BPAR: A Bundle-Based Parallel Aggregation Framework for Decoupled I/O Execution

In today's "Big Data" era, developers have adopted I/O techniques such as MPI-IO, Parallel NetCDF and HDF5 to garner enough performance to manage the vast amount of data that scientific applications require. These I/O techniques offer parallel access to shared datasets and together with a set of optimizations such as data sieving and two-phase I/O to boost I/O throughput. While most of these techniques focus on optimizing the access pattern on a single file or file extent, few of these techniques consider cross-file I/O optimizations. This paper aims to explore the potential benefit from cross-file I/O aggregation. We propose a Bundle-based PARallel Aggregation framework (BPAR) and design three partitioning schemes under such framework that targets at improving the I/O performance of a mission-critical application GEOS-5, as well as a broad range of other scientific applications. The results of our experiments reveal that BPAR can achieve on average 2.1x performance improvement over the baseline GEOS-5.