Annotating user-defined abstractions for optimization

Authors:
Dan Quinlan;Markus Schordan;Richard Vuduc;Qing Yi
Affiliations:
Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Institute of Computer Languages, Vienna University of Technology, Vienna, Austria;Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA;Dept. of Computer Science, University of Texas at San Antonio, San Antonio, TX
Venue:
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Year:
2006

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Abstract

Although conventional compilers implement a wide range of optimization techniques, they frequently miss opportunities to optimize the use of abstractions, largely because they are not designed to recognize and use the relevant semantic information about such abstractions. In this position paper, we propose a set of annotations to help communicate high-level semantic information about abstractions to the compiler, thereby enabling the large body of traditional compiler optimizations to be applied to the use of those abstractions. Our annotations explicitly describe properties of abstractions that are needed to guarantee the applicability and profitability of a broad variety of such optimizations, including memoization, reordering, data layout transformations, and inlining and specialization.