AMD 3D V-Cache Comparison

Tests for a future article.

HTML result view exported from: https://openbenchmarking.org/result/2204299-NE-CC771232156&grr.

OpenFOAM

Input: Motorbike 60M

Open Porous Media Git

OPM Benchmark: Flow MPI Extra - Threads: 1

LeelaChessZero

Backend: Eigen

Open Porous Media Git

OPM Benchmark: Flow MPI Extra - Threads: 8

Open Porous Media Git

OPM Benchmark: Flow MPI Extra - Threads: 2

Open Porous Media Git

OPM Benchmark: Flow MPI Extra - Threads: 4

WebP2 Image Encode

Encode Settings: Quality 100, Lossless Compression

Open Porous Media Git

OPM Benchmark: Flow MPI Norne-4C MSW - Threads: 8

Open Porous Media Git

OPM Benchmark: Flow MPI Norne-4C MSW - Threads: 1

Open Porous Media Git

OPM Benchmark: Flow MPI Norne-4C MSW - Threads: 2

Open Porous Media Git

OPM Benchmark: Flow MPI Norne - Threads: 8

Open Porous Media Git

OPM Benchmark: Flow MPI Norne-4C MSW - Threads: 4

ONNX Runtime

Model: yolov4 - Device: CPU - Executor: Standard

LeelaChessZero

Backend: BLAS

ECP-CANDLE

Benchmark: P3B1

WebP2 Image Encode

Encode Settings: Quality 95, Compression Effort 7

Open Porous Media Git

OPM Benchmark: Flow MPI Norne - Threads: 1

Open Porous Media Git

OPM Benchmark: Flow MPI Norne - Threads: 4

ECP-CANDLE

Benchmark: P3B2

ONNX Runtime

Model: ArcFace ResNet-100 - Device: CPU - Executor: Standard

ONNX Runtime

Model: GPT-2 - Device: CPU - Executor: Standard

Open Porous Media Git

OPM Benchmark: Flow MPI Norne - Threads: 2

ONNX Runtime

Model: fcn-resnet101-11 - Device: CPU - Executor: Standard

ONNX Runtime

Model: bertsquad-12 - Device: CPU - Executor: Standard

ONNX Runtime

Model: super-resolution-10 - Device: CPU - Executor: Standard

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

Xcompact3d Incompact3d

Input: input.i3d 193 Cells Per Direction

TNN

Target: CPU - Model: DenseNet

Caffe

Model: GoogleNet - Acceleration: CPU - Iterations: 200

Numpy Benchmark

WebP2 Image Encode

Encode Settings: Quality 75, Compression Effort 7

ONNX Runtime

Model: super-resolution-10 - Device: CPU - Executor: Parallel

ONNX Runtime

Model: fcn-resnet101-11 - Device: CPU - Executor: Parallel

ONNX Runtime

Model: GPT-2 - Device: CPU - Executor: Parallel

ONNX Runtime

Model: ArcFace ResNet-100 - Device: CPU - Executor: Parallel

ONNX Runtime

Model: yolov4 - Device: CPU - Executor: Parallel

ONNX Runtime

Model: bertsquad-12 - Device: CPU - Executor: Parallel

ASKAP

Test: tConvolve MT - Degridding

ASKAP

Test: tConvolve MT - Gridding

OpenFOAM

Input: Motorbike 30M

Caffe

Model: GoogleNet - Acceleration: CPU - Iterations: 100

oneDNN

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

oneDNN

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

oneDNN

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

oneDNN

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

Mlpack Benchmark

Benchmark: scikit_qda

oneDNN

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

oneDNN

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

Mobile Neural Network

Model: inception-v3

Mobile Neural Network

Model: mobilenet-v1-1.0

Mobile Neural Network

Model: MobileNetV2_224

Mobile Neural Network

Model: SqueezeNetV1.0

Mobile Neural Network

Model: resnet-v2-50

Mobile Neural Network

Model: squeezenetv1.1

Mobile Neural Network

Model: mobilenetV3

oneDNN

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

Mlpack Benchmark

Benchmark: scikit_linearridgeregression

Caffe

Model: AlexNet - Acceleration: CPU - Iterations: 200

ASKAP

Test: tConvolve MPI - Gridding

ASKAP

Test: tConvolve MPI - Degridding

Mlpack Benchmark

Benchmark: scikit_ica

Caffe

Model: AlexNet - Acceleration: CPU - Iterations: 100

oneDNN

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

Xcompact3d Incompact3d

Input: input.i3d 129 Cells Per Direction

ASKAP

Test: tConvolve OpenMP - Degridding

ASKAP

Test: tConvolve OpenMP - Gridding

oneDNN

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

oneDNN

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

TNN

Target: CPU - Model: MobileNet v2

Mlpack Benchmark

Benchmark: scikit_svm

TNN

Target: CPU - Model: SqueezeNet v1.1

ASKAP

Test: Hogbom Clean OpenMP

oneDNN

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

oneDNN

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

oneDNN

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

oneDNN

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

WebP2 Image Encode

Encode Settings: Quality 100, Compression Effort 5

TNN

Target: CPU - Model: SqueezeNet v2

ECP-CANDLE

Benchmark: P1B2

oneDNN

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

oneDNN

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

WebP2 Image Encode

Encode Settings: Default

WebP2 Image Encode

CPU Peak Freq (Highest CPU Core Frequency) Monitor

WebP2 Image Encode

CPU Peak Freq (Highest CPU Core Frequency) Monitor

WebP2 Image Encode

CPU Peak Freq (Highest CPU Core Frequency) Monitor

WebP2 Image Encode

CPU Peak Freq (Highest CPU Core Frequency) Monitor

WebP2 Image Encode

CPU Peak Freq (Highest CPU Core Frequency) Monitor

Open Porous Media Git

CPU Peak Freq (Highest CPU Core Frequency) Monitor

Open Porous Media Git

CPU Peak Freq (Highest CPU Core Frequency) Monitor

Open Porous Media Git

CPU Peak Freq (Highest CPU Core Frequency) Monitor