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Blocking and Array Contraction Across Arbitrarily Nested Loops Using Affine Partitioning (2001)
| Citations: | 80 - 1 self |
BibTeX
@INPROCEEDINGS{Lim01blockingand,
author = {Amy W. Lim and Shih-wei Liao and Monica S. Lam},
title = {Blocking and Array Contraction Across Arbitrarily Nested Loops Using Affine Partitioning},
booktitle = {},
year = {2001},
pages = {103--112},
publisher = {ACM Press}
}
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Abstract
Applicable to arbitrary sequences and nests of loops, affine partitioning is a program transformation framework that unifies many previously proposed loop transformations, including unimodular transforms, fusion, fission, reindexing, scaling and statement reordering. Algorithms based on affine partitioning have been shown to be effective for parallelization and communication minimization. This paper presents algorithms that improve data locality using affine partitioning. Blocking and array contraction are two important optimizations that have been shown to be useful for data locality. Blocking creates a set of inner loops so that data brought into the faster levels of the memory hierarchy can be reused. Array contraction reduces an array to a scalar variable and thereby reduces the number of memory operations executed and the memory footprint. Loop transforms are often necessary to make blocking and array contraction possible.
Keyphrases
array contraction affine partitioning data locality inner loop memory footprint program transformation framework memory hierarchy memory operation unimodular transforms loop transformation loop transforms important optimization arbitrary sequence statement reordering scalar variable communication minimization faster level
