CUDA resources

Here are links that illustrate how CUDA works and how it can help in maximizing the parallelism inherently in your Nvidia graphics cards.

Part1 - Introduction to CUDA

Part2 - Explanation of CUDA programming constructs

Part3 - Error handling and CUDA global memory limitations

Part4 - Understanding CUDA shared memory

Part5 - How to use CUDA shared memory effectively

Part6 - Profiling CUDA code

Part7 - Current and future implementations

Part8 - Using CUBLAS and CUFFT (CUDA versions of Fast Fourier Transformation & Basic Linear Algebra Subprograms)

Part9 - Extending CUDA to support other programming languages

Part10 - CUDPP

Part11 - Revisiting CUDA memory spaces

Personally, i like to thank Rob Farber for this series of REALLY awesome articles published on Dr. Dobb's Journal which is another great platform for developers world-wide. Yay! 

While using CUDA to implement your test or commercial applications, its likely you'll create your own arrays/matrices or vectors which is fine but i would recommend that you re-implement that part of your program that uses these constructs using the CUBLAS library. Remember to try out the CUFFT library as well.