Opportunistic Data-driven Execution of Parallel Programs for Efficient I/O Services

A parallel system relies on both process scheduling and I/O scheduling for efficient use of resources, and a program's performance hinges on the resource on which it is bottlenecked. Existing process schedulers and I/O schedulers are independent. However, when the bottleneck is I/O, there is an opportunity to alleviate it via cooperation between the I/O and process schedulers: the service efficiency of I/O requests can be highly dependent on their issuance order, which in turn is heavily influenced by process scheduling. We propose a data-driven program execution mode in which process scheduling and request issuance are coordinated to facilitate effective I/O scheduling for high disk efficiency. Our implementation, Dual Par, uses process suspension and resumption, as well as pre-execution and prefetching techniques, to provide a pool of pre-sorted requests to the I/O scheduler. This data-driven execution mode is enabled when I/O is detected to be the bottleneck, otherwise the program runs in the normal computation-driven mode. Dual Par is implemented in the MPICH2 MPI-IO library for MPI programs to coordinate I/O service and process execution. Our experiments on a 120-node cluster using the PVFS2 file system show that Dual Par can increase system I/O throughput by 31% on average, compared to existing MPI-IO with or without using collective I/O.