Scalable Input/Output

The major research results from the Scalable Input/Output Initiative exploring software and algorithmic solutions to the I/O imbalance.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.