U. Glaeser

External or out-of-core algorithms using parallel data transfers can be traced to the work by Aggarwal
and Vitter [3], generalizing earlier models, which dealt with sequential or nonblocked data transfers. The
model used in that work was more powerful than the PDM that models multiple-disk systems. A number
of out-of-core algorithms for external sorting, computational geometry, FFT data permutations, linear
algebra computations, scientific codes, and data structures for indexing complex multidimensional data
have since been developed [1–3,9,20,21,27,46,56,58]. The reader is referred to [1] and the references
therein for a comprehensive bibliography and discussion of these works.
Run-time environments to increase efficiency and simplify the programming effort in applications
requiring parallel I/O has been addressed by several research groups [6,8,14,16,18,28,31,38,39,42,44,54].
For a detailed discussion of the different proposals the reader is referred to [41,49].
33.10 Conclusion
Parallel I/O systems consisting of multiple concurrent devices are necessary to handle the storage and
bandwidth requirements of modern applications. Parallel I/O hardware and interconnection technology
will continue to evolve to meet the growing demands. New algorithms and system software are essential
to effectively manage the hundreds of richly interconnected concurrent devices. Caching and prefetching
are two fundamental techniques to improve data access performance by exploiting temporal locality and
latency hiding. In a parallel I/O system using these mechanisms effectively involve challenging issues,
which have been extensively studied over the past few years. These have resulted in the design of optimal
algorithms for prefetching and caching, techniques to obtain lookahead of the I/O accesses, external
algorithms for important problems, and file system and I/O primitives to support parallel I/O. As systems
grow larger and more complex, challenging problems to control and manage the parallelism automatically
and effectively will continue to be explored. Building on the fundamental understanding of what works and
the algorithms required to control them, tools to automatically perform configuration, dynamic declustering,
replication, prefetching, and caching will continue to be developed. Finally, although this chapter deals
primarily with disk I/O, it can be readily seen that many of the issues transcend device specificity and
apply in more general contexts dealing with managing and processing multiple concurrent I/O streams,
using limited storage and bandwidth resources, as in embedded system environments.
Acknowledgment
Supported in part by NSF grant CCR-9704562.
References
1. J. M. Abello and J. S. Vitter (Eds.). External Memory Algorithms, Volume 50 of DIMACS Series in
Discrete Mathematics and Theoretical Computer Science. DIMACS, American Mathematical Society,
Providence, RI, 1999.
2. P. K. Agarwal, L. Arge, and J. Erickson. Indexing moving points. In Proceedings ACM SIGACT-
SIGMOD-SIGART Symposium on Principles of Database Systems, May 2000.
3. A. Aggarwal and J. S. Vitter. The input/output complexity of sorting and related problems. Communications
of the ACM, 31(9): 1116–1127, Sep. 1988.
4. D. Aksoy and M. Franklin. RxW: A scheduling approach to large scale on-demand broadcast.
IEEE/ACM Transactions on Networking, 7: 846–861, Dec. 1999.
5. S. Albers, N. Garg, and S. Leonardi. Minimizing stall times in single and parallel disk systems. In
Proceedings of the ACM Symposium on Theory of Computing, pp. 454–462, 1998.
6. T. E. Anderson et al. Serverless network file systems. ACM Transactions on Computer Systems, 14(1):
41–79, Feb. 1996.
7. L. Arge. External-memory algorithms with applications in geographic information systems. In M. van
Kreveld, J. Nievergelt, T. Roos, and P. Windmayer (Eds.). Algorithmic Foundations of GIS, volume
1340, Lecture Notes in Computer Science, Springer-Verlag, 1997.
© 2002 by CRC Press LLC