Your parallel application is running fine, but you want it to run faster. Naturally, you use more and more cores, and everything is great; however, suddenly performance starts decreasing. What just happened?

The quest for exascale performance by the year 2020 is on. Delivering 10¹⁸ FLOPS could be slowed by the speed of light, among other things.

Getting started in the HPC world requires learning to write parallel applications and learning to administer and manage clusters. We take a look at some ways to get started.

Hadoop has been growing clusters in data centers at a rapid pace. Is Hadoop the new corporate HPC?

Adding and subtracting software from a running cluster can be tricky; however, many application packages can be added or removed easily with a few tools and some simple tricks.

Cloud computing has become a viable option for high-performance computing. In this article, we discuss the use case for cloud-based HPC, introduce the StarCluster toolkit, and show how to build a custom machine image for compute nodes.

As core counts increase in both CPUs and GPUs, the HPC lifestyle could become a bit more difficult.

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.

The Modules package makes life easier in the HPC world (and beyond).

HPC systems are really designed to be shared by several users. One way to share them is through a software tool called a resource manager. Openlava is an open source version of the commercial scheduler LSF. It shares the robustness of LSF while being freely available, very scalable, and easy to install and customize.