Publications

Doerfler, Douglas W.; Rajan, Mahesh R.; Epperson, Marcus E.; Vaughan, Courtenay T.; Pedretti, Kevin P.; Barrett, Richard F.; Barrett, Brian B.

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Hemmert, K.S.; Barrett, Brian B.; Underwood, Keith D.

Efficient collective operations are a major component of application scalability. Offload of collective operations onto the network interface reduces many of the latencies that are inherent in network communications and, consequently, reduces the time to perform the collective operation. To support offload, it is desirable to expose semantic building blocks that are simple to offload and yet powerful enough to implement a variety of collective algorithms. This paper presents the implementation of barrier and broadcast leveraging triggered operations - a semantic building block for collective offload. Triggered operations are shown to be both semantically powerful and capable of improving performance. © 2010 Springer-Verlag.

Stark, Dylan S.; Murphy, Richard C.; Barrett, Brian B.; Berry, Jonathan W.

Abstract not provided.

Brightwell, Ronald B.; Barrett, Brian B.; Hemmert, Karl S.

Achieving the next three orders of magnitude performance increase to move from petascale to exascale computing will require a significant advancements in several fundamental areas. Recent studies have outlined many of the challenges in hardware and software that will be needed. In this paper, we examine these challenges with respect to high-performance networking. We describe the repercussions of anticipated changes to computing and networking hardware and discuss the impact that alternative parallel programming models will have on the network software stack. We also present some ideas on possible approaches that address some of these challenges.

Wheeler, Kyle B.; Barrett, Brian B.; Ang, James A.

Abstract not provided.

Murphy, Richard C.; Wheeler, Kyle B.; Barrett, Brian B.; Ang, James A.

Abstract not provided.

International Journal of Distributed Systems and Technologies

Alvin, Kenneth F.; Barrett, Brian B.; Brightwell, Ronald B.; Dosanjh, Sudip S.; Geist, Al; Hemmert, Karl S.; Heroux, Michael; Kothe, Doug; Murphy, Richard C.; Nichols, Jeff; Oldfield, Ron A.; Rodrigues, Arun; Vetter, Jeffrey S.

There is considerable interest in achieving a 1000 fold increase in supercomputing power in the next decade, but the challenges are formidable. In this paper, the authors discuss some of the driving science and security applications that require Exascale computing (a million, trillion operations per second). Key architectural challenges include power, memory, interconnection networks and resilience. The paper summarizes ongoing research aimed at overcoming these hurdles. Topics of interest are architecture aware and scalable algorithms, system simulation, 3D integration, new approaches to system-directed resilience and new benchmarks. Although significant progress is being made, a broader international program is needed.

Devine, Karen D.; Plimpton, Steven J.; Bayer, Gregory B.; Barrett, Brian B.; Berry, Jonathan W.

Abstract not provided.

IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium

Barrett, Brian B.; Berry, Jonathan W.; Murphy, Richard C.; Wheeler, Kyle B.

Abstract not provided.

Barrett, Brian B.; Hendrickson, Bruce A.; Laviolette, Randall A.; Leung, Vitus J.; Mackey, Greg; Murphy, Richard C.; Phillips, Cynthia A.; Pinar, Ali P.

Large relational datasets such as national-scale social networks and power grids present different computational challenges than do physical simulations. Sandia's distributed-memory supercomputers are well suited for solving problems concerning the latter, but not the former. The reason is that problems such as pattern recognition and knowledge discovery on large networks are dominated by memory latency and not by computation. Furthermore, most memory requests in these applications are very small, and when the datasets are large, most requests miss the cache. The result is extremely low utilization. We are unlikely to be able to grow out of this problem with conventional architectures. As the power density of microprocessors has approached that of a nuclear reactor in the past two years, we have seen a leveling of Moores Law. Building larger and larger microprocessor-based supercomputers is not a solution for informatics and network infrastructure problems since the additional processors are utilized to only a tiny fraction of their capacity. An alternative solution is to use the paradigm of massive multithreading with a large shared memory. There is only one instance of this paradigm today: the Cray MTA-2. The proposal team has unique experience with and access to this machine. The XMT, which is now being delivered, is a Red Storm machine with up to 8192 multithreaded 'Threadstorm' processors and 128 TB of shared memory. For many years, the XMT will be the only way to address very large graph problems efficiently, and future generations of supercomputers will include multithreaded processors. Roughly 10 MTA processor can process a simple short paths problem in the time taken by the Gordon Bell Prize-nominated distributed memory code on 32,000 processors of Blue Gene/Light. We have developed algorithms and open-source software for the XMT, and have modified that software to run some of these algorithms on other multithreaded platforms such as the Sun Niagara and Opteron multi-core chips.

Doerfler, Douglas W.; Hemmert, Karl S.; Barrett, Brian B.; Kelly, Suzanne M.

Abstract not provided.

Pedretti, Kevin P.; Barrett, Brian B.; Hemmert, Karl S.; Vaughan, Courtenay T.

Abstract not provided.

Barrett, Brian B.

Abstract not provided.