Next Generation Sequencing (NGS) technologies use parallel sequencing technology to generate a large number of short DNA reads in a single run, allowing for de novo assembly of whole genomes.   One of the key operations needed when performing analysis and de novo assembly is to cluster the sequences based on their similarity, where the sequence similarity is determined using a global alignment algorithm.  Even tough individual alignments are relatively inexpensive, yet computing the pairwise distances requires O(n^2) alignments, which becomes prohibitively expensive for large datasets. We present our updated kernel, in which we solved a crucial performance limitation related to memory usage, and our results from scaling to Stampede, a massive GPU cluster.  By executing our kernel on 32 of Stampede’s NVIDIA K20 GPU.