stress extra

AMD EPYC 7763 64-Core testing with a AMD DAYTONA_X (RYM1009B 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 2308279-NE-STRESSEXT05
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Result
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Performance Per
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Date
Run
  Test
  Duration
AMD EPYC 7763 64-Core - ASPEED - AMD DAYTONA_X
August 27 2023
  1 Hour, 35 Minutes
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stress extra Suite 1.0.0 System Test suite extracted from stress extra. pts/brl-cad-1.5.0 VGR Performance Metric pts/cassandra-1.2.0 WRITE Test: Writes pts/ncnn-1.5.0 -1 Target: CPU - Model: FastestDet pts/ncnn-1.5.0 -1 Target: CPU - Model: vision_transformer pts/ncnn-1.5.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.5.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.5.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.5.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.5.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.5.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.5.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.5.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.5.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.5.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.5.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.5.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.5.0 -1 Target: CPU - Model: mobilenet pts/stress-ng-1.11.0 --msg -1 --no-rand-seed Test: System V Message Passing pts/stress-ng-1.11.0 --qsort -1 --no-rand-seed Test: Glibc Qsort Data Sorting pts/stress-ng-1.11.0 --str -1 --no-rand-seed Test: Glibc C String Functions pts/stress-ng-1.11.0 --vecfp -1 --no-rand-seed Test: Vector Floating Point pts/stress-ng-1.11.0 --fma -1 --no-rand-seed Test: Fused Multiply-Add pts/stress-ng-1.11.0 --switch -1 --no-rand-seed Test: Context Switching pts/stress-ng-1.11.0 --vecwide -1 --no-rand-seed Test: Wide Vector Math pts/stress-ng-1.11.0 --sock -1 --no-rand-seed --sock-zerocopy Test: Socket Activity pts/stress-ng-1.11.0 --schedmix -1 Test: Mixed Scheduler pts/stress-ng-1.11.0 --vecshuf -1 --no-rand-seed Test: Vector Shuffle pts/stress-ng-1.11.0 --memcpy -1 --no-rand-seed Test: Memory Copying pts/stress-ng-1.11.0 --matrix-3d -1 --no-rand-seed Test: Matrix 3D Math pts/stress-ng-1.11.0 --fp -1 --no-rand-seed Test: Floating Point pts/stress-ng-1.11.0 --rdrand -1 --no-rand-seed Test: x86_64 RdRand pts/stress-ng-1.11.0 --funccall -1 --no-rand-seed Test: Function Call pts/stress-ng-1.11.0 --vnni -1 Test: AVX-512 VNNI pts/stress-ng-1.11.0 --vecmath -1 --no-rand-seed Test: Vector Math pts/stress-ng-1.11.0 --matrix -1 --no-rand-seed Test: Matrix Math pts/stress-ng-1.11.0 --sem -1 --no-rand-seed Test: Semaphores pts/stress-ng-1.11.0 --cpu -1 --cpu-method all --no-rand-seed Test: CPU Stress pts/stress-ng-1.11.0 --cache -1 --no-rand-seed Test: CPU Cache pts/stress-ng-1.11.0 --sendfile -1 --no-rand-seed Test: SENDFILE pts/stress-ng-1.11.0 --io-uring -1 --no-rand-seed Test: IO_uring pts/stress-ng-1.11.0 --tree -1 --tree-method avl --no-rand-seed Test: AVL Tree pts/stress-ng-1.11.0 --pthread -1 --no-rand-seed Test: Pthread pts/stress-ng-1.11.0 --fork -1 --no-rand-seed Test: Forking pts/stress-ng-1.11.0 --clone -1 --no-rand-seed Test: Cloning pts/stress-ng-1.11.0 --malloc -1 --no-rand-seed Test: Malloc pts/stress-ng-1.11.0 --crypt -1 --no-rand-seed Test: Crypto pts/stress-ng-1.11.0 --atomic -1 --no-rand-seed Test: Atomic pts/stress-ng-1.11.0 --mutex -1 --no-rand-seed Test: Mutex pts/stress-ng-1.11.0 --memfd -1 --no-rand-seed Test: MEMFD pts/stress-ng-1.11.0 --futex -1 --no-rand-seed Test: Futex pts/stress-ng-1.11.0 --zlib -1 --no-rand-seed Test: Zlib pts/stress-ng-1.11.0 --poll -1 --no-rand-seed Test: Poll pts/stress-ng-1.11.0 --pipe -1 --no-rand-seed Test: Pipe pts/stress-ng-1.11.0 --numa -1 --no-rand-seed Test: NUMA pts/stress-ng-1.11.0 --mmap -1 --no-rand-seed Test: MMAP pts/stress-ng-1.11.0 --hash -1 --no-rand-seed Test: Hash 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 50 --ratio=1:10 Protocol: Redis - Clients: 50 - 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 50 --ratio=1:5 Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 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 async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream 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: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/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: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/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: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/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/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: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-1.5.2 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: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 500 --ratio=1:10 Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10 pts/memtier-benchmark-1.5.0 -P redis -c 500 --ratio=1:5 Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5 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