In the latest release of Mathematica 8, a large number of programming tools for GPU computing are available. In this presentation, new tools for CUDA and OpenCL programming will be explored. Several applications, including image processing, medical imaging, multi-gpu, statistics and finance will be presented.

If you are a Mathematica user, you will find that advance compiling tools for C, CUDA and OpenCL coding are now part of the familiar Mathematica language. If you are a hard core C and GPU developer, Mathematica 8 is a convenient platform to prototype and develop your own algorithms, with the added benefit to integrate your code with the thousands of Mathematica's build in functions.

Researchers should cite this work as follows:

• Ulises Cervantes-Pimentel, Abdul Dakkak (2011), "Mathematica for CUDA and OpenCL Programming," https://nanohub.org/resources/10940.

  BibTex | EndNote

1. CUDA
2. Illinois
3. Kernel Development
4. Mathematica
5. nanoelectronics
6. OpenCL
7. parallel algorithms
8. parallel programming
9. programming techniques
10. Simulation
11. Visulization