Improving the scalability of parallel N-body applications with an event-driven constraint-based execution model. Dekate, C., Anderson, M., Brodowicz, M., Kaiser, H., Adelstein-Lelbach, B., & Sterling, T. International Journal of High Performance Computing Applications, 26(3):319-332, Sage Publications Sage UK: London, England, 2012.
Improving the scalability of parallel N-body applications with an event-driven constraint-based execution model [link]Website  abstract   bibtex   
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. © The Author(s) 2012.
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 title = {Improving the scalability of parallel N-body applications with an event-driven constraint-based execution model},
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 year = {2012},
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 keywords = {AS graph,Barnes-Hut,Computer systems programming,Constraint-based,Convention,Scalability,Semantics},
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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. © The Author(s) 2012.},
 bibtype = {article},
 author = {Dekate, Chirag and Anderson, Matthew and Brodowicz, Maciej and Kaiser, Hartmut and Adelstein-Lelbach, Bryce and Sterling, Thomas},
 journal = {International Journal of High Performance Computing Applications},
 number = {3}
}

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