In this third and last article on OpenMP, we look at good OpenMP coding habits and present a short introduction to employing OpenMP with GPUs.

Moore’s Law continues its unwavering march, with a twist.

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.

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.

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

In this final article on the Julia Language, we look at a few other useful features, including distributed arrays and possible GPU programming.

GNU Parallel brings the benefits of multicore processing to the command line.