7763 2204
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/2308059-NE-77632204529&grs&rdt.
NCNN
Target: CPU - Model: regnety_400m
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
NCNN
Target: CPU - Model: shufflenet-v2
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
NCNN
Target: CPU - Model: blazeface
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
NCNN
Target: CPU - Model: FastestDet
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
srsRAN Project
Test: Downlink Processor Benchmark
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
NCNN
Target: CPU - Model: mnasnet
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
NCNN
Target: CPU - Model: squeezenet_ssd
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
NCNN
Target: CPU - Model: efficientnet-b0
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
VVenC
Video Input: Bosphorus 4K - Video Preset: Faster
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
NCNN
Target: CPU - Model: yolov4-tiny
Apache Cassandra
Test: Writes
srsRAN Project
Test: PUSCH Processor Benchmark, Throughput Thread
NCNN
Target: CPU - Model: resnet50
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
NCNN
Target: CPU - Model: vgg16
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Synchronous Single-Stream
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: mobilenet
Blender
Blend File: BMW27 - Compute: CPU-Only
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
NCNN
Target: CPU - Model: vision_transformer
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream
BRL-CAD
VGR Performance Metric
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Synchronous Single-Stream
srsRAN Project
Test: PUSCH Processor Benchmark, Throughput Total
Blender
Blend File: Pabellon Barcelona - Compute: CPU-Only
Blender
Blend File: Classroom - Compute: CPU-Only
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Synchronous Single-Stream
VVenC
Video Input: Bosphorus 1080p - Video Preset: Faster
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream
VVenC
Video Input: Bosphorus 4K - Video Preset: Fast
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - 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: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream
VVenC
Video Input: Bosphorus 1080p - Video Preset: Fast
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: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
NCNN
Target: CPU - Model: alexnet
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Blender
Blend File: Fishy Cat - Compute: CPU-Only
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - 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: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Detection, YOLOv5s COCO - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Blender
Blend File: Barbershop - Compute: CPU-Only
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Document Classification, oBERT base uncased on IMDB - 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: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Timed GCC Compilation
Time To Compile
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream
Apache CouchDB
Bulk Size: 500 - Inserts: 3000 - Rounds: 30
Apache CouchDB
Bulk Size: 500 - Inserts: 1000 - Rounds: 30
Apache CouchDB
Bulk Size: 300 - Inserts: 3000 - Rounds: 30
Apache CouchDB
Bulk Size: 300 - Inserts: 1000 - Rounds: 30
Apache CouchDB
Bulk Size: 100 - Inserts: 3000 - Rounds: 30
Apache CouchDB
Bulk Size: 100 - Inserts: 1000 - Rounds: 30
Phoronix Test Suite v10.8.5