How long does it take to get 98X performance improvement with GPUs?

John Melonakos Case Studies 2 Comments

Well, here is a recent story with one of our customers that accomplished 98X performance speed up with Jacket in 16 days.  Of the 16 days, 15 days were spent sending emails back and forth about the problem and less than a day was spent getting the customer code in Jacket and running some performance tests!  Who would have imagined GPU programming with performance in 1 day.  Happy Reading. Day 1: Customer uses inverse radon (or iradon in MATLAB terms) extensively for their back projection algorithms.  They would like to know when the iradon function will be available/supported in Jacket. AccelerEyes product management informs the customer that the inverse radon algorithm used in MATLAB is based on the filtered back ...

LAPACK Functions in Jacket (eig, inv, etc.)

John Melonakos CUDA 2 Comments

One of the questions people commonly ask us is: When will Jacket support LAPACK features such as eigenvalue decomposition, matrix inverse, system solvers, etc.? The reason this question is so popular is that people recognize that these kinds of problems are well-suited for the GPU and will end up giving great performance boosts for Jacket users.  We are looking forward to delivering these functions in Jacket. Jacket is currently built on top of CUDA.  For reasons why we like CUDA, see our previous blog post about OpenCL.  While NVIDIA is busy building from CUDA from the ground up, we are busy building Jacket from the top (MATLAB) down.  NVIDIA is working hard to promote and develop LAPACK libraries directly into ...

The NVIDIA MEX-Plugin & Jacket

John Melonakos CUDA Leave a Comment

One of the first questions people ask when considering Jacket for GPU MATLAB computing is the following: How is Jacket different from the MATLAB plugin on the NVIDIA website (found here:  http://developer.nvidia.com/object/matlab_cuda.html)? The short answer to this is that the NVIDIA MEX-plugin requires you to write CUDA code, while Jacket does not.  This has many implications and ends up resulting in a lot of advantages for you as a MATLAB programmer.  First let's describe the features of the MEX-plugin: You write CUDA code that solves your problem. You use the MEX configuration files provided by NVIDIA to compile your CUDA code into a MEX file that is callable by MATLAB. MATLAB calls your MEX file, moves data out to the ...

OpenCL

John Melonakos CUDA, OpenCL 4 Comments

We often get questions such as the one we just received via email: 1) Any idea if you will be supporting AMD/ATI cards in future ? 2) Have you considered OpenCL as a potential pathway for the future ? I can see an advantage there for you (if it takes off) in that you're not tied to a single vendor any more and potentially you'd be able to take advantage of other accelerators that may support it. It's very early days yet but certainly from our point of view the current paradigm of code to a single vendors card doesn't seem sustainable.. OpenCL is a community effort to create a standard for parallel computing, with early emphasis on GPGPU computing, ...