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Code optimization techniques in source transformations for interpreted languages. (English) Zbl 1156.68621

Bischof, Christian H. (ed.) et al., Advances in automatic differentiation. Selected papers based on the presentations at the 5th international conference on automatic differentiation, Bonn, Germany, August 11–15, 2008. Berlin: Springer (ISBN 978-3-540-68935-5/pbk). Lecture Notes in Computational Science and Engineering 64, 223-233 (2008).
Summary: A common approach to implement Automatic Differentiation (AD) is based on source-to-source transformation. In contrast to the standard case in mathematical software that is concemed with compiled languages, AD for interpreted languages is considered. Here, techniques to improve code performance are introduced in transformations on a high-level rather than by an optimizing compiler carrying out these transformations on a lower-level intermediate representation. The languages MATLAB and CapeML are taken as examples to demonstrate these issues and quantify performance differences of codes generated by the AD tools ADiMat and ADiCape using the five code optimization techniques constant tolding, loop unrolling, constant propagation, forward substitution, and common subexpression elimination.
For the entire collection see [Zbl 1143.65003].

MSC:

68W30 Symbolic computation and algebraic computation
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