Scalable parallel I/O alternatives for massively parallel partitioned solver systems

With the development of high-performance computing, I/O issues have become the bottleneck for many massively parallel applications. This paper investigates scalable parallel I/O alternatives for massively parallel partitioned solver systems. Typically such systems have synchronized ¿loops¿ and will write data in a well defined block I/O format consisting of a header and data portion. Our target use for such an parallel I/O subsystem is checkpoint-restart where writing is by far the most common operation and reading typically only happens during either initialization or during a restart operation because of a system failure. We compare four parallel I/O strategies: 1 POSIX File Per Processor (1PFPP), a synchronized parallel IO library (syncIO), ¿Poor-Man's¿ Parallel I/O (PMPIO) and a new ¿reduced blocking¿ strategy (rbIO). Performance tests using real CFD solver data from PHASTA (an unstructured grid finite element Navier-Stokes solver) show that the syncIO strategy can achieve a read bandwidth of 6.6GB/Sec on Blue Gene/L using 16K processors which is significantly faster than 1PFPP or PMPIO approaches. The serial ¿token-passing¿ approach of PMPIO yields a 900 MB/sec write bandwidth on 16K processors using 1024 files and 1PFPP achieves 600 MB/sec on 8K processors while the ¿reduced-blocked¿ rbIO strategy achieves an actual writing performance of 2.3GB/sec and perceived/latency hiding writing performance of more than 21,000 GB/sec (i.e., 21TB/sec) on a 32,768 processor Blue Gene/L.