PROJECTION Algorithm for Motif Finding on GPUs
Algorithms & Complexity Lab, Department of Computer Science, University of the Philippines Diliman, sDiliman 1101 Quezon City, Philippines
arXiv:1605.06904 [cs.DC], (23 May 2016)
@article{clemente2016projection,
title={PROJECTION Algorithm for Motif Finding on GPUs},
author={Clemente, Jhoirene B. and Cabarle, Francis George C. and Adorna, Henry N.},
year={2016},
month={may},
archivePrefix={"arXiv"},
primaryClass={cs.DC},
doi={10.1007/978-4-431-54106-6_9}
}
Motif finding is one of the NP-complete problems in Computational Biology. Existing nondeterministic algorithms for motif finding do not guarantee the global optimality of results and are sensitive to initial parameters. To address this problem, the PROJECTION algorithm provides a good initial estimate that can be further refined using local optimization algorithms such as EM, MEME or Gibbs. For large enough input (600-1000 bp per sequence) or for challenging motif finding problems, the PROJECTION algorithm may run in an inordinate amount of time. In this paper we present a parallel implementation of the PROJECTION algorithm in Graphics Processing Units (GPUs) using CUDA. We also list down several major issues we have encountered including performing space optimizations because of the GPU’s space limitations.
May 26, 2016 by hgpu
