Bioinformatics applications are one of the most relevant and compute-demanding applications today. While normally these applications are executed on clusters or dedicated parallel systems, in this work we explore the use of an alternative architecture. We focus on exploiting the compute-intensive characteristics offered by the graphics processors (GPU) in order to accelerate a bioinformatics application. The GPU is a good match for these applications as it is an inexpensive, high-performance SIMD architecture.
In our initial experiments we evaluate the use of a regular graphics card to improve the performance of RAxML, a bioinformatics program for phylogenetic tree inference. In this paper we focus on porting to the GPU the most time-consuming loop, which accounts for nearly 50% of the total execution time. The preliminary results show that the loop code achieves a speedup of 3x while the whole application with a single loop optimization, achieves a speedup of 1.2x.