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References in periodicals archive ?
Partial redundancy elimination (PRE) is a powerful optimization technique first developed by Morel and Renvoise [1979].
Computational optimality is an important requirement in partial redundancy elimination, but several early methods, e.g., Morel and Renvoise [1979] and Chow [1983], lacked this property.
Although partial redundancy elimination is not among the optimizations treated by Choi et al.
A new algorithm for partial redundancy elimination based on SSA form.
Register promotion by sparse partial redundancy elimination of loads and stores.
We generalize Knoop et al.'s Lazy Code Motion (LCM) algorithm for partial redundancy elimination so that the generalized version also performs strength reduction.
There are two fundamentally distinct approaches to integrating strength reduction into partial redundancy elimination. One approach (exemplified by our work) treats all computations that have the same net effect (at some point in the flowgraph) as equivalent.
In the alternative approach to integrating strength reduction with partial redundancy elimination, the low-cost update computations are initially ignored.
The majority of prior work on integrating strength reduction with partial redundancy elimination has fallen into this latter category, unlike the approach presented in this article.
Of the above papers focusing on the integration of strength reduction with partial redundancy elimination, Kennedy et al.'s is unique in that it is based on a novel algorithm (SSAPRE) for partial redundancy elimination in static single-assignment (SSA) form [Chow et al.
Turning to our own approach, in which partial redundancy elimination is used to place all computations, not just the full-cost ones, the prior work is decidedly sparser.
There is a body of work on partial redundancy elimination that follows up on precisely this observation, by placing computations that are "speculative" (or in our more prejudicial terminology, unsafe), and using statistical information [[Delta].sub.b]out execution frequencies to optimize over the frequency-weighted collection of paths.