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Sorting has been one of the most challenging studied problems in differentscientific researches. Although many techniques and algorithms have beenproposed on the theory of having efficient parallel sorting implementation,however achieving desired performance on different types of the architectureswith large number of processors is still a challenging issue. Maximizingparallelism level in applications can be achieved by minimizing overheads dueto load imbalance and waiting time due to memory latencies. In this paper, wepresent a distributed sorting algorithm implemented in PGX.D, a fastdistributed graph processing system, which outperforms the Spark's distributedsorting implementation by around 2x-3x by hiding communication latencies andminimizing unnecessary overheads. Furthermore, it shows that the proposed PGX.Dsorting method handles dataset containing many duplicated data entriesefficiently and always results in keeping balanced workloads for differentinput data distribution types.

A Load-Balanced Parallel and Distributed Sorting Algorithm Implemented with PGX.D