Parallel Mapping Approaches for GNUMAP

Author

Clement, Nathan L. ; Clement, Mark J. ; Snell, Quinn ; Johnson, W. Evan

Author_Institution

Dept. of Comput. Sci., Univ. of Texas at Austin, Austin, TX, USA

fYear

2011

fDate

16-20 May 2011

Firstpage

435

Lastpage

443

Abstract

Mapping short next-generation reads to reference genomes is an important element in SNP calling and expression studies. A major limitation to large-scale whole-genome mapping is the large memory requirements for the algorithm and the long run-time necessary for accurate studies. Several parallel implementations have been performed to distribute memory on different processors and to equally share the processing requirements. These approaches are compared with respect to their memory footprint, load balancing, and accuracy. When using MPI with multi-threading, linear speedup can be achieved for up to 256 processors.

Keywords

biology computing; genomics; message passing; multi-threading; resource allocation; storage management; GNUMAP; MPI; SNP calling; expression study; large-scale whole-genome mapping; linear speedup; load balancing; memory distribution; memory footprint; memory requirement; multithreading; next-generation sequencing; parallel mapping approach; processing requirement sharing; Bioinformatics; Genomics; Heuristic algorithms; Instruction sets; Memory management; Probabilistic logic; Random access memory;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on

Conference_Location

Shanghai

ISSN

1530-2075

Print_ISBN

978-1-61284-425-1

Electronic_ISBN

1530-2075

Type

conf

DOI

10.1109/IPDPS.2011.184

Filename

6008863