SCHEDULE: NOV 12-18, 2011

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Highly Scalable Ab Initio Genomic Motif Identification

SESSION: Application Performance

EVENT TYPE: Paper

TIME: 11:30AM - 12:00PM

AUTHOR(S):Benoit Marchand, Vadimir Bajic, Dinesh Kaushik

ROOM:TCC 303

ABSTRACT:
We present results of scaling an ab initio motif family identification system, Dragon Motif Finder (DMF), to 65536 processor cores of IBM Blue Gene/P. DMF seeks groups of mutually similar polynucleotide patterns within a set of genomic sequences and builds from them various motif families. Such information is of relevance to many problems in life sciences. Prior attempts to scale ab initio motif-finding algorithms were not successful past a handful of nodes. We solve this using a combination of mixed-mode MPI-OpenMP parallel processing, master-slave MPI parallel processing, multi-level workload distribution, multi- level MPI collective operations, and serial optimizations. While the scalability proved excellent, reaching 94% parallel efficiency on 65536 cores relative to 256 cores on a modest-size problem, the final speedup, exceeding 250,000-fold including serial optimization, enables large scale ab initio motif-finding problems to be tackled. Problems that were estimated to take decades to compute can be solved in hours.

Chair/Author Details:

Benoit Marchand - King Abdullah University of Science and Technology

Vadimir Bajic - King Abdullah University of Science & Technology

Dinesh Kaushik - King Abdullah University of Science & Technology

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The full paper can be found in the ACM Digital Library