MPI collective I/O based on advanced reservations to obtain performance guarantees from shared storage systems

As more data-intensive computing applications are executed on high performance computing clusters, resource contention on the shared storage system attached to the clusters becomes significant. The contention might cause I/O performance degradation and spoil performance improvement of coordinated parallel I/O by the MPI-IO implementation. In order to solve this problem, an advanced reservation approach where storage resources are managed based on the reservations to satisfy the I/O performance requirements, has been proposed. In this paper, we apply the concept of reserved data access to MPI-IO, in particular to Two-Phase collective I/O which is primarily used for I/O aggregation in non-contiguous access by MPI applications. We developed a prototype by using Dynamic-CoMPI which supports further improvement of Two-Phase I/O by using a locality aware strategy, and Papio which is a parallel storage system providing performance reservation functionality. After describing our prototype design and implementation, we show leverage of the concept by comparing our implementation with other existing MPI-IO implementations backed by OrangeFS and Lustre. The evaluation experiment confirms that the optimization benefit of Two-Phase I/O can be preserved by our approach, under the resource contention situation.