by Gokhan Memik, Mahmut T. K, Alok Choudhary
http://www.ee.ucla.edu/~memik/papers/selcio.ps
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Abstract:
Current approaches to parallel I/O demand extensive user effort to obtain acceptable performance. This is in part due to difficulties in understanding the characteristics of a wide variety of I/O devices and in part due to inherent complexity of I/O software. While parallel I/O systems provide users with environments where large datasets can be shared between parallel processors, the ultimate performance of I/Ointensive codes depends largely on the relation between data access patterns and storage patterns of data in files and on disks. In cases where access patterns and storage patterns match, we can exploit parallel I/O hardware by allowing each processor to perform independent parallel I/O. To handle the cases in which data access patterns and storage patterns do not match, several I/O optimization techniques have been developed in recent years. Collective I/O is such an optimization technique that enables each processor to do I/O on behalf of
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