gcn.MOPS: Accelerating cn.MOPS with GPU | Zendy

Moh A. Alkhamis | Zendy; Amirali Baniasadi | Zendy

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gcn.MOPS: Accelerating cn.MOPS with GPU

Author(s) -

Moh A. Alkhamis,

Amirali Baniasadi

Publication year - 2019

Publication title -

epic series in computing

Language(s) - English

Resource type - Conference proceedings

ISSN - 2398-7340

DOI - 10.29007/hb5r

Subject(s) - speedup , computer science , parallel computing , central processing unit , cpu shielding , multi core processor , acceleration , process (computing) , single core , parallelism (grammar) , computer hardware , operating system , physics , classical mechanics

cn.MOPS is a frequently cited model-based algorithm used to quantitatively detect copy-number variations in next-generation, DNA-sequencing data. Previous work has implemented the algorithm as an R package and has achieved considerable yet limited performance improvement by employing multi-CPU parallelism (maximum achievable speedup was experimentally determined to be 9.24). In this paper, we propose an alternative mechanism of process acceleration. Using one CPU core and a GPU device in the proposed solution, gcn.MOPS, we achieve a speedup factor of 159 and reduce memory usage by more than half compared to cn.MOPS running on one CPU core.

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