AMD Ryzen Non-X benchmarks new cpus

Benchmarks for a future article on Phoronix.

HTML result view exported from: https://openbenchmarking.org/result/2301096-PTS-EO2022BE72&grw&sro.

BRL-CAD

VGR Performance Metric

Stargate Digital Audio Workstation

Sample Rate: 44100 - Buffer Size: 512

Stargate Digital Audio Workstation

Sample Rate: 96000 - Buffer Size: 512

Stargate Digital Audio Workstation

Sample Rate: 192000 - Buffer Size: 512

Stargate Digital Audio Workstation

Sample Rate: 44100 - Buffer Size: 1024

Stargate Digital Audio Workstation

Sample Rate: 480000 - Buffer Size: 512

Stargate Digital Audio Workstation

Sample Rate: 96000 - Buffer Size: 1024

Stargate Digital Audio Workstation

Sample Rate: 192000 - Buffer Size: 1024

Stargate Digital Audio Workstation

Sample Rate: 480000 - Buffer Size: 1024

JPEG XL Decoding libjxl

CPU Threads: 1

JPEG XL Decoding libjxl

CPU Threads: All

JPEG XL libjxl

Input: PNG - Quality: 80

JPEG XL libjxl

Input: PNG - Quality: 90

JPEG XL libjxl

Input: JPEG - Quality: 80

JPEG XL libjxl

Input: JPEG - Quality: 90

JPEG XL libjxl

Input: PNG - Quality: 100

JPEG XL libjxl

Input: JPEG - Quality: 100

Xmrig

Variant: Monero - Hash Count: 1M

Xmrig

Variant: Wownero - Hash Count: 1M

miniBUDE

Implementation: OpenMP - Input Deck: BM1

miniBUDE

Implementation: OpenMP - Input Deck: BM1

nekRS

Input: TurboPipe Periodic

OpenRadioss

Model: Bumper Beam

OpenRadioss

Model: Cell Phone Drop Test

OpenRadioss

Model: Bird Strike on Windshield

OpenRadioss

Model: Rubber O-Ring Seal Installation

OpenRadioss

Model: INIVOL and Fluid Structure Interaction Drop Container

TensorFlow

Device: CPU - Batch Size: 16 - Model: AlexNet

TensorFlow

Device: CPU - Batch Size: 32 - Model: AlexNet

TensorFlow

Device: CPU - Batch Size: 64 - Model: AlexNet

TensorFlow

Device: CPU - Batch Size: 16 - Model: GoogLeNet

TensorFlow

Device: CPU - Batch Size: 16 - Model: ResNet-50

TensorFlow

Device: CPU - Batch Size: 32 - Model: GoogLeNet

TensorFlow

Device: CPU - Batch Size: 32 - Model: ResNet-50

TensorFlow

Device: CPU - Batch Size: 64 - Model: GoogLeNet

TensorFlow

Device: CPU - Batch Size: 64 - Model: ResNet-50

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 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 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 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 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: 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

Y-Cruncher

Pi Digits To Calculate: 500M

Y-Cruncher

Pi Digits To Calculate: 1B

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

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 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 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 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

spaCy

Model: en_core_web_lg

spaCy

Model: en_core_web_trf

oneDNN

Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU

oneDNN

Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU

oneDNN

Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU

oneDNN

Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU

oneDNN

Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU

oneDNN

Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU

oneDNN

Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU

oneDNN

Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: f32 - Engine: CPU

oneDNN

Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU

oneDNN

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: u8s8f32 - Engine: CPU

oneDNN

Harness: Matrix Multiply Batch Shapes Transformer - Data Type: bf16bf16bf16 - Engine: CPU

OpenVINO

Model: Face Detection FP16 - Device: CPU

OpenVINO

Model: Face Detection FP16 - Device: CPU

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Person Detection FP16 - Device: CPU

OpenVINO

Model: Person Detection FP32 - Device: CPU

OpenVINO

Model: Person Detection FP32 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16 - Device: CPU

OpenVINO

Model: Vehicle Detection FP16 - Device: CPU