CODEWITHSUNDEEP
matlabgpugpgpujacket
Stiefel
Stiefel

Reputation: 2793

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?

Upvotes: 58

Views: 6633

Answers (2)

Re Captcha
Re Captcha

Reputation: 3133

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
  • Multiple GPUs on the desktop and computer clusters using MATLAB workers in Parallel Computing Toolbox and MATLAB Distributed Computing Server

Upvotes: 5

dinwal
dinwal

Reputation: 467

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.

Upvotes: 3

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