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Author: search.filters.author.Ranokphanuwat, Ratthaslip

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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.
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    Performance analysis & improvement of SNPHAP on Multi-core CPUs
    (2013-09-02)
    Ranokphanuwat, Ratthaslip
    ;
    Kittitornkun, Surin
    ;
    Tongsima, Sissades
    In this paper, we attempt to analyse this highly computational problem by parallelizing a haplotype inference algorithm, called SNPHAP. The analysis is based on both the orignal (sequential) algorithm and its corresponding run time complexity in Big-O notations. Then, we improve its performance using OpenMP 3.0 and test on a 4-core Intel Core i7-2600 (Hyper-Threading), an 8-core Intel XeonE5405, an 8-core Intel Xeon E5520 (Hyper-Threading) and a 32-core AMD Opteron 8356 Linux machines. The achievements in terms of maximum speedups are 260%, 316%, 410% and 488%, respectively. The factors that affect the speedup of SNPHAP are the specific parallelized code fraction, the suitable OpenMP constructs, the number of physical cores, the sizes of cache memories within/among CPU cores, the clock frequency and finally the memory technology. © 2013 IEEE.
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    Analysing and optimizing snphap using radix-2 computation and openmp
    (2013-07-12)
    Ranokphanuwat, Ratthaslip
    ;
    Rattanatranurak, Apisit
    ;
    Kittitornkun, Surin
    ;
    Tongsima, Sissades
    In this paper, the run time complexity of SNPHAP, which is a haplotype inference tool, is extensively examined. The analysis is based on our previous work in terms of profiling and run-time complexity function. To reduce the run time complexity and enhance its performance, a Radix-2 computation and OpenMP multithreading are applied. The optimized results are compared with both original and compiler optimized versions on an AMD A6-3650 Linux machine. Due to the Radix-2 technique, the complexity is drastically reduced. In addition, the theoretical Speedup is consistent with the experimental one. Furthermore, up to 1,303% Speedup is achievable as a result of OpenMP multithreading.