Parallel programming is not easy, but one tool you can use to help parallelize your application is OpenMP. Most compilers are compatible with OpenMP and allow you to parallelize your code on a single node.

Gathering data on various aspects of your HPC system is a key step toward developing information about the system and one of the first steps toward tuning your system for performance and reporting on system use. It can tell how users are using the system and, at a high level, what they are doing. In this article, I present a method for gathering data on how users are using Environment Modules, such as which modules are being used, how often, and so on.

Using the Open|SpeedShop Performance Tool to Find Application Performance Bottlenecks

We talk with Michael Wong and Matthijs van Waveren about the latest developments surrounding the OpenMP ARB consortium.

AMD's new Opteron 6200 chip is tooled for HPC, with a revolutionary 16-core design and improved power management features.

OpenMP brings the power of multiprocessing to your C, C++, and Fortran programs.

Examine and optimize your server’s internal topology with this hardware locality tool suite.

Overcome the Global Interpreter Lock in Python and take advantage of run-time parallelism on multicore machines.

We take a close look at how to integrate graphics processors into your parallel programs with OpenCL.

We’ll show you around some of the tools you need for rolling out your own high-performance cluster.