Collaborative Research: Application-adaptive I/O Stack for Data-intensive Scientific Computing

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Advances in computational sciences have been greatly accelerated by the rapid growth of high-end computing (HEC) facilities. However, the continuous speedup of end-to-end scientific discovery cycles relies on the ability to store, share, and analyze the terabytes and petabytes of data generated by today's supercomputers. With the growing performance gap between I/O systems and processor/memory units, data storage and accesses are inevitably becoming more bottleneck-prone. In this proposal, we address the I/O stack performance problem with adaptive optimizations at multiple layers of the HEC I/O stack (from high-level scientific data libraries to secondary storage devices and archiving systems), and propose effective communication schemes to integrate such optimizations across layers. In particular, our proposed PATIO (Parallel AdapTive I/O) framework explores multi-layer caching/prefetching that coordinates storage resources ranging from processors to tape archiving systems. This novel approach will bridge existing disjoint optimization efforts at each individual layer and responds to the critical call of improving the overall I/O system performance with increasingly deep HEC I/O stacks.