m600_7940hs-96gb-5600mhz-16gb-igpu-performance-tpd-2tb-sn850x-2023-09-01-2
AMD Ryzen 9 7940HS testing with a Win element M600 (SR500P03_P5C2V07 BIOS) and AMD Phoenix1 16GB on EndeavourOS rolling via the Phoronix Test Suite.
HTML result view exported from: https://openbenchmarking.org/result/2309028-NE-M6007940H04&grs.
Scikit-Learn
Benchmark: Hist Gradient Boosting
Scikit-Learn
Benchmark: Isotonic / Logistic
Scikit-Learn
Benchmark: TSNE MNIST Dataset
Scikit-Learn
Benchmark: Feature Expansions
Scikit-Learn
Benchmark: Plot OMP vs. LARS
Scikit-Learn
Benchmark: Plot Hierarchical
Scikit-Learn
Benchmark: Plot Fast KMeans
Scikit-Learn
Benchmark: SGDOneClassSVM
Scikit-Learn
Benchmark: SGD Regression
Scikit-Learn
Benchmark: MNIST Dataset
Scikit-Learn
Benchmark: Plot Ward
Scikit-Learn
Benchmark: Sparsify
Scikit-Learn
Benchmark: Lasso
Scikit-Learn
Benchmark: Tree
Scikit-Learn
Benchmark: GLM
Numenta Anomaly Benchmark
Detector: Contextual Anomaly Detector OSE
Numenta Anomaly Benchmark
Detector: Bayesian Changepoint
Numenta Anomaly Benchmark
Detector: Earthgecko Skyline
Numenta Anomaly Benchmark
Detector: Windowed Gaussian
Numenta Anomaly Benchmark
Detector: Relative Entropy
Numenta Anomaly Benchmark
Detector: KNN CAD
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
NCNN
Target: CPU - Model: FastestDet
NCNN
Target: CPU - Model: vision_transformer
NCNN
Target: CPU - Model: regnety_400m
NCNN
Target: CPU - Model: yolov4-tiny
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
TensorFlow Lite
Model: Inception ResNet V2
TensorFlow Lite
Model: Mobilenet Quant
TensorFlow Lite
Model: Mobilenet Float
TensorFlow Lite
Model: NASNet Mobile
TensorFlow Lite
Model: Inception V4
TensorFlow Lite
Model: SqueezeNet
RNNoise
DeepSpeech
Acceleration: CPU
Numpy Benchmark
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 Inference - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU
oneDNN
Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU
oneDNN
Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU
oneDNN
Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU
oneDNN
Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU
NCNN
Target: CPU - Model: squeezenet_ssd
NCNN
Target: CPU - Model: resnet50
oneDNN
Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU
Phoronix Test Suite v10.8.5