Demos and benchmarks of arraymancer (WIP)
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README.md
dnn_classification.nim
dnn_classification.py
logistic_regression.lua
logistic_regression.nim
logistic_regression.py
run_benchmarks.sh

README.md

Arraymancer Demos

This repo contains some arraymancer demos and benchmarks.

Latest Benchmarks

These benchmarks share same model implementation and hyperparameters across frameworks.

Logistic regression

This benchmark consists of a classification on 209 RGB 3x64x64 images, classifying them as cats or noncats. The model is a logistic unit. Simple batch gradient descent is used.

CPU

Framework Backend Forward+Backward Pass Time
Arraymancer OpenMP + MKL 0.458ms
Torch7 MKL 0.686ms
Numpy MKL 0.723ms

GPU

Framework Backend Forward+Backward Pass Time
Arraymancer CUDA WIP
Torch7 CUDA 0.286ms

Deep neural network classification

As the above benchmark, consists of a classification on 209 RGB 3x64x64 images, classifying them as cats or noncats. The model is a deep fully connected neural network of layer sizes [209, 16, 8, 4, 1] (3 hidden layers + inputs/outputs layers). The activation function for the hidden layers is ReLU, the layer layer activation function is Sigmoid, and the loss is the binary cross entropy. Adam optimizer is used for batch gradient descent.

CPU

Framework Backend Forward+Backward Pass Time
Arraymancer OpenMP + MKL 2.907ms
PyTorch MKL 6.797ms

GPU

Framework Backend Forward+Backward Pass Time
Arraymancer CUDA WIP
PyTorch CUDA 4.765ms

Benchmark machine specs

  • Intel(R) Core(TM) i7-3770K CPU @ 3.50GHz
  • GeForce GTX 1080 Ti
  • ArchLinux (kernel 4.12.13-1-ARCH, glibc 2.26)
  • GCC 7.2.0
  • MKL 2017.17.0.4.4
  • OpenBLAS 0.2.20
  • CUDA 8.0.61
  • Nim 0.18.0 (head)