8490h 1s

Intel Xeon Platinum 8490H testing with a Quanta Cloud S6Q-MB-MPS (3A10.uh BIOS) and ASPEED 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 2307296-NE-8490H1S1663
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CPU Massive 2 Tests
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Database Test Suite 3 Tests
Multi-Core 2 Tests
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July 28 2023
  1 Hour, 52 Minutes
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July 28 2023
  2 Hours, 53 Minutes
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July 28 2023
  1 Hour, 26 Minutes
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July 28 2023
  1 Hour, 25 Minutes
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July 29 2023
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8490h 1s Suite 1.0.0 System Test suite extracted from 8490h 1s. pts/brl-cad-1.5.0 VGR Performance Metric pts/cryptopp-1.1.0 b 2 Test: All Algorithms pts/cryptopp-1.1.0 b2 3 Test: Keyed Algorithms pts/blender-3.6.0 -b ../barbershop_interior_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Barbershop - Compute: CPU-Only pts/cryptopp-1.1.0 b1 6 Test: Unkeyed Algorithms pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream pts/cassandra-1.2.0 WRITE Test: Writes pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario sync Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario sync Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream pts/dragonflydb-1.1.0 -c 10 --ratio=1:5 Clients Per Thread: 10 - Set To Get Ratio: 1:5 pts/dragonflydb-1.1.0 -c 10 --ratio=1:100 Clients Per Thread: 10 - Set To Get Ratio: 1:100 pts/dragonflydb-1.1.0 -c 10 --ratio=1:10 Clients Per Thread: 10 - Set To Get Ratio: 1:10 pts/blender-3.6.0 -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/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario sync Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 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-1.5.2 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 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/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:10 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 pts/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:5 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 pts/memtier-benchmark-1.5.0 -P redis -c 100 --ratio=1:1 Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:1 pts/blender-3.6.0 -b ../classroom_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Classroom - Compute: CPU-Only pts/deepsparse-1.5.2 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/memtier-benchmark-1.5.0 -P redis -c 50 --ratio=1:5 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 pts/memtier-benchmark-1.5.0 -P redis -c 50 --ratio=1:10 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 pts/memtier-benchmark-1.5.0 -P redis -c 50 --ratio=1:1 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:1 pts/deepsparse-1.5.2 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-1.5.2 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-1.5.2 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-1.5.2 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-1.5.2 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-1.5.2 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-1.5.2 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-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario sync Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario sync Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario sync Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario sync Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 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-1.5.2 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-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario async Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 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-1.5.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --input_shapes='[1,128]' --scenario sync Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream pts/deepsparse-1.5.2 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/blender-3.6.0 -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/blender-3.6.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/memtier-benchmark-1.5.0 -P redis -c 500 --ratio=1:5 Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5 pts/memtier-benchmark-1.5.0 -P redis -c 500 --ratio=1:1 Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:1 pts/memtier-benchmark-1.5.0 -P redis -c 500 --ratio=1:10 Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10 pts/dragonflydb-1.1.0 -c 20 --ratio=1:5 Clients Per Thread: 20 - Set To Get Ratio: 1:5 pts/dragonflydb-1.1.0 -c 20 --ratio=1:10 Clients Per Thread: 20 - Set To Get Ratio: 1:10 pts/dragonflydb-1.1.0 -c 20 --ratio=1:100 Clients Per Thread: 20 - Set To Get Ratio: 1:100