auggy
AMD Ryzen 5 4500U testing with a LENOVO LNVNB161216 (EECN20WW BIOS) and AMD Renoir 512MB on Pop 22.04 via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2308051-NE-AUGGY363552&grr&rdt.
Timed GCC Compilation
Time To Compile
Apache CouchDB
Bulk Size: 500 - Inserts: 3000 - Rounds: 30
BRL-CAD
VGR Performance Metric
Apache CouchDB
Bulk Size: 300 - Inserts: 3000 - Rounds: 30
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 100 - Sensor Count: 500
Apache CouchDB
Bulk Size: 100 - Inserts: 3000 - Rounds: 30
Apache CouchDB
Bulk Size: 500 - Inserts: 1000 - Rounds: 30
vkpeak
int16-vec4
vkpeak
int16-scalar
vkpeak
int32-vec4
vkpeak
int32-scalar
vkpeak
fp64-vec4
vkpeak
fp64-scalar
vkpeak
fp16-vec4
vkpeak
fp16-scalar
vkpeak
fp32-vec4
vkpeak
fp32-scalar
VVenC
Video Input: Bosphorus 4K - Video Preset: Fast
Apache CouchDB
Bulk Size: 300 - Inserts: 1000 - Rounds: 30
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
NCNN
Target: CPU - Model: FastestDet
NCNN
Target: CPU - Model: vision_transformer
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: yolov4-tiny
NCNN
Target: CPU - Model: resnet50
NCNN
Target: CPU - Model: alexnet
NCNN
Target: CPU - Model: resnet18
NCNN
Target: CPU - Model: vgg16
NCNN
Target: CPU - Model: googlenet
NCNN
Target: CPU - Model: blazeface
NCNN
Target: CPU - Model: efficientnet-b0
NCNN
Target: CPU - Model: mnasnet
NCNN
Target: CPU - Model: shufflenet-v2
NCNN
Target: CPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: CPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: CPU - Model: mobilenet
NCNN
Target: Vulkan GPU - Model: FastestDet
NCNN
Target: Vulkan GPU - Model: vision_transformer
NCNN
Target: Vulkan GPU - Model: regnety_400m
NCNN
Target: Vulkan GPU - Model: squeezenet_ssd
NCNN
Target: Vulkan GPU - Model: yolov4-tiny
NCNN
Target: Vulkan GPU - Model: resnet50
NCNN
Target: Vulkan GPU - Model: alexnet
NCNN
Target: Vulkan GPU - Model: resnet18
NCNN
Target: Vulkan GPU - Model: vgg16
NCNN
Target: Vulkan GPU - Model: googlenet
NCNN
Target: Vulkan GPU - Model: blazeface
NCNN
Target: Vulkan GPU - Model: efficientnet-b0
NCNN
Target: Vulkan GPU - Model: mnasnet
NCNN
Target: Vulkan GPU - Model: shufflenet-v2
NCNN
Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3
NCNN
Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2
NCNN
Target: Vulkan GPU - Model: mobilenet
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: 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
VVenC
Video Input: Bosphorus 4K - Video Preset: Faster
Apache CouchDB
Bulk Size: 100 - Inserts: 1000 - Rounds: 30
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: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 500
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
Apache Cassandra
Test: Writes
VVenC
Video Input: Bosphorus 1080p - Video Preset: Fast
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
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
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: 500
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 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: 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 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
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
Dragonflydb
Clients Per Thread: 60 - Set To Get Ratio: 1:5
Dragonflydb
Clients Per Thread: 60 - Set To Get Ratio: 1:100
Dragonflydb
Clients Per Thread: 50 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 50 - Set To Get Ratio: 1:5
Dragonflydb
Clients Per Thread: 50 - Set To Get Ratio: 1:100
Dragonflydb
Clients Per Thread: 60 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 20 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 20 - Set To Get Ratio: 1:100
Dragonflydb
Clients Per Thread: 10 - Set To Get Ratio: 1:10
Dragonflydb
Clients Per Thread: 10 - Set To Get Ratio: 1:100
Dragonflydb
Clients Per Thread: 20 - Set To Get Ratio: 1:5
Dragonflydb
Clients Per Thread: 10 - Set To Get Ratio: 1:5
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 500
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
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: 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 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 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
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: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Synchronous Single-Stream
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 100 - Sensor Count: 200
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: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: NLP Text Classification, DistilBERT mnli - Scenario: Synchronous Single-Stream
VVenC
Video Input: Bosphorus 1080p - Video Preset: Faster
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: 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: CV Detection, YOLOv5s COCO - 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: 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 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 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: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream
Neural Magic DeepSparse
Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-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: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
Neural Magic DeepSparse
Model: ResNet-50, Sparse INT8 - Scenario: Synchronous Single-Stream
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 500 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 100 - Sensor Count: 200
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 200 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 500
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
Apache IoTDB
Device Count: 100 - Batch Size Per Write: 1 - Sensor Count: 200
VkResample
Upscale: 2x - Precision: Single
Phoronix Test Suite v10.8.5