Skip to Main Content
With the explosive growth of genomic information, the searching of sequence databases has emerged as one of the most computation and data-intensive scientific applications. Our previous studies suggested that parallel genomic sequence-search possesses highly irregular computation and I/O patterns. Effectively addressing these runtime irregularities is thus the key to designing scalable sequence-search tools on massively parallel computers. While the computation scheduling for irregular scientific applications and the optimization of noncontiguous file accesses have been well-studied independently, little attention has been paid to the interplay between the two. In this paper, we systematically investigate the computation and I/O scheduling for data-intensive, irregular scientific applications within the context of genomic sequence search. Our study reveals that the lack of coordination between computation scheduling and I/O optimization could result in severe performance issues. We then propose an integrated scheduling approach that effectively improves sequence-search throughput by gracefully coordinating the dynamic load balancing of computation and high-performance noncontiguous I/O.