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Creators/Authors contains: "Tan, Weida"

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  1. ; ; ; ; ; (, Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming)
    Many computationally expensive problems are solved by a divide-and-conquer algorithm: a problem over a big dataset can be recursively divided into independent tasks over smaller subsets of the dataset. We present a distributed general-purpose framework called T-thinker which effectively utilizes the CPU cores in a cluster by properly decomposing an expensive problem into smaller independent tasks for parallel computation. T-thinker well overlaps CPU processing with network communication, and its superior performance is verified over a re-engineered graph mining system G-thinker available at http://cs.uab.edu/yanda/gthinker/ 
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