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    Parallel hybrid dual pivot sorting algorithm
    (2017-11-03)
    Taotiamton, Surapong
    ;
    Kittitornkun, Surin
    Sorting is one of the common problems in Computer Science and data analytics. This paper presents empirical results of parallel Hybrid Dual Pivot Sort (HDPSort) for multicore/manycore CPU systems. HDPSort makes use of both classic Lomuto and Hoare partioning algorithms with two pivot values in parallel. It is developed in C++ with OpenMP 3.0 or better. HDPSort is benchmarked with the sequential STLSort in terms of run time, instruction count and branch load. The Speedups of HDPSort are up to 3.02× and 2.79× faster than the STLSort on 8-core AMD FX-8320 and 4-core Intel i7-2600 Linux systems, respectively. An indepth analysis shows that HDPSort gains the Speedup by 300% over STLSort at the expense of 1%-4% of branch mispredictions.
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    Parallel Partition and Merge QuickSort (PPMQSort) on Multicore CPUs
    (2016-03-01)
    Ranokphanuwat, Ratthaslip
    ;
    Kittitornkun, Surin
    An explosive amount of data has tremendous impacts on sorting, searching, indexing, and so on. Sorting is one of the basic Computer Science problems needed to be fast and efficient to serve Big Data. This paper presents an efficient and scalable algorithm called Parallel Partition and Merge QuickSort (PPMQSort) running on any shared memory/multicore/multi-socket systems. Together with OpenMP 3.0 library, the PPMQSort is developed to be compatible and benchmarked with the fastest C/C++ Stdlib qsort(). The PPMQSort recursively divides an unsorted input array into partially sorted partitions up to Cutoff length using nested multithreading. Finally, those independent partitions are qsort() (conquered) such that no synchronizations are needed. The resulting Speedup of 12.29 (Formula presented.) on a dual-socket 8-core Xeon E5520 can be achieved for sorting random 200 M 32-bit integer data at 16 threads. With the same configuration, a 4-core AMD A6-3600 CPU (non-HyperThread) can reach up to 4.67 (Formula presented.) , a superlinear Speedup. It has been proved that the proposed PPMQSort can exploit all available cache levels and HyperThread CPU cores well thus utilizing up to 83 % and 96 % of CPU on E5520 and A6-3600, respectively.