TITLE

Parallel Genome-Wide Analysis With Central And Graphic Processing Units (SCOPUS)

TYPE

Seminars

Abstract

The Indonesia Colorectal Cancer Consortium (IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the MapReduce and Compute Unified Device Architecture (CUDA) frameworks and evaluate performance (speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data (1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620 (6 cores) and NVIDIA Tesla K20 over sequential processing.

Citation

Kacamarga M. F, Baurley J. W and Pardamean B. (2016). Parallel Genome-Wide Analysis With Central And Graphic Processing Units (SCOPUS). 2015 IEEE International Conference on Computer and Communications(ICCC 2015), 265-269. Chengdu, China: IEEE

Keywords

CUDA; Genome-wide Analysis; MapReduce; Parallel Programming

Published On

2015 IEEE International Conference on Computer and Communications(ICCC 2015)

Author

  • James W. Baurley
  • Bens Pardamean

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