Collective buffering: Improving parallel I/O performance

Abstract

"Parallel I/O" is the support of a single parallel application run on many nodes; application data is distributed among the nodes, and is read or written to a single logical file, itself spread across nodes and disks. Parallel I/O is a mapping problem from the data layout in node memory to the file layout on disks. Since the mapping can be quite complicated and involve significant data movement, optimizing the mapping is critical for performance. We discuss our general model of the problem, describe four Collective Buffering algorithms we designed, and report experiments testing their performance on an Intel Paragon and an IBM SP2 both housed at NASA Ames Research Center. Our experiments show improvements of up to two order of magnitude over standard techniques and the potential to deliver peak performance with minimal hardware support.