An efficient algorithm for the run-time parallelization of DOACROSS loops

While automatic parallelization of loops usually relies on compile-time analysis of data dependences, for some loops the data dependences cannot be determined at compile time. An example is loops accessing arrays with subscripted subscripts. To parallelize these loops, it is necessary to perform run...

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Bibliographic Details
Published in:Proceedings of the 1994 ACM/IEEE conference on Supercomputing pp. 518 - 527
Main Authors: Chen, Ding Kai, Torrellas, Josep, Yew, Pen Chung
Format: Conference Proceeding
Language:English
Published: Los Alamitos, CA, USA IEEE Computer Society Press 01.01.1994
Series:ACM Conferences
Subjects:
Computing methodologies
Computing methodologies > Parallel computing methodologies
Computing methodologies > Parallel computing methodologies > Parallel programming languages
General and reference
General and reference > Cross-computing tools and techniques
General and reference > Cross-computing tools and techniques > Performance
Software and its engineering
Software and its engineering > Software notations and tools
Software and its engineering > Software notations and tools > General programming languages
Software and its engineering > Software notations and tools > General programming languages > Language types
Software and its engineering > Software notations and tools > General programming languages > Language types > Parallel programming languages
Theory of computation
Theory of computation > Models of computation
Theory of computation > Models of computation > Concurrency
Theory of computation > Models of computation > Concurrency > Parallel computing models
ISBN:0818666056, 9780818666056
Online Access:Get full text
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Summary:While automatic parallelization of loops usually relies on compile-time analysis of data dependences, for some loops the data dependences cannot be determined at compile time. An example is loops accessing arrays with subscripted subscripts. To parallelize these loops, it is necessary to perform run-time analysis. In this paper, we present a new algorithm to parallelize these loops at run time. Our scheme handles any type of data dependence in the loop without requiring any special architectural support in the multiprocessor. Furthermore, compared to an older scheme with the same generality, our scheme significantly reduces the amount of processor communication required and increases the overlap among dependent iterations. We evaluate our algorithm with parameterized loops running on the 32-processor Cedar shared-memory multiprocessor. The results show speedups over the serial code of up to 14 with the full overhead of run-time analysis and of up to 27 if part of the analysis is reused across loop invocations. Moreover, the algorithm outperforms the older scheme in nearly all cases, reaching speedups of up to times when the loop has many dependences.
ISBN:0818666056
9780818666056
DOI:10.5555/602770.602857