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.

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Result
Identifier
View Logs
Performance Per
Dollar
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 AMD EPYC 7763 64-Core testing with a AMD DAYTONA_X (RYM1009B BIOS) and ASPEED on Ubuntu 22.04 via the Phoronix Test Suite. ,,"AMD EPYC 7763 64-Core - ASPEED - AMD DAYTONA_X" Processor,,AMD EPYC 7763 64-Core @ 2.45GHz (64 Cores / 128 Threads) Motherboard,,AMD DAYTONA_X (RYM1009B BIOS) Chipset,,AMD Starship/Matisse Memory,,256GB Disk,,3841GB Micron_9300_MTFDHAL3T8TDP Graphics,,ASPEED Monitor,,VE228 Network,,2 x Mellanox MT27710 OS,,Ubuntu 22.04 Kernel,,5.15.0-47-generic (x86_64) Desktop,,GNOME Shell 42.4 Display Server,,X Server 1.21.1.3 Vulkan,,1.2.204 Compiler,,GCC 11.2.0 File-System,,ext4 Screen Resolution,,1920x1080 ,,"AMD EPYC 7763 64-Core - ASPEED - AMD DAYTONA_X" "Stress-NG - Test: Hash (Bogo Ops/s)",HIB,11144840.42 "Stress-NG - Test: MMAP (Bogo Ops/s)",HIB,1233.47 "Stress-NG - Test: NUMA (Bogo Ops/s)",HIB,291.55 "Stress-NG - Test: Pipe (Bogo Ops/s)",HIB,37830030.68 "Stress-NG - Test: Poll (Bogo Ops/s)",HIB,7893061.95 "Stress-NG - Test: Zlib (Bogo Ops/s)",HIB,6210.36 "Stress-NG - Test: Futex (Bogo Ops/s)",HIB,2343496.22 "Stress-NG - Test: MEMFD (Bogo Ops/s)",HIB,437.04 "Stress-NG - Test: Mutex (Bogo Ops/s)",HIB,36913960.47 "Stress-NG - Test: Atomic (Bogo Ops/s)",HIB,206.31 "Stress-NG - Test: Crypto (Bogo Ops/s)",HIB,123450.01 "Stress-NG - Test: Malloc (Bogo Ops/s)",HIB,186568211.34 "Stress-NG - Test: Cloning (Bogo Ops/s)",HIB,7463.78 "Stress-NG - Test: Forking (Bogo Ops/s)",HIB,61808.18 "Stress-NG - Test: Pthread (Bogo Ops/s)",HIB,108230.65 "Stress-NG - Test: AVL Tree (Bogo Ops/s)",HIB,558.18 "Stress-NG - Test: IO_uring (Bogo Ops/s)",HIB,6295021.37 "Stress-NG - Test: SENDFILE (Bogo Ops/s)",HIB,839871.86 "Stress-NG - Test: CPU Cache (Bogo Ops/s)",HIB,1414607.05 "Stress-NG - Test: CPU Stress (Bogo Ops/s)",HIB,128172.34 "Stress-NG - Test: Semaphores (Bogo Ops/s)",HIB,140637012.03 "Stress-NG - Test: Matrix Math (Bogo Ops/s)",HIB,223038.42 "Stress-NG - Test: Vector Math (Bogo Ops/s)",HIB,341516.25 "Stress-NG - Test: AVX-512 VNNI (Bogo Ops/s)",HIB,3797236.44 "Stress-NG - Test: Function Call (Bogo Ops/s)",HIB,38065.86 "Stress-NG - Test: x86_64 RdRand (Bogo Ops/s)",HIB,17319858.87 "Stress-NG - Test: Floating Point (Bogo Ops/s)",HIB,17129.98 "Stress-NG - Test: Matrix 3D Math (Bogo Ops/s)",HIB,5709 "Stress-NG - Test: Memory Copying (Bogo Ops/s)",HIB,13220.97 "Stress-NG - Test: Vector Shuffle (Bogo Ops/s)",HIB,36503.49 "Stress-NG - Test: Mixed Scheduler (Bogo Ops/s)",HIB,53645.16 "Stress-NG - Test: Socket Activity (Bogo Ops/s)",HIB,9049.41 "Stress-NG - Test: Wide Vector Math (Bogo Ops/s)",HIB,2104650.98 "Stress-NG - Test: Context Switching (Bogo Ops/s)",HIB,7733688.1 "Stress-NG - Test: Fused Multiply-Add (Bogo Ops/s)",HIB,49433147.11 "Stress-NG - Test: Vector Floating Point (Bogo Ops/s)",HIB,156275.9 "Stress-NG - Test: Glibc C String Functions (Bogo Ops/s)",HIB,45614430.99 "Stress-NG - Test: Glibc Qsort Data Sorting (Bogo Ops/s)",HIB,1272.64 "Stress-NG - Test: System V Message Passing (Bogo Ops/s)",HIB,8956860.19 "BRL-CAD - VGR Performance Metric (VGR Performance Metric)",HIB,735105 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,37.5977 "Neural Magic DeepSparse - Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,842.2719 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,1102.8786 