CUDA Spotlight: GPU-Accelerated Real Science
by Nadeem Mohammad, posted Mar 12 2012 at 03:12PM
This week we interviewed Dr. Jeffrey Vetter of Oak Ridge National Laboratory and Georgia Tech as part of the CUDA Spotlight Series.
NVIDIA: Jeff, what is the focus of your work?
I have worked on a number of projects: IBM BlueGene/L, Cray X1, Cray XT, FPGAs, GPUs and other technologies. Our team’s early work has contributed to the design and deployment of the NSF Keeneland system and the DOE Titan system.
Not surprisingly, our work over the last several years has primarily focused on GPUs. Our team is involved in most every aspect of GPUs in computational science: future architectures, programming systems, applications development, and education and outreach.
For example, we are partners on NVIDIA’s Echelon research project, which is sponsored by the DARPA UHPC program with the goal of fitting one PetaFLOPS in one rack and using less than 57K watts of power. On Keeneland, we actively engage applications teams with the full spectrum of experience on GPUs – from experts to no experience whatsoever.
NVIDIA: Tell us about Keeneland.
Together, we manage the facility (e.g., power, system administration, allocations), perform education and outreach activities for advanced architectures, develop and deploy software productivity tools for this class of architecture, and team with early adopters to map their applications to Keeneland architectures.
In 2010, the Keeneland project procured and deployed its initial delivery system (KIDS): a 201 TeraFLOPS, 120-node HP Proliant SL390 system with 240 Intel Xeon CPUs and 360 NVIDIA Tesla GPUs with the nodes connected by an InfiniBand QDR network. The KID system is being used to develop programming tools and libraries in order to ensure that users can productively accelerate important scientific and engineering applications. The system is also available to a select group of users to port and tune their codes to a scalable GPU-accelerated system.
In 2012, the Keeneland project will procure and deploy its full scale system, which will be available as a NSF XSEDE (Extreme Science and Engineering Discovery Environment) production resource.
NVIDIA: Who is utilizing Keeneland?
As of last week, Keeneland had approximately 75 projects and 200 users. Not surprisingly for a NSF supercomputer, most of the users are scientists from academia and other research organizations: Georgia Tech, University of Texas at Austin, University of Illinois at Urbana-Champaign, University of California, Stanford, Temple, Florida State, George Washington, Indiana, MIT, Purdue, Emory, NCAR, Utah and many others. We also partner with numerous industry vendors in order to ensure that tools work properly on Keeneland.
NVIDIA: What are some primary requirements of today’s researchers?
For example, the computational molecular biologists have applications that are generally ‘strong scaling’ applications, and, hence, adding more processors to, or scaling up, their application will eventually have diminishing returns in terms of performance. In essence, these strong scaling applications need faster processors, which is why many of these scientists have turned to GPUs.
On the other hand, other applications, such as those in combustion or materials design, need better performance on more complex versions of their applications: adding new physics for higher resolution. These applications typically must balance the need for faster processors with the need for low latency and high bandwidth communication among processors (GPUs), and larger memory capacity.
NVIDIA: What are the key drivers in supercomputing today?
On the other hand, applications developers are most concerned about programmability. The last significant transition for the scientific computing community was the transition in the 1990s from vector computing to distributed memory computing with MPI. In order to provide solutions to these questions, our team is investigating multiple fronts: CUDA, compiler directives, runtime libraries, frameworks and debugging and correctness tools. It is an exciting time to be in computer science!
NVIDIA: How does CUDA fit into the modern computing landscape?
Bio: Dr. Jeffrey Vetter