eee

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 2304025-NE-EEE49872408
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Timed Code Compilation 5 Tests
C/C++ Compiler Tests 9 Tests
CPU Massive 11 Tests
Creator Workloads 9 Tests
Cryptography 2 Tests
Database Test Suite 4 Tests
Encoding 4 Tests
Game Development 3 Tests
HPC - High Performance Computing 3 Tests
Common Kernel Benchmarks 4 Tests
Machine Learning 3 Tests
Multi-Core 15 Tests
Intel oneAPI 2 Tests
Programmer / Developer System Benchmarks 6 Tests
Python Tests 6 Tests
Server 7 Tests
Server CPU Tests 7 Tests
Video Encoding 4 Tests

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April 01 2023
  6 Hours, 3 Minutes
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April 01 2023
  3 Hours, 58 Minutes
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April 01 2023
  6 Hours, 7 Minutes
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eee Suite 1.0.0 System Test suite extracted from eee. pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU 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 50 Scaling Factor: 100 - Clients: 50 - Mode: Read Write pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/memcached-1.2.0 --ratio=1:100 Set To Get Ratio: 1:100 pts/memcached-1.2.0 --ratio=1:5 Set To Get Ratio: 1:5 pts/memcached-1.2.0 --ratio=1:10 Set To Get Ratio: 1:10 pts/compress-zstd-1.6.0 -b3 --long Compression Level: 3, Long Mode - Compression Speed pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/ffmpeg-6.0.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/rocksdb-1.5.0 --benchmarks="fillsync" Test: Random Fill Sync pts/john-the-ripper-1.8.0 --format=HMAC-SHA512 Test: HMAC-SHA512 pts/svt-av1-2.7.2 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/compress-zstd-1.6.0 -b8 Compression Level: 8 - Compression Speed 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/compress-zstd-1.6.0 -b19 --long Compression Level: 19, Long Mode - Compression Speed pts/compress-zstd-1.6.0 -b12 Compression Level: 12 - Compression Speed pts/compress-zstd-1.6.0 -b3 Compression Level: 3 - Compression Speed pts/build-llvm-1.5.0 Build System: Unix Makefiles pts/rocksdb-1.5.0 --benchmarks="fillseq" Test: Sequential Fill pts/svt-av1-2.7.2 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/compress-zstd-1.6.0 -b8 --long Compression Level: 8, Long Mode - Compression Speed pts/rocksdb-1.5.0 --benchmarks="fillrandom" Test: Random Fill 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/svt-av1-2.7.2 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/clickhouse-1.2.0 100M Rows Hits Dataset, Third Run pts/vvenc-1.1.0 -i Bosphorus_3840x2160.y4m --preset fast Video Input: Bosphorus 4K - Video Preset: Fast 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/specfem3d-1.0.0 Mount_StHelens Model: Mount St. Helens pts/rocksdb-1.5.0 --benchmarks="updaterandom" Test: Update Random pts/svt-av1-2.7.2 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/specfem3d-1.0.0 waterlayered_halfspace Model: Water-layered Halfspace 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/build2-1.2.0 Time To Compile 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/onednn-3.1.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/svt-av1-2.7.2 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/pgbench-1.13.0 -s 1 -c 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only pts/nginx-3.0.1 -c 500 Connections: 500 pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset ultrafast Video Input: Bosphorus 4K - Video Preset: Ultra Fast pts/nginx-3.0.1 -c 200 Connections: 200 pts/vvenc-1.1.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset fast Video Input: Bosphorus 1080p - Video Preset: Fast pts/svt-av1-2.7.2 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset veryfast Video Input: Bosphorus 1080p - Video Preset: Very Fast pts/specfem3d-1.0.0 tomographic_model Model: Tomographic Model pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/vvenc-1.1.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset faster Video Input: Bosphorus 1080p - Video Preset: Faster pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset superfast Video Input: Bosphorus 1080p - Video Preset: Super Fast 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/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset superfast Video Input: Bosphorus 4K - Video Preset: Super Fast pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=googlenet Device: CPU - Batch Size: 256 - Model: GoogLeNet pts/build-llvm-1.5.0 Ninja Build System: Ninja pts/specfem3d-1.0.0 layered_halfspace Model: Layered Halfspace pts/ffmpeg-6.0.0 --encoder=libx264 live Encoder: libx264 - Scenario: Live pts/ffmpeg-6.0.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/openssl-3.1.0 sha256 Algorithm: SHA256 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/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/openssl-3.1.0 rsa4096 Algorithm: RSA4096 pts/rocksdb-1.5.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/specfem3d-1.0.0 homogeneous_halfspace Model: Homogeneous Halfspace pts/compress-zstd-1.6.0 -b19 Compression Level: 19 - Decompression Speed pts/compress-zstd-1.6.0 -b19 --long Compression Level: 19, Long Mode - Decompression Speed pts/build-godot-4.0.0 Time To Compile pts/vvenc-1.1.0 -i Bosphorus_3840x2160.y4m --preset faster Video Input: Bosphorus 4K - Video Preset: Faster pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset slow Video Input: Bosphorus 1080p - Video Preset: Slow pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/build-ffmpeg-6.0.0 Time To Compile pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU 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/compress-zstd-1.6.0 -b12 Compression Level: 12 - Decompression Speed pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset medium Video Input: Bosphorus 1080p - Video Preset: Medium pts/embree-1.4.0 pathtracer -c crown/crown.ecs Binary: Pathtracer - Model: Crown pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet 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/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/svt-av1-2.7.2 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/compress-zstd-1.6.0 -b19 Compression Level: 19 - Compression Speed pts/john-the-ripper-1.8.0 --format=bcrypt Test: Blowfish pts/rocksdb-1.5.