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.

HTML result view exported from: https://openbenchmarking.org/result/2308279-NE-STRESSEXT05&grw.

Stress-NG

Test: Hash

Stress-NG

Test: MMAP

Stress-NG

Test: NUMA

Stress-NG

Test: Pipe

Stress-NG

Test: Poll

Stress-NG

Test: Zlib

Stress-NG

Test: Futex

Stress-NG

Test: MEMFD

Stress-NG

Test: Mutex

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Test: Atomic

Stress-NG

Test: Crypto

Stress-NG

Test: Malloc

Stress-NG

Test: Cloning

Stress-NG

Test: Forking

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Test: Pthread

Stress-NG

Test: AVL Tree

Stress-NG

Test: IO_uring

Stress-NG

Test: SENDFILE

Stress-NG

Test: CPU Cache

Stress-NG

Test: CPU Stress

Stress-NG

Test: Semaphores

Stress-NG

Test: Matrix Math

Stress-NG

Test: Vector Math

Stress-NG

Test: AVX-512 VNNI

Stress-NG

Test: Function Call

Stress-NG

Test: x86_64 RdRand

Stress-NG

Test: Floating Point

Stress-NG

Test: Matrix 3D Math

Stress-NG

Test: Memory Copying

Stress-NG

Test: Vector Shuffle

Stress-NG

Test: Mixed Scheduler

Stress-NG

Test: Socket Activity

Stress-NG

Test: Wide Vector Math

Stress-NG

Test: Context Switching

Stress-NG

Test: Fused Multiply-Add

Stress-NG

Test: Vector Floating Point

Stress-NG

Test: Glibc C String Functions

Stress-NG

Test: Glibc Qsort Data Sorting

Stress-NG

Test: System V Message Passing

BRL-CAD

VGR Performance Metric

Neural Magic DeepSparse

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

Neural Magic DeepSparse

Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream

NCNN

Target: CPU - Model: mobilenet

NCNN

Target: CPU-v2-v2 - Model: mobilenet-v2

NCNN

Target: CPU-v3-v3 - Model: mobilenet-v3

NCNN

Target: CPU - Model: shufflenet-v2

NCNN

Target: CPU - Model: mnasnet

NCNN

Target: CPU - Model: efficientnet-b0

NCNN

Target: CPU - Model: blazeface

NCNN

Target: CPU - Model: googlenet

NCNN

Target: CPU - Model: vgg16

NCNN

Target: CPU - Model: resnet18

NCNN

Target: CPU - Model: alexnet

NCNN

Target: CPU - Model: resnet50

NCNN

Target: CPU - Model: yolov4-tiny

NCNN

Target: CPU - Model: squeezenet_ssd

NCNN

Target: CPU - Model: regnety_400m

NCNN

Target: CPU - Model: vision_transformer

NCNN

Target: CPU - Model: FastestDet

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10

Redis 7.0.12 + memtier_benchmark

Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10

Apache Cassandra

Test: Writes

Phoronix Test Suite v10.8.5