Here are the slides for the talk as promised. Note that slideshare is not showing some of the images etc., so you might be better to download the pdf from slideshare.
We will be speaking at this year’s GPU Tech. Conf. in San Jose, which goes from Sept. 30 to Oct. 2, about using CUDA within Mathematica. The slides are almost ready and we are just organizing some logistics etc. I thought we might write a bit about the talk in order to get some initial feedback on the content.
The talk is divided into three parts, initially we introduce the structure of Mathematica, in particular its MathLink API and go into the basics idea of creating a simple C++ application which we can call from Mathematica. Then we discuss the API in a bit more details, especially receiving and sending arrays to and from Mathematica. Its here where we also discus how to receive and send complex numbers, which is handy when doing FFT for example. We then briefly discuss running MathLink applications on remote computers, which is specially useful if you share your CUDA enabled computer with others. Finally we go through some basic error and interruption handling in the MathLink API.
The second part then concentrates on the CUDA aspect of the MathLink application, in some sense the whole philosophy of the talk. If we create a CUDA application that can get and receive data from Mathematica, via the MathLink API, then we are done! In particular we give an overview of a simple example using the
mathematica_cuda plugin, which lets you do just this. For a more universal solution, one that works under Windows, there is the excellent CMake module: FindCUDA together with my FindMathLink module which I wrote about previously. We then finish this part by going through a complete example: FFT via CUFFT and show how one goes about getting it working in Mathematica.
The last part, time permitting, is where we show some of the work we have been doing with sending computations to the GPU from Mathematica. In particular I will show some of the work I have been doing with image deconvolution of Confocal and Wide-field images. I am using the GPU to do my deconvolution experiments and using Mathematica to read in the images and analyze the results. check domains . Shoaib will present his work on calculating the vegetation index in multi and hyper-spectral satellite images.
I hope you find this overview helpful. We will put the slides up here when the tutorial is over, and if you plan to attend the conference it would be great to see you and get your feedback. Also if there is something specific you would like us to cover, you still have a few days to let us know.
In order to get a more universal solution to my mathematica_cuda plugin, one that works on Windows as well as on Mac and Linux, I decided to use CMake, which comes with the excellent FindCUDA module together with a MathLink module which would offer the same functionality as the current
mathematica_cuda plugin, plus more.
I looked on the web if someone else had already written such a module for MathLink, and in the end found Erik Franken who sent me a version he had modified from a version by Jan Woetzel and others:
A great article about Twittering with Mathematica on the Wolfram blog. I had investigated a while ago a Mathematica twitter bot for doing “Micro-calculations” with the results from Mathematica being less than 140 chars. Not very useful but a fun bot.
Anyways if you are interested, I made a gist for it. Its in Java and uses JLink to communicate with Mathematica. It was never running for long as I suspect it violated some end user license, but basically one would send a Mathematica command to @mathematica and it would tweet you back your result evaluated by the MathKernel. I am hoping Wolfram might create a similar bot themselves for when you need to know the value of a special function quickly
The basic structure of the project follows that of the Nvidia’s Cuda SDK, in that the individual projects are in their own folder inside the projects folder. Right now I have the scalarProd example from Nvidia. I have also included Nvidia’s cuda utilities cutils and extended the make system to handle Mathematica template files.
Currently I have tested it only on 64-bit Linux, but hopefully I will see if I can get it working under Mac and Windows. I also plan to add more documentation in the project’s wiki on github, and hopefully get some more useful examples implemented, perhaps FFT.
Since there is a Matlab plug-in for CUDA that provides some examples of off-loading computation to the GPU, I thought it might be neat to have something similar for Mathematica. So as a start, I decided to try out a simple scalar product example using MathLink.
The initial template of my function is in the scalarProd.tm file:
which describes the ScalarProd function in Mathematica, and links it to the scalarProd() C method, which is where we receive the two arrays from Mathematica and use CUDA to calculate their scalar product and send the result back. This and the main() function for Linux and Mac, which is what I was using, are in the scalarProd.cu file. Note that Windows has a slightly different main() method.
and in the same scalarProd.cu we now include the scalarProd_kernel.cu kernel from CUDA’s SDK together with our scalarProd() C function:
Now we are ready to run Mathematica’s mprep pre-processor from MathLink to generate a scalarProdtm.cu file, and on this we run CUDA’s compiler nvcc and compile everything with the appropriate CUDA and MathLink libraries to generate our scalarProd binary, which we can now call from within Mathematica: