SC Conference - Activity Details

Multiresolution Analysis, Computational Chemistry, and Implications for High Productivity Parallel Programming

Aniruddha G. Shet  (Oak Ridge National Laboratory)
James Dinan  (Ohio State University)
Robert J. Harrison  (Oak Ridge National Laboratory)
P. Sadayappan  (Ohio State University)
Posters Session
Tuesday,  05:15PM - 07:00PM
Room Rotunda Lobby
Multiresolution Analysis is a technique for approximating a continuous function as a hierarchy of coefficients over a set of basis functions. This hierarchy is naturally represented as a tree data structure which is used to perform fast computations with guaranteed precision by trading numerical accuracy for computation time. Emerging many-core clusters and existing parallel programming tools pose significant challenges to the efficient and scalable implementation of this type of application due to the issues of data distribution, locality, and load balancing. Global view, high productivity programming languages, such as Chapel, provide new tools for expressing and managing irregular and distributed structures, such as those that arise in multiresolution codes. We investigate the benefits and challenges of expressing this class of applications in Chapel through Madness, a framework for multiresolution computational chemistry. Through Madness, we demonstrate the role of key Chapel language features in separating and managing parallel programming concerns.
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