Parallel GWAS Calculation Compared with OpenMP and OpenACC

• 발행기관: 한국자료분석학회
• 저자명: Jaeyong Yee Mira Park
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.19 No.6, 2853~2862쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2017.12.31

영문 초록

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

1. Introduction

2. Methods

3. Analysis

4. Conclusion

키워드

parallel computing genome-wide association openMP openACC