Jimi Malcolm, VP of Engineering and Co-founder of AccelerEyes takes about 15 minutes to share CUDA optimization strategies to maximize performance of CUDA code. Watch the video below to find out what needs to go into strategizing CUDA development to maximize performance. Jimi uses Median Filtering for this case study. Get the Flash Player to see this player.
Using Parallel For Loops (parfor) with MATLAB® and Jacket
MATLAB® parallel for loops (parfor) allow the body of a for loop to be executed across multiple workers simultaneously, but with some pretty large restrictions. With Jacket MGL, Jacket can be used within parfor loops, with the same restrictions. However, it is important to note that Jacket MGL does not currently support co-distributed arrays. Problem Size Problem size might be the single most important consideration in parallelization using the Parallel Computing Toolbox (PCT) and Jacket MGL. When data is used by a worker in the MATLAB pool it must be copied from MATLAB to the worker, and must be copied back when the computation is complete. Additionally, when GPU data is used, it must then be copied by the worker …
Streaming data to the GPU
Learn how to stream data directly to the GPU using the Jacket SDK.
Torben’s Corner
We work very closely with our customers and really appreciate the feedback we receive and value the insight provided. One Jacket programmer has started to post fantastic content on the Jacket Documentation Wiki under Torben’s Corner. This content is maintained by Torben Larsen‘s team at AAU focusing primarily on outlining performance observations between GPUs and CPUs. This information is not only of great value to our technical team but also valuable to the entire Jacket community. Thanks Torben for this great resource!
Jacket in a GPU Cloud
Wow! Jacket is now running MATLAB on a GPU cloud server from Penguin Computing! We were setting up demos today at SuperComputing 2009 and just got things setup inside Penguin Computing’s booth. Jacket is now running as compiled MATLAB code on Penguin Computing’s POD (Penguin on Demand) cloud service! The OpenGL visualizations are running without a hitch through VGL, so for everyone on the forums, this seems like another effective method of running Jacket remotely — at least on Linux. Does anyone know if VGL runs under windows? Penguin Computing was using it quite effecitvely – their setup was very slick!
Developer SDK Upgrade
In Jacket v1.1, an optional Developer SDK Upgrade is available. This upgrade provides the ability for you to integrate custom CUDA code for use with MATLAB. With a few simple jkt functions (which mimic standard MEX API functions), you can integrate custom CUDA kernels into Jacket. This task is as simple as replacing the main function in your program with jktFunction, which is used in the place of mexFunction for integration of CUDA code into MATLAB and Jacket. This serves an an entry point to Jacket’s runtime. Within a jktFunction, you have access to several jkt API functions to do tasks such as getting input from MATLAB, allocating device memory, calling the CUDA kernels, and casting the kernel’s output to …
Commentary on Jacket v1.1
I’m pleased to announce the release of Jacket v1.1! This release represents a major milestone in Jacket’s development and a great boost in functionality for Jacket customers. The major feature of this release is the inclusion of new GPU datatypes, most notably double-precision. We are very pleased with the performance we’ve seen for double-precision computations. At the time of this writing, the NVIDIA Tesla T10 series is the newest GPU on the market and NVIDIA’s first in what will become a great line of double-precision enabled GPUs. Even on this first double-precision generation card, we are seeing ~20x speedups for a lot of our examples and test cases. Of course, GPUs still give higher speedups when comparing single-precision GPU to …
LAPACK Functions in Jacket (eig, inv, etc.)
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 …
Data-parallelism vs Task-parallelism
In order to understand how Jacket works, it is important to understand the difference between data parallelism and task parallelism. There are many ways to define this, but simply put and in our context: Task parallelism is the simultaneous execution on multiple cores of many different functions across the same or different datasets. Data parallelism (aka SIMD) is the simultaneous execution on multiple cores of the same function across the elements of a dataset. Jacket focuses on exploiting data parallelism or SIMD computations. The vectorized MATLAB language is especially conducive to good SIMD operations (more so than a non-vectorized language such as C/C++). And if you’re going to need a vectorized notation to achieve SIMD computation, why not choose the …
The NVIDIA MEX-Plugin & Jacket
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 …