A Convergence Study of the Discrete FGDLS Algorithm
Summary :
The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.2 pp.673-678
- Publication Date
- 2006/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.2.673
- Type of Manuscript
- Special Section PAPER (Special Section on Parallel/Distributed Computing and Networking)
- Category
- Parallel/Distributed Algorithms
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Sabin TABIRCA, Tatiana TABIRCA, Laurence T. YANG, "A Convergence Study of the Discrete FGDLS Algorithm" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 2, pp. 673-678, February 2006, doi: 10.1093/ietisy/e89-d.2.673.
Abstract: The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.2.673/_p
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@ARTICLE{e89-d_2_673,
author={Sabin TABIRCA, Tatiana TABIRCA, Laurence T. YANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Convergence Study of the Discrete FGDLS Algorithm},
year={2006},
volume={E89-D},
number={2},
pages={673-678},
abstract={The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.},
keywords={},
doi={10.1093/ietisy/e89-d.2.673},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - A Convergence Study of the Discrete FGDLS Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 673
EP - 678
AU - Sabin TABIRCA
AU - Tatiana TABIRCA
AU - Laurence T. YANG
PY - 2006
DO - 10.1093/ietisy/e89-d.2.673
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2006
AB - The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.
ER -
English
