Learn how parallel programming works in computer science, what are the benefits and challenges of using it, and what are some of the common techniques and tools for parallel programming.

Learn the best ways and resources to learn parallel programming on your own, with tips and tools for different languages and platforms.

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on its own GPUs (graphics processing units).

Today's computer science students are entering a new era in parallel computing, featuring cheap multicores and high-performance clusters, but have received traditional largely-sequential training.

The computing industry has been ready for the parallel computing era for more than a decade. Most small-to-mid-size organizations use multiprocessor servers; commercial databases (such as Oracle and ...

Technicians at Argonne National Laboratory work on MIRA, the fifth-fastest supercomputer in the world (as of July 2014). Using statistical models, computer scientists have shown that certain kinds of ...

Parallel computing allows you to perform many calculations simultaneously, leveraging the power of modern multi-core processors and GPUs. CUDA (Compute Unified Device Architecture) is a parallel ...

Every light switch in your house operates in parallel with the others. There’s a new edition of a book, titled Parallel Programming for FPGAs that explores that topic in depth and it is under ...