Ouro

Auto-tuner that builds optimized kernels for Convolutional Layers on ConvNets

Given convolution parameters, It runs the three main convolution methods: Direct Method, 
Lowering and FFT-based. Then, it gives the best method .o and auto-tuned parameters. 
The project is multi-staged. It uses Lua as a high-level language and Terra as the low-level staged code. 

requirements:
    Terra (github.com/zdevito/terra)
    Terra is a low-level language created with interoperability with Lua in mind. 

running:
    <terra binary> src/tuner.lua (it generates dconv.o or sconv.o)
    You can run it as terra src/tuner.lua --help to see how it works and some flags or just see the code. 
    *Find the terra binary and run it or just make sure terra is in your $PATH or you have an alias to it
    image test: use the makefile (summarily: terra src/imageconv.lua && ./my_convolution <kernel>)       Images are stored on /images.

most important branches: 
    -> Direct Method 
    -> Lowering (multi-threaded, using optimized GEMM))
    State of the art of the method: http://arxiv.org/abs/1504.04343
    -> FFT-based method (also called: "Fast Convolution") 
    Features: Multi-threaded, using: 2-points and 4-points FFT kernels, kernel for transpose  
and kernel for point-wise multiplication (blocked and auto-tuned)
    State of the art of the method: http://arxiv.org/abs/1412.7580  


most important files: 
    src/tuner.lua: auto-tuner that iterates over the three methods 
    benchmarks/: benchmarking with other implementations
    src/examples/: some minimal code of implemented features

libs:  
    image.t:image library (from github.com/jameshegarty/darkroom)
    <method>-matrixtestharness.t: time measure in the auto-tuning process
    <method>-multithreads.t: multi-thread library based on pthreads
    fft-kernels.t: n-point kernels for FFT

branching tags:
  wip: works in progress
  junk: experiments
  bug: fixing a bug
  dev: intermediate developments (to be put on tests/ folder) or with some specific feature

tags:
  v1.0

Note: the FFT-based has accuracy errors and the idea is to improve it and fix an upper-bound to round-off errors