A classification of nested loops parallelization algorithms

Compares three nested loops parallelization algorithms (Allen and Kennedy's algorithm, Wolf and Lam's algorithm and Darte and Vivien's algorithm) that use different representations of distance vectors as input. The authors identify the concepts that make them similar or different. The...

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Bibliographic Details
Published in:1995 INRIA/IEEE Symposium on Emerging Technologies and Factory Automation : proceedings, ETFA '95, Paris, France, October 10-13, 1995 Vol. 1; pp. 217 - 234 vol.1
Main Authors: Darte, A., Vivien, F.
Format: Conference Proceeding
Language:English
Published: Los Alamitos CA IEEE 1995
IEEE Computer Society Press
Subjects:
Algorithm design and analysis
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Concurrent computing
Detection algorithms
Difference equations
Exact sciences and technology
Parallel processing
Phase change random access memory
Pipeline processing
Scheduling algorithm
Software
Software engineering
Theoretical computing
VLIW
Input output
Dependence graphe
Program loop
Parallelization
Classification
Program transformation
ISBN:9780780325357, 0780325354
Online Access:Get full text
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Summary:Compares three nested loops parallelization algorithms (Allen and Kennedy's algorithm, Wolf and Lam's algorithm and Darte and Vivien's algorithm) that use different representations of distance vectors as input. The authors identify the concepts that make them similar or different. The authors study the optimality of each with respect to the dependence analysis it uses. The authors propose well-chosen examples that illustrate the power and limitations of the three algorithms. This study permits the authors to identify which algorithm is the most suitable for a given representation of dependences.
ISBN:9780780325357
0780325354
DOI:10.1109/ETFA.1995.496776