We'll describe recent work to map comparative genomics algorithms to GPU-accelerated leadership-class systems. The explosion in availability of genomic data holds promise for enabling determination of the genetic causes of phenotypic characteristics, with applications to problems such as the discovery of the genetic roots of diseases. The growing sizes of these datasets and the quadratic and cubic scaling properties of the algorithms necessitate use of leadership-scale accelerated computing. We'll discuss the mapping of two-way and three-way algorithms for comparative genomics calculations to large-scale GPU-accelerated systems. Focusing primarily on the Proportional Similarity metric and the Custom Correlation Coefficient, we'll discuss issues of optimal mapping of the algorithms to GPUs, eliminating redundant calculations due to symmetries, and efficient mapping to many-node parallel systems. We'll also present results scaled to thousands of GPUs on the ORNL Titan system.