In rapidly growing HPC installations, you need to understand what is happening within the system to make improvements or simply to justify the purchase.

The Xeon Phi accelerator card from Intel takes an unusual approach: Instead of GPUs, the Xeon Phi features a cluster of CPUs for easier programming.

Introduction of the new OpenMP specification abstracts away many of the thorny issues associated with today’s HPC hardware.

Even with tons of cores per node today, the traditional sets of tools are still serial-only, utilizing a single core; however, some of the more popular tools have parallel versions, allowing you to use the extra cores either to run the same command in parallel or to perform the same task across multiple nodes.

The language you choose to use affects I/O patterns and performance. We track a simple write I/O pattern with C and look at how to improve performance.

Hadoop version 2 expands Hadoop beyond MapReduce and opens the door to MPI applications operating on large parallel data stores.

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?