I/O in Parallel and Distributed Systems
David F. Kotz, Ravi Jain · 1998
One is scientific computing with massive datasets, such as those found in seismic processing, climate modeling, and so forth [dC94]. The second is databases [DG92]. The I/O bottleneck continues to be a serious concern for scientific computing, particularly Grand Challenge problems, where it is now commonly recognized as an obstacle. Many scientific applications generate 1 GB of I/O per run [dC94], and applications performing an order of magnitude more are not uncommon: applications in computational physics and fluid dynamics are projected to require I/O on the order of 1 TB [dC94]. It seems clear that these total I/O requirements will keep increasing as scientists continue to study phenomena at larger space and time scales, and at finer space and time resolutions. Since the response time that humans can tolerate for obtaining computational results--- no matter how comprehensive and detailed--- is always bounded, the I/O rates required will continue to increase also. Thus while curre