Abstract
Most scientific computations serve to apply mathematical operations to a set of preconceived data structures, e.g., matrices, vectors, and grids. In this paper, we use a number of widely used matrix computations from the LINPACK library to demonstrate that complex internal organizations of data structures can severely degrade the effectiveness of compilers optimizations. We then present a data layout oblivious optimization methodology, where by isolating an abstract representation of computations from complex implementation details of their data, we enable these computations to be much more accurately analyzed and optimized through varying state-of-the-art compiler technologies.
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