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/compress-zstd-1.6.0 -b8 --long Compression Level: 8, Long Mode - Decompression Speed pts/embree-1.4.0 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/compress-zstd-1.6.0 -b3 Compression Level: 3 - Decompression Speed pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=resnet50 Device: CPU - Batch Size: 256 - Model: ResNet-50 pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/compress-zstd-1.6.0 -b3 --long Compression Level: 3, Long Mode - Decompression Speed pts/ffmpeg-6.0.0 --encoder=libx264 vod Encoder: libx264 - Scenario: Video On Demand pts/john-the-ripper-1.8.0 --format=wpapsk Test: WPA PSK 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/tensorflow-2.1.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/john-the-ripper-1.8.0 --format=md5crypt Test: MD5 pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/nginx-3.0.1 -c 1000 Connections: 1000 pts/blender-3.5.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/embree-1.4.0 pathtracer -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon Obj pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset slow Video Input: Bosphorus 4K - Video Preset: Slow pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=resnet50 Device: CPU - Batch Size: 512 - Model: ResNet-50 pts/ffmpeg-6.0.0 --encoder=libx264 upload Encoder: libx264 - Scenario: Upload pts/uvg266-1.0.0 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset ultrafast Video Input: Bosphorus 1080p - Video Preset: Ultra Fast pts/openssl-3.1.0 sha512 Algorithm: SHA512 pts/svt-av1-2.7.2 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/embree-1.4.0 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=googlenet Device: CPU - Batch Size: 512 - Model: GoogLeNet pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/embree-1.4.0 pathtracer -c asian_dragon/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon pts/pgbench-1.13.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write - Average Latency pts/openssl-3.1.0 -evp aes-128-gcm Algorithm: AES-128-GCM pts/compress-zstd-1.6.0 -b8 Compression Level: 8 - Decompression Speed pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset veryfast Video Input: Bosphorus 4K - Video Preset: Very Fast pts/tensorflow-2.1.0 --device cpu --batch_size=256 --model=alexnet Device: CPU - Batch Size: 256 - Model: AlexNet pts/onednn-3.1.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=512 --model=alexnet Device: CPU - Batch Size: 512 - Model: AlexNet pts/pgbench-1.13.0 -s 1 -c 50 Scaling Factor: 1 - Clients: 50 - Mode: Read Write 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/ffmpeg-6.0.0 --encoder=libx264 platform Encoder: libx264 - Scenario: Platform pts/ffmpeg-6.0.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/embree-1.4.0 pathtracer_ispc -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon Obj pts/ffmpeg-6.0.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/pgbench-1.13.0 -s 100 -c 50 -S Scaling Factor: 100 - Clients: 50 - Mode: Read Only pts/uvg266-1.0.0 -i Bosphorus_3840x2160.y4m --preset medium Video Input: Bosphorus 4K - Video Preset: Medium pts/rocksdb-1.5.0 --benchmarks="readrandom" Test: Random Read pts/tensorflow-2.1.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/draco-1.6.0 -i church.ply Model: Church Facade pts/nginx-3.0.1 -c 100 Connections: 100 pts/openssl-3.1.0 -evp aes-256-gcm Algorithm: AES-256-GCM pts/draco-1.6.0 -i lion.ply Model: Lion pts/deepsparse-1.3.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario sync Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream pts/deepsparse-1.3.2 zoo:nlp/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/blender-3.5.0 -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/blender-3.5.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/blender-3.5.0 -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-1.3.2 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/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/tensorflow-2.1.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/onednn-3.1.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/john-the-ripper-1.8.0 --format=bcrypt Test: bcrypt pts/blender-3.5.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/build-nodejs-1.3.0 Time To Compile pts/tensorflow-2.1.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/onednn-3.1.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.1.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU 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/openssl-3.1.0 -evp chacha20 Algorithm: ChaCha20 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/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/openssl-3.1.0 -evp chacha20-poly1305 Algorithm: ChaCha20-Poly1305 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 50 -S Scaling Factor: 1 - Clients: 50 - Mode: Read Only - Average Latency pts/apache-3.0.0 -c 1000 Concurrent Requests: 1000 pts/apache-3.0.0 -c 500 Concurrent Requests: 500 pts/apache-3.0.0 -c 200 Concurrent Requests: 200 pts/apache-3.0.0 -c 100 Concurrent Requests: 100