By Topic

Resource-aware scientific computation on a heterogeneous cluster

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Teresco, J.D. ; Dept. of Comput. Sci., Williams Coll., Williamstown, MA, USA ; Fair, J. ; Flaherty, J.E.

Although researchers can develop software on small, local clusters and move it later to larger clusters and supercomputers, the software must run efficiently in both environments. Two efforts aim to improve the efficiency of scientific computation on clusters through resource-aware dynamic load balancing. The popularity of cost-effective clusters built from commodity hardware has opened up a new platform for the execution of software originally designed for tightly coupled supercomputers. Because these clusters can be built to include any number of processors ranging from fewer than 10 to thousands, researchers in high-performance scientific computation at smaller institutions or in smaller departments can maintain local parallel computing resources to support software development and testing, then move the software to larger clusters and supercomputers. As promising as this ability is, it has also led to the need for local expertise and resources to set up and maintain these clusters. The software must execute efficiently both on smaller local clusters and on larger ones. These computing environments vary in the number of processors, speed of processing and communication resources, and size and speed of memory throughout the memory hierarchy as well as in the availability of support tools and preferred programming paradigms. Software developed and optimized using a particular computing environment might not be as efficient when it's moved to another one. In this article, we describe a small cluster along with two efforts to improve the efficiency of parallel scientific computation on that cluster. Both approaches modify the dynamic load-balancing step of an adaptive solution procedure to tailor the distribution of data across the cooperating processes. This modification helps account for the heterogeneity and hierarchy in various computing environments.

Published in:

Computing in Science & Engineering  (Volume:7 ,  Issue: 2 )