The Panda Array I/O library, created at the University of Illinois, Urbana-Champaign, was built especially to address the needs of high-performance scientific applications. I/O has been one of the most frustrating bottlenecks to high performance for quite some time, and the Panda project is an attempt to ameliorate this problem while still providing the user with a simple, high-level interface. The Galley File System, with its hierarchical structure of files and strided requests, is another attempt at addressing the performance problem. My project was to redesign the Panda Array library for use on the Galley file system. This project involved porting Panda's three main functions: a checkpoint function for writing a large array periodically for 'safekeeping,' a restart function that would allow a checkpointed file to be read back in, and finally a timestep function that would allow the user to write a group of large arrays several times in a sequence. Panda supports several different distributions in both the compute-node memories and I/O-node disks.

We have found that the Galley File System provides a good environment on which to build high-performance libraries, and that the mesh of Panda and Galley was a successful combination.