Not enough HPC programmers. How to fill the gap?

Engineers with top notch parallel programming experience are highly in demand in the U.S.  This fact was recently pointed out in stories published by the mainstream Daily Beast, as well as HPC Wire. A quote from Stan Ahalt in the Daily Beast story caught my attention: “It’s not enough to keep building powerful supercomputers unless we have the brains. Think of a supercomputer as a very fast racing engine. We need more drivers to use those engines." Stan is the director of a supercomputing center at the University of North Carolina at Chapel Hill.

Programming supercomputers is hard work. Those involved in programming large HPC systems go through in-depth training and spend months (sometimes years) fine-tuning their algorithms until they are fully leveraging the massive computing power these machines offer. There is a growing number of tools and libraries for HPC programmers, but not necessarily suitable for all levels of computer engineers. For non HPC-experts, programming small to mid-scale systems can be a pretty challenging and time-consuming task, something we hear quite often from our customers and partners.

Where EM Photonics Can Make a Difference

Companies with recently installed small- to mid-scale supercomputing systems often need help porting their applications to their new machines. This is where we bring tremendous value. We are easy to engage with and offer in-depth understanding of parallel architectures. On top of parallel programming expertise, we bring knowledge and experience in physics-based modeling and simulation, image processing, life sciences, finance, military and defense applications. (Typically, the bigger the problem, the greater the fun!)

We encourage you to take a peak at our EM Photonics site to learn more about our consulting services, as well as current research projects and published papers. We have a team of talented engineers looking forward to tackling new challenges. Just let us know how we can help!

HPCwire on GPU Computing

Dr. Vincent Natoli from Stone Ridge Technology has recently published a very good article in HPCwire evaluating 10 common objections to GPU computing.  In this article he brings up 10 reasons why people have been hesitant to get involved with GPU computing and provides a counter-argument to each of these reasons.

The GPU team at EM Photonics agrees with a number of Dr. Natoli's points as can be seen in our CULA designs principals.  For example, his #1 fallacy is:

"I don’t want to rewrite my code or learn a new language."

In CULA, we designed a system that completely abstracts all GPU details from the user - there is no need to learn CUDA to accelerate an existing LAPACK function. You can simply compile your code using CULA functions (or alternatively link against the new link interface) and all details including initialization, memory transfers, and computation are performed with a single function call. This approach allows scientists and developers unfamiliar with GPU programming to quickly accelerate codes with LAPACK functionality.

Another fallacy examined by Dr. Natoli is:

"The PCIe bandwidth will kill my performance"

In CULA, for large enough problems, the PCIe transfer time accounts for less than 1% of the total runtime! For many of the LAPACK functions, the memory requirements are of order O(N²) while the computation is of order O(N³).  This discrepancy means that the amount of computation needed is growing at a much faster rate than the memory required. While this might not always be true for other domains, it is certainly the case for the majority of CULA. Additionally, through creative implementations it is possible to overlap GPU computation with GPU transfers and CPU computation. This technique is used heavily in CULA to achieve even higher speeds.

Overall, the article answers some of the most common misconceptions about GPU computing and is a good read for both novices and experts in the area.

CRAY Goes for GPUs

Michael Feldman from HPCWire wrote a very interesting piece on CRAY's first GPU Supercomputer - the XK6, a pretty impressive system that combines AMD X86 processors with NVIDIA GPUs.  In fact, the news is being covered by all major media outlets and you may have already read about it either directly from CRAY or your favorite news site.

We enjoyed reading Feldman's coverage of the story because he mentioned some details in addition to the information provided by CRAY on their May 24 press release. One of the details he mentioned was about CRAY's plans to offer third-party GPU software libraries like CULA:

"Cray also will be developing additional GPU compilers, runtime libraries, and tools, as well as bringing in third-party software, such as EM Photonics' CULA library, to make the environment richer and more productive. The idea here is to bring GPU acceleration in line with its Adaptive Supercomputing approach. The ultimate goal is to be able to write source code that could automatically be transformed to run on either CPUs, GPUs or some mix of the two. The goal is not just to deliver performance, says (Barry) Bolding, but to "get your codes to better performance faster."

Read HPCWire's full story and feel fee to ask us any questions that you may have.