NDLI: An Ultrafast Scalable Many-Core Motif Discovery Algorithm for Multiple GPUs

Content Provider	IEEE Xplore Digital Library
Author	Yongchao Liu Schmidt, B. Maskell, D.L.
Copyright Year	2011
Abstract	The identification of genome-wide transcription factor binding sites is a fundamental and crucial problem to fully understand the transcriptional regulatory processes. However, the high computational cost of many motif discovery algorithms heavily constraints their application for large-scale datasets. The rapid growth of genomic sequences and gene transcription data further deteriorates the situation and establishes a strong requirement for time-efficient scalable motif discovery algorithms. The emergence of many-core architectures, typically CUDA-enabled GPUs, provides an opportunity to reduce the execution time by an order of magnitude without the loss of accuracy. In this paper, we present mCUDA-MEME, an ultrafast scalable many-core motif discovery algorithm for multiple GPUs based on the MEME algorithm. Our algorithm is implemented using a hybrid combination of the CUDA, OpenMP and MPI parallel programming models in order to harness the powerful compute capability of modern GPU clusters. At present, our algorithm supports OOPS and ZOOPS models, which are sufficient for most motif discovery applications. mCUDAMEME achieves significant speedups for the starting point search stage (and the overall execution) when benchmarked, using real datasets, against parallel MEME running on 32 CPU cores. Speedups of up to 1.4 (1.1) on a single GPU of a Fermi-based Tesla S2050 quad-GPU computing system and up to 10.8 (8.3) on the eight GPUs of a two Tesla S2050 system were observed. Furthermore, our algorithm shows good scalability with respect to dataset size and the number of GPUs (availability:https://sites.google.com/site/yongchaosoftware/mc uda-meme).
Starting Page	428
Ending Page	434
File Size	707693
Page Count	7
File Format	PDF
ISBN	9781612844251
ISSN	15302075
DOI	10.1109/IPDPS.2011.183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Instruction sets Computational modeling Clustering algorithms Computer architecture Silicon Kernel Graphics processing unit
Content Type	Text
Resource Type	Article

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