Unified compiler algorithm for optimizing locality, parallelism and communication in out-of-core computations

This paper presents compiler algorithms to optimize out-of-core programs. These algorithms consider loop and data layout transformations in a unified framework. The performance of an out-of-core loop nest containing many references can be improved by a combination of restructuring the loops and file layouts. This approach considers array references one-by-one and attempts to optimize each reference for parallelism and locality. When there are references for which parallelism optimizations do not work, communication is vectorized so that data transfer can be performed before the innermost tiling loop. Preliminary results from hand-compiles on IBM SP-2 and Intel Paragon show that this approach reduces the execution time, improves the bandwidth speedup and overall speedup. In addition, we extend the base algorithm to work with file layout constraints and show how it can be used for optimizing programs consisting of multiple loop nests.