Learn how to use tools and techniques such as OpenMP, Pthreads, Cilk Plus, and CUDA to implement shared memory parallel programming and improve your application performance.

Parallel programming techniques have been prominently explored in various engineering applications as it provides a time efficient solution to the complex problems without affecting the accuracy.

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.

To deal with multiple memory locations, “traditional” parallel programming has had to resort to synchronization. With the help of mutex (mutual exclusion) directives, a program can ensure that it is ...

Beaverton, Oregon — The OpenMP Architecture Review Board (ARB) today announced that Samsung has joined the board. The OpenMP ARB is a group of hardware and software vendors and research organizations ...

At the same time, of course, TurboMP lets Unix systems use shared-memory segments. It uses IBM's low-level API (LAPI) to exploit IBM hardware. Platforms supported are AIX, Power4, Power5 and Pseries.

Parallel Programming: The practice of developing software that concurrently executes multiple computations to leverage the capabilities of multicore architectures.