  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.