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,28.9855 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,487.3574 "Neural Magic DeepSparse - Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,65.6178 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,143.9746 "Neural Magic DeepSparse - Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,222.0683 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,467.5086 "Neural Magic DeepSparse - Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,68.3081 "Neural Magic DeepSparse - Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB, "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,224.7545 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,142.0216 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,46.4849 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,681.4891 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,467.0794 "Neural Magic DeepSparse - Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,68.4423 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,228.638 "Neural Magic DeepSparse - Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,139.5721 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,328.0733 "Neural Magic DeepSparse - Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,97.3098 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,53.6599 "Neural Magic DeepSparse - Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,596.0641 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,575.436 "Neural Magic DeepSparse - Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,55.529 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,166.699 "Neural Magic DeepSparse - Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,191.4891 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (items/sec)",HIB,37.6277 "Neural Magic DeepSparse - Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream (ms/batch)",LIB,838.0698 "NCNN - Target: CPU - Model: mobilenet (ms)",LIB,14.09 "NCNN - Target: CPU-v2-v2 - Model: mobilenet-v2 (ms)",LIB,6.3 "NCNN - Target: CPU-v3-v3 - Model: mobilenet-v3 (ms)",LIB,6.94 "NCNN - Target: CPU - Model: shufflenet-v2 (ms)",LIB,8.88 "NCNN - Target: CPU - Model: mnasnet (ms)",LIB,5.87 "NCNN - Target: CPU - Model: efficientnet-b0 (ms)",LIB,9.96 "NCNN - Target: CPU - Model: blazeface (ms)",LIB,4.92 "NCNN - Target: CPU - Model: googlenet (ms)",LIB,14.39 "NCNN - Target: CPU - Model: vgg16 (ms)",LIB,23.78 "NCNN - Target: CPU - Model: resnet18 (ms)",LIB,8.57 "NCNN - Target: CPU - Model: alexnet (ms)",LIB,5.25 "NCNN - Target: CPU - Model: resnet50 (ms)",LIB,15.48 "NCNN - Target: CPU - Model: yolov4-tiny (ms)",LIB,21.04 "NCNN - Target: CPU - Model: squeezenet_ssd (ms)",LIB,13.92 "NCNN - Target: CPU - Model: regnety_400m (ms)",LIB,36.69 "NCNN - Target: CPU - Model: vision_transformer (ms)",LIB,47.5 "NCNN - Target: CPU - Model: FastestDet (ms)",LIB,11.87 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,2233880.23 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:5 (Ops/sec)",HIB,2318759.53 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 50 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,2223166.07 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:5 ()",, "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 100 - Set To Get Ratio: 1:10 (Ops/sec)",HIB,2450822.55 "Redis 7.0.12 + memtier_benchmark - Protocol: Redis - Clients: 500 - Set To Get Ratio: 1:10 ()",, "Apache Cassandra - Test: Writes (Op/s)",HIB,227576