Kittitornkun, Surin
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Preferred name
Kittitornkun, Surin
Alternative Name
Kittitornkun, S.
Main Affiliation
Email
surin.ki@kmitl.ac.th
6 results
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Item type:Publication, Performance analysis & improvement of SNPHAP on Multi-core CPUs(2013-09-02); Tongsima, SissadesIn 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multithreading bioinformatics software with OpenMP: SNPHAP case study(2010-12-01); Tongsima, SissadesThis paper presents a parallelization framework for infer- ring haplotypes using an expectation maximization (EM) algorithm. Our framework utilizes GProf profiling tool, OpenMP library, and ompP profiling tool to parallelize the algorithm by determining the hotspot functions, mul- tithreading, and executing them on the Multi-core CPUs. In our experiments, we choose the SNPHAP program for this case study and run it on an 8-core Xeon Linux ma- chine. The results show that our framework can signifi- cantly speedup up to 214% on a large data set with 151 loci of a 10,000 data samples. In addition, deep profiles of multithreaded SNPHAP support our discovery that maxi- mum speedup can be achieved when the number of parallel threads equals to the number of physical cores. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A multithreading methodology with OpenMP on multi-core CPUs: SNPHAP case study(2011-08-12); Tongsima, SissadesThis paper presents a multithreading methodology for OpenMP library. The methodology can be applied to convert existing sequential and demanding programs to be multithreaded programs with OpenMP running on the Multi-core CPUs. In our experiments, we apply this methodology to SNPHAP, which is one of the best haplotype inference bioinformatics program in terms of speed. The results show that our significant achievement is the maximum Speedup 316% for Intel Xeon E5405 (8-core 2.0 GHz) and 410% for Intel Xeon E5520 (8-Core with HyperThreading 2.66GHz) faster than its own sequential version. © 2011 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysing and optimizing snphap using radix-2 computation and openmp(2013-07-12); Tongsima, SissadesIn 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimizing and multithreading SNPHAP on a multi-core APU with OpenCL(2012-09-24); Tongsima, SissadesIn this paper, we have optimized and multithreaded SNPHAP, a bioinformatics program, with OpenCL to reduce the computation time and thus accelerate the execution. Our method is called Radix Comparison algorithm running in sequential and parallel (multithreading). Based on the recent multi-core AMD A6-3650 APU (Accelerated Processing Unit), the achieveable Speedups of Sequential Radix and Parallel Radix SNPHAP compared with the original SNPHAP are 260% and 271%, respectively. © 2012 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MT-ClustalW: Multithreading multiple sequence alignment(2006-01-01); Tongsima, SissadesClustalW is the most widely used tool for aligning multiple protein or nucleotide sequences. The alignment is achieved via three stages: pairwise alignment, guide tree generation and progressive alignment. This paper analyzes and enhances a multithreaded implementation of ClustalW called ClustalW-SMP for higher throughput. Our goal is to maximize the degree of parallelism on multithreading ClustalW called MultiThreading-ClustalW (MT-ClustalW). As a result, bioinformatics laboratories are able to use this MT-ClustalW with much less energy consumption on multicore and SMP (Symmetric Multiprocessor) machines than that of PC clusters. The experiment results show that the MT-ClustalW framework can achieve a considerable speedup over the sequential ClustalW and original multithreaded ClustalW-SMP implementations. © 2006 IEEE.