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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제19권 제6호
- 발행연도
- 2017.1
- 수록면
- 2,853 - 2,862 (10page)
이용수
초록· 키워드
오류제보하기
Genome-wide association study (GWAS) may be characterized by relatively simple calculation for individual single nucleotide polymorphism (SNP) or SNP combinations and rapidly increasing number of such calculation when considering higher order interactions between SNPs. In principle, simultaneous evaluation of association measure for multiple SNP or SNP combinations should expedite the whole study, which coincides with the definition of the parallel computing. Distributing the job to connected and controllable many cpu’s has been called as the clustering. However as the cpu integration technology develops, a single physical cpu can have multiple cores, over which a job may be distributed and executed concurrently. More recently, general purpose graphics processing unit (GPGPU) is drawing attention, which may be characterized by much more cores with less computing powers. Serial codes should be converted into parallel version so as to take advantage of the multi-core central processing unit (CPU) and GPGPU. Using specialized directives or pragmas called openMP and openACC, performance of parallel computing is examined on genomic data in various computing conditions.
목차
등록된 정보가 없습니다.
참고문헌 (22)
참고문헌 신청
Chandrasekaran, S. / 2017 / OpenACC for Programmers / Addison-Weslay
Chapman, B. / 2007 / Using OpenMP: portable shared memory parallel programming / MIT Press
Chen, G. K. / 2013 / Discovering epistasis in large scale genetic association studies by exploiting graphics cards / Frontiers in Genetics 4 : 266 ~
Farber, R. / 2017 / Parallel Programming with OpenACC / Elsevier Inc
Jiang, X. / 2012 / Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality / PLoS ONE 7 (10) : e46771 ~
Chapman, B. / 2007 / Using OpenMP: portable shared memory parallel programming / MIT Press
Chen, G. K. / 2013 / Discovering epistasis in large scale genetic association studies by exploiting graphics cards / Frontiers in Genetics 4 : 266 ~
Farber, R. / 2017 / Parallel Programming with OpenACC / Elsevier Inc
Jiang, X. / 2012 / Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality / PLoS ONE 7 (10) : e46771 ~
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