Learn about the common parallel programming models and their pros and cons for different applications. Find out how to choose the best model for your problem, hardware, and goals.

Two Google Fellows just published a paper in the latest issue of Communications of the ACM about MapReduce, the parallel programming model used to process more than 20 petabytes of data every day ...

The aim is to cover a wide range of parallel programming models, enabling the reader to understand what each has to offer. The book begins with a description of the Message Passing Interface (MPI), ...

In this work, we present a survey of the different parallel programming models and tools available today with special consideration to their suitability for high-performance computing. Thus, we review ...

This course examines the current techniques for design and development of parallel programs targeted for platforms ranging from multicore computers to high-performance clusters, with and without ...

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

These graphical models, based on asynchronous distributed control, explicitly represent all interactions of processes. The group has also developed Visa, a parallel programming language, which is ...

According to Nvidia, the new compiler source code "opens up" its CUDA parallel programming platform, allowing developers to more easily add GPU support for more programming languages.

Learn about the most common parallel programming models for computer science and how they work. Compare the advantages and disadvantages of shared memory, message passing, data parallel, task ...