Layout-oblivious optimization for matrix computations

Authors:
Huimin Cui;Qing Yi;Jingling Xue;Xiaobing Feng
Affiliations:
Institute of Computing Technology, CAS, Beijing, China;University of Texas at San Antonio, San Antonio, TX, USA;University of New South Wales, Sydney, Australia;Institute of Computing Technology, CAS, Beijing, China
Venue:
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Year:
2012

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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.