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