Rob Farber writes about CUDA programming for Dr. Dobb’s. In Part 8 of this article series on CUDA (short for “Compute Unified Device Architecture”), he focuses on using libraries with CUDA. In Part 9, he takes a look at how one can extend high-level languages (like Python) with CUDA.

“CUDA lets programmers who develop in languages other than C and C++ harness the power of thousands of software threads simultaneously running on hundreds of thread-processors inside of today’s graphics processors. Libraries (discussed in Part 8) provide some of this capability, as most languages can link with C-language libraries. A more flexible and powerful capability lies in the ability of many languages — such as Python, Perl, and Java — to be extended through modules written in C, or CUDA when programming for GPU environments. Much of the power in these extensions is a result of the freedom they offer developers to define classes and methods that can locate and operate on data within the GPU without being limited by a static library interface.”

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Part 8
Part 7
Part 6
Part 5
Part 4
Part 3
Part 2
Part 1