Characterizing parallel file-access patterns on a large-scale multiprocessor

Rapid increases in the computational speeds of multiprocessors have not been matched by corresponding performance enhancements in the I/O subsystem. To satisfy the large and growing I/O requirements of some parallel scientific applications, we need parallel file systems that can provide high-bandwidth and high-volume data transfer between the I/O subsystem and thousands of processors. Design of such high-performance parallel file systems depends on a thorough grasp of the expected workload. So far there have been no comprehensive usage studies of multiprocessor file systems. Our CHARISMA project intends to fill this void. The first results from our study involve an iPSC/860 at NASA Ames. This paper presents results from a different platform, the CM-5 at the National Center for SupercomputingApplications. The CHARISMA studies are unique because we collect information about every individual read and write request and about the entire mix of applications running on the machines. The results of our trace analysis lead to recommendations for parallel file system design. First, the file system should support efficient concurrent access to many files, and I/O requests from many jobs under varying load conditions. Second, it must efficiently manage large files kept open for long periods. Third, it should expect to see small requests, predominantly sequential access patterns, application-wide synchronous access, no concurrent file-sharing between jobs, appreciable byte and block sharing between processes within jobs, and strong interprocess locality. Finally, the trace data suggest that node-level write caches and collective I/O request interfaces may be useful in certain environments. This work was supported in part by, the National Science Foundation under grant number CCR-9113170, the National Center for Supercomputing Applications, NASA Ames Research Center under agreement number NCC 2-849, and Thinking Machines Corporation. Duke University, email: [ap,carla]@cs.duke.edu Dartmouth College, email: [dfk,nils]@cs.dartmouth.edu M.I.T, email: [email protected], also affiliated with Thinking Machines Corporation