Charuwat Houngkaew

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Highly Productive Computing Systems (HPCS) and PGAS languages are considered as important ways in achieving the exascale computational capabilities. Most of the current large graph processing applications are custom developed using non-HPCS/PGAS techniques such as MPI, MapReduce. This paper introduces Scale-Graph, an X10 library targeting billion scale(More)
Many social simulations can be represented using mobile-agent-based model in which agents moving around on a given space such as evacuations, traffic flow and epidemics. Whole planet simulation with billions of agents at microscopic level helps mitigate the global crisis. It introduces new technical challenges such as processing and migrating many agents(More)
Scalable analysis of massive graphs has become a challenging issue in high performance computing environments. ScaleGraph is an X10 library aimed for large scale graph analysis scenarios. This paper evaluates scalability of ScaleGraph library for degree distribution calculation, betweeness centrality, and spectral clustering algorithms. We make scalability(More)
Betweenness centrality is a measure that determines the relative importance of a vertex (or an edge) within a graph based on shortest paths. Recently, large-scale graphs have emerged in many different domains, as social networks, road networks, protein interaction networks, etc., and they are too large to fit into the memory of a single SMP. The algorithm(More)
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