Scalable input/output : achieving system balance / edited by Daniel A. Reed.

Includes bibliographical references.

Print version record.

I/O [input/output] characterization and analysis / Phyllis E. Crandall, Ruth A. Aydt, Andrew A. Chien and Daniel A. Reed -- Collective I/O and large-scale data management / Alok Choudhary [and others] -- Building parallel database systems for multidimensional data / Chailin Chang, Tahsin M. Kurc, Alan Sussman and Joel Saltz -- ADIO : a framework for high-performance, portable parallel I/O / Rajeev Thakur, William Gropp and Ewing Lusk -- Informed prefetching of collective input/output requests / Tara M. Madhyastha, Gartha A. Gibson, and Christos Faloutsos -- Compiler support for out-of-core arrays on parallel machines / Bradley Broom [and others] -- CLIP : a checkpointing tool for message passing parallel programs / Yuqun Chen, James S. Plank and Kai Li -- Learning to classify parallel I/O access patterns / Tara M. Madhyastha and Daniel A. Reed -- Thread scheduling for out-of-core applications with a memory server / Yuanyuan Zhou, Limin Wang, Douglas W. Clark and Kai Li.

A scalability study of shared virtual memory systems / Yuanyuan Zhou, Liviu Iftode and Kai Li -- Appendix: Proposal for a common parallel file system programming interface / Peter F. Corbett [and others].

As we enter the "decade of data," the disparity between the vast amount of data storage capacity (measurable in terabytes and petabytes) and the bandwidth available for accessing it has created an input/output bottleneck that is proving to be a major constraint on the effective use of scientific data for research. Scalable Input/Output is a summary of the major research results of the Scalable I/O Initiative, launched by Paul Messina, then Director of the Center for Advanced Computing Research at the California Institute of Technology, to explore software and algorithmic solutions to the I/O imbalance. The contributors explore techniques for I/O optimization, including: I/O characterization to understand application and system I/O patterns; system checkpointing strategies; collective I/O and parallel database support for scientific applications; parallel I/O libraries and strategies for file striping, prefetching, and write behind; compilation strategies for out-of-core data access; scheduling and shared virtual memory alternatives; network support for low-latency data transfer; and parallel I/O application programming interfaces.

eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - Worldwide