CRAY Goes for GPUs

by Liana

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.


CULA in GPU Computing Gems 2

by John

It's been over a year in the making, but the final copy of our chapter contributed to GPU Computing Gems 2 has been submitted. The topic is a deeper look into how our routines function, with emphasis on the ever-popular LU decomposition. The book isn't due out yet for a while longer, but we hope you'll enjoy the article when the book is released. The best part is that we have permission to post the chapter here on our website at that time!


Modeling Complex Aircraft Carrier Landing Scenarios on the GPU

by Liana

Partial screenshot of the COTS Journal main homepage.

This month's issue of COTS Journal - a publication dedicated to military technology applications, gave special emphasis to GPGPU as "the latest disruptive technology to invade military embedded computing."  Since GPU computing for military applications is one of our areas of expertise, we were invited to contribute to the Special Feature article Carrier Landing Modeling Systems Leverages GPGPU Computing.

The article provides a great overview of how GPUs have become a well-suited platform for data-intense military applications.

The part of the article we contributed for discusses the development of GPU-accelerated Computational Fluid Dynamic (CFD) solvers that can accurately model aircraft landing scenarios on naval vessels.  This is a challenging project because we're modeling scenarios in which small moving elements interact with larger moving structures.  Talk about extremely complex scenarios and data intense computations!

Here is what John Humphrey, Product Manager and Developer of CULA, had to say in the article:

“Creating wholly new CFD solvers based around CUDA is interesting, but not practical because customers are familiar with their present applications and don’t want to validate and re-learn new solvers, so the need to modify existing code is a reality. What we look for is the roughly 10% of program code that’s responsible for consuming 90% of the runtime cycles, as parallelizing the most compute-intensive portions of the program will yield the greatest performance improvement when applying GPGPU technology.”

When John and his team were working on the proof of concept for this project, they were able to accelerate a solver for Euler equations by 54x. This means reducing the solver's compute time from 18 hours to 20 minutes, which is pretty significant. What they are facing now are computations that can consume 150,000 CPU hours to run!  How many GPUs and CPUs will it take?  Check back with us in a few months as we're just getting started on this Phase II project.

Read full story here!