Persistent array access using server-directed I/O

Large multidimensional arrays are a common data type in high-performance scientific applications. Without special techniques for handling access to these arrays, I/O can easily become a large fraction of execution time for applications using these arrays, especially on parallel platforms. We show how to reduce the parallel I/O bottleneck for array data in closely-synchronized SPMD applications on distributed-memory platforms, through the use of server-directed I/O. This method allows array data requests on parallel platforms to be translated into long sequential disk reads and writes, while also minimizing the cost of rearranging data as they move between on-disk and in-memory schemas. We present experimental results from the implementation of server-directed I/O in Panda, showing that for I/O of large arrays, Panda utilizes nearly the maximum throughput of the underlying AIX file system on an IBM SP2. We also discuss Panda´s user interface, an essential factor in Panda´s high performance