800 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 800 -S Scaling Factor: 1 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 1 -S Scaling Factor: 100 - Clients: 1 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 100 Scaling Factor: 1 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 1000 -S Scaling Factor: 1 - Clients: 1000 - Mode: Read Only pts/pgbench-1.13.0 -s 1 -c 1000 -S Scaling Factor: 1 - Clients: 1000 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 250 Scaling Factor: 1 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 500 Scaling Factor: 1 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 1 -c 800 Scaling Factor: 1 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 800 Scaling Factor: 1 - Clients: 800 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1 Scaling Factor: 100 - Clients: 1 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 1 -c 1000 Scaling Factor: 1 - Clients: 1000 - Mode: Read Write pts/pgbench-1.13.0 -s 1 -c 1000 Scaling Factor: 1 - Clients: 1000 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 100 -S Scaling Factor: 100 - Clients: 100 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 250 -S Scaling Factor: 100 - Clients: 250 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 500 -S Scaling Factor: 100 - Clients: 500 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 800 -S Scaling Factor: 100 - Clients: 800 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 100 Scaling Factor: 100 - Clients: 100 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1000 -S Scaling Factor: 100 - Clients: 1000 - Mode: Read Only pts/pgbench-1.13.0 -s 100 -c 1000 -S Scaling Factor: 100 - Clients: 1000 - Mode: Read Only - Average Latency pts/pgbench-1.13.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 250 Scaling Factor: 100 - Clients: 250 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 500 Scaling Factor: 100 - Clients: 500 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 800 Scaling Factor: 100 - Clients: 800 - Mode: Read Write - Average Latency pts/pgbench-1.13.0 -s 100 -c 1000 Scaling Factor: 100 - Clients: 1000 - Mode: Read Write pts/pgbench-1.13.0 -s 100 -c 1000 Scaling Factor: 100 - Clients: 1000 - Mode: Read Write - Average Latency pts/deepsparse-1.3.2 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario sync Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario async Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario sync Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario sync Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario sync Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario sync Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --scenario sync Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.3.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/openvino-1.2.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU 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/openvino-1.2.0 -m models/intel/person-detection-0106/FP32/person-detection-0106.xml -d CPU Model: Person Detection FP32 - 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/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/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/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/openvino-1.2.0 -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/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/openvino-1.2.0 -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/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/openems-1.0.0 pyems/examples/coupler.py Test: pyEMS Coupler pts/openems-1.0.0 openEMS-Project/openEMS/python/Tutorials/MSL_NotchFilter.py Test: openEMS MSL_NotchFilter pts/rocksdb-1.4.0 --benchmarks="fillrandom" Test: Random Fill pts/rocksdb-1.4.0 --benchmarks="readrandom" Test: Random Read pts/rocksdb-1.4.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.4.0 --benchmarks="fillseq" Test: Sequential Fill pts/rocksdb-1.4.0 --benchmarks="fillsync" Test: Random Fill Sync pts/rocksdb-1.4.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.4.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/brl-cad-1.4.0 VGR Performance Metric