Accelerating MATLAB code using GPUs?

AccelerEyes announced in December 2012 that it works with Mathworks on the GPU code and has discontinued its product Jacket for MATLAB:

http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/

Unfortunately they do not sell Jacket licences anymore.

As far as I understand, the Jacket GPU Array solution based on ArrayFire was much faster than the gpuArray solution provided by MATLAB.

I started working with gpuArray, but I see that many functions are implemented poorly. For example a simple

myArray(:) = 0 

is very slow. I have written some custom CUDA-Kernels, but the poorly-implemented standard MATLAB functionality adds a lot of overhead, even if working with gpuArrays consistently throughout the code. I fixed some issues by replacing MATLAB code with hand written CUDA code - but I do not want to reimplement the MATLAB standard functionality.

Another feature I am missing is sparse GPU matrices.

So my questions are:

How do is speed up the badly implemented default GPU implementations provided by MATLAB? In particular, how do I speed up sparse matrix operations in MATLAB using the GPU?

Solution 1:

MATLAB does support CUDA based GPU. You have to access it from the "Parallel Computing Toolbox". Hope these 2 links also help:

Parallel Computing Toolbox Features

Key Features

• Parallel for-loops (parfor) for running task-parallel algorithms on multiple processors
• Support for CUDA-enabled NVIDIA GPUs
• Full use of multicore processors on the desktop via workers that run locally
• Computer cluster and grid support (with MATLAB Distributed Computing Server)
• Interactive and batch execution of parallel applications
• Distributed arrays and single program multiple data (spmd) construct for large dataset handling and data-parallel algorithms

MATLAB GPU Computing Support for NVIDIA CUDA-Enabled GPUs

Using MATLAB for GPU computing lets you accelerate your applications with GPUs more easily than by using C or Fortran. With the familiar MATLAB language you an take advantage of the CUDA GPU computing technology without having to learn the intricacies of GPU architectures or low-level GPU computing libraries.

You can use GPUs with MATLAB through Parallel Computing Toolbox, which supports:

• CUDA-enabled NVIDIA GPUs with compute capability 2.0 or higher. For releases 14a and earlier, compute capability 1.3 is sufficient.
• GPU use directly from MATLAB
  • GPU-enabled MATLAB functions such as fft, filter, and several linear algebra operations
  • GPU-enabled functions in toolboxes: Image Processing Toolbox, Communications System Toolbox, Statistics and Machine Learning Toolbox, Neural Network Toolbox, Phased Array Systems Toolbox, and Signal Processing Toolbox (Learn more about GPU support for signal processing algorithms)
  • CUDA kernel integration in MATLAB applications, using only a single line of MATLAB code
• Multiple GPUs on the desktop and computer clusters using MATLAB workers in Parallel Computing Toolbox and MATLAB Distributed Computing Server

Solution 2:

I had the pleasure of attending a talk by John, the founder of AccelerEyes. They did not get the speedup because they just removed poorly written code and replaced it with code that saved a few bits here and there. Their speedup was mostly from exploiting the availability of cache and doing a lot of operations in-memory (GPU's). Matlab relied on transferring data between GPU and CPU, if I remember correctly, and hence the speedup was crazy.