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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| Vydáno v: | International journal of parallel programming Ročník 34; číslo 3; s. 261 - 317 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer Nature B.V
01.06.2006
Springer Verlag |
| Témata: | |
| ISSN: | 0885-7458, 1573-7640 |
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| Abstract | 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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| AbstractList | 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. We address this challenge 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, (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. The proposed framework relies on a unified polyhedral representation of loops and statements, using normalization rules to allow flexible and expressive transformation sequencing. Thisrepresentation allows to extend the scalability of polyhedral dependence analysis, and to delay the (automatic) legality checks until the end of a transformation sequence. Our work leverages on algorithmic advances in polyhedral code generation and has been implemented in a modern research compiler. 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. |
| Author | Girbal, Sylvain Parello, David Temam, Olivier Cohen, Albert Bastoul, Cédric Sigler, Marc Vasilache, Nicolas |
| Author_xml | – sequence: 1 givenname: Sylvain surname: Girbal fullname: Girbal, Sylvain – sequence: 2 givenname: Nicolas surname: Vasilache fullname: Vasilache, Nicolas – sequence: 3 givenname: Cédric surname: Bastoul fullname: Bastoul, Cédric – sequence: 4 givenname: Albert surname: Cohen fullname: Cohen, Albert – sequence: 5 givenname: David surname: Parello fullname: Parello, David – sequence: 6 givenname: Marc surname: Sigler fullname: Sigler, Marc – sequence: 7 givenname: Olivier surname: Temam fullname: Temam, Olivier |
| BackLink | https://hal.science/hal-01257288$$DView record in HAL |
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| Copyright | Springer Science+Business Media, Inc. 2006 Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | Compiler optimization Automatic parallelization Polyhedral model Semi-automatic program transformation |
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| SubjectTerms | 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 |
| Title | Semi-Automatic Composition of Loop Transformations for Deep Parallelism and Memory Hierarchies |
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