Improving the scalability of parallel N-body applications with an event-driven constraint-based execution model

Authors:
Chirag Dekate;Matthew Anderson;Maciej Brodowicz;Hartmut Kaiser;Bryce Adelstein-Lelbach;Thomas Sterling
Affiliations:
Department of Computer Science, Louisiana State University, Baton Rouge, LA, USA, Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA;Center for Research in Extreme Scale Technologies, Indiana University, Bloomington, IN, 47408, USA;Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA;Department of Computer Science, Louisiana State University, Baton Rouge, LA, USA, Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA;Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA;Department of Computer Science, Louisiana State University, Baton Rouge, LA, USA
Venue:
International Journal of High Performance Computing Applications
Year:
2012

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Abstract

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends such as multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the parallel execution of graphs that are generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery by using the advanced semantics for exascale computing.