Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2011-09-23
Computer Science
Distributed, Parallel, and Cluster Computing
11 figures
Scientific paper
The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically 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 improving efficiency using the advanced semantics for Exascale computing.
Adelstein-Lelbach Bryce
Anderson Matthew
Brodowicz Maciej
Dekate Chirag
Kaiser Hartmut
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