Semi-Automatic Composition of Loop Transformations for Deep Parallelism and Memory Hierarchies

Modern compilers are responsible for translating the idealistic operational semantics of the source program into a form that makes efficient use of a highly complex heterogeneous machine. Since optimization problems are associated with huge and unstructured search spaces, this combinational task is...

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Bibliographic Details
Published in:International journal of parallel programming Vol. 34; no. 3; pp. 261 - 317
Main Authors: Girbal, Sylvain, Vasilache, Nicolas, Bastoul, Cédric, Cohen, Albert, Parello, David, Sigler, Marc, Temam, Olivier
Format: Journal Article
Language:English
Published: New York Springer Nature B.V 01.06.2006
Springer Verlag
Subjects:
Algorithms
Architecture
Communication
Computer Science
Distributed, Parallel, and Cluster Computing
Embedded Systems
Linear programming
Machine learning
Mathematical programming
Optimization
Optimization techniques
Programming Languages
Semantics
Software
Studies
Compiler optimization
Automatic parallelization
Polyhedral model
Semi-automatic program transformation
ISSN:0885-7458, 1573-7640
Online Access:Get full text
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Summary:Modern compilers are responsible for translating the idealistic operational semantics of the source program into a form that makes efficient use of a highly complex heterogeneous machine. Since optimization problems are associated with huge and unstructured search spaces, this combinational task is poorly achieved in general, resulting in weak scalability and disappointing sustained performance. This challenge is addressed by working on the program representation itself, using a semi-automatic optimization approach to demonstrate that current compilers offen suffer from unnecessary constraints and intricacies that can be avoided in a semantically richer transformation framework. Technically, the purpose of this paper is threefold: 1. to show that syntactic code representations close to the operational semantics lead to rigid phase ordering and cumbersome expression of architecture-aware loop transformations, 2. to illustrate how complex transformation sequences may be needed to achieve significant performance benefits, and 3. to facilitate the automatic search for program transformation sequences, improving on classical polyhedral representations to better support operation research strategies in a simpler, structured search space.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-006-0012-3