Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques

170 Pages·1999·4.36 MB·English

by  Shaharuddin Salleh

SCHEDULING IN PARALLEL
COMPUTING SYSTEMS
Fuzzy and Annealing Techniques
THE KLUWER INTERNATIONAL SERIES
IN ENGINEERING DNA COMPUTER SCIENCE
SCHEDULING IN PARALLEL
COMPUTING SYSTEMS
Fuzzy and Annealing Techniques
by
Shaharuddin Salleh
University of Technology Malaysia
Albert Y. Zomaya
The University of Westem Australia
.....
"
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library of Congress Cataloging-in-Publication Data
Salleh Shaharuddin, 1956-
Scheduling in parallel computing systems : fuzzy and annealing
techniques / by Shaharuddin Salleh, Albert Y. Zomaya.
p. cm. -- (Kluwer international series in engineering and
computer science ; SECS 510)
Includes bibliographical references.
ISBN 978-1-4613-7303-2 ISBN 978-1-4615-5065-5 (eBook)
DOI 10.1007/978-1-4615-5065-5
1. Parallel processing (Electronic computers) 2. Fuzzy systems.
3. Simulated annealing (Mathematics) 1. Zomaya, Albert Y.
II. Title. III. Series.
QA76.58.S24 1999
004'.35--dc21 99-24716
CIP
Copyright ® 1999 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1999
Softcover reprint ofthe hardcover Ist edition 1999
AII rights reserved. No part of this publication may be reproduced, stored in a
retrieval system or transmitted in any form or by any means, mechanical, photo
copying, recording, or otherwise, without the prior written permission of the
publisher, Springer Science+Business Media, LLC.
Printed an acid-free paper.
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idea whose time has come. Victor Hugo
Contents
Preface Xl
1 SCHEDULING: SETTING THE SEEN 1
1.1 Introduction 1
5
1.2 Problem Overview
8
1.3 Definitions
1.4 Task Precedence Relationships 12
1.4.1 Objective Function 51
1.5 NP-Completeness and Scheduling 17
1.6 Scope of this Work 18
2 PARALLEL COMPUTING: EXPERIMENTAL PLATFORM 21
2.1 Introduction 22
2.2 Parallel Computers 23
2.3 Transputer-Based Systems 26
2.4 Software Tools for the Transputer 31
2.4.1 Occam 13
2.4.2 Parallel C 32
2.5 FAMTS 34
63
2.6 Summary
3 TASK SCHEDULING: HIGHLIGHTS AND FRAMEWORK 73
73
3.1 List Scheduling Heuristics
93
3.2 Heuristic Clustering Algorithms
04
3.3 Graph Theoretic Approaches
Vlll Contents
3.4 Queuing Theory 41
3.5 A Framework for Experiments 42
3.6 Case Study 46
3.7 Parallel Implementation 48
3.8 Summary 54
4 75
STATIC SCHEDULING: MEAN-FIELD ANNEALING
75
4.1 Neural Networks
4.2 An Overview of Mean-Field Annealing 16
4.3 The Graph Partitioning Problem 86
96
4.4 Minimum Interprocessor Communication
4.5 MFA Model for Minimum Interprocessor Communication 17
57
4.6 Implementation Strategy
4.7 Case Study: A Fully-Connected Network 78
4.8 Different Network Topologies 18
4.8.1 Mesh and Hypercube Networks 38
4.8.2 MFA and Machine-Dependent Factors 86
4.8.3 Simulating TS_MFA-2 88
4.9 Summary 90
5 DYNAMIC SCHEDULING: A FUZZY LOGIC APPROACH 39
5.1 Fuzzy Logic 39
59
5.2 Dynamic Scheduling
69
5.3 A Fuzzy Model for Dynamic Task Allocation
5.3.1 Computing Platform 79
5.3.2 Fuzzy Scheduling 89
5.3.3 Simulation Results 601
5.4 Fuzzy Dynamic Scheduling 112
5.4.1 Simulation Results 116
stnetnoC xi
5.5. Implementation 121
5.6 Summary 125
6
SINGLE-ROW ROUTING: ANOTHER COMPUTATIONALLY-
127
INTRACTABLE .PROBLEM
6.1 Introduction 127
6.2 Solving the SRR Problem 131
1.2.6 yrasseceN dna tneiciffuS snoitidnoC 431
6.3 Existing Methods 137
1.3.6 dohteM yb gnraT et .la )4891( 731
2.3.6 dohteM yb uD dna uiL )7891( 931
6.4 Simulated Annealing 140
1.4.6 ygrenE noitalumroF 141
2.4.6 ASS noitatnemelpmI 241
6.5 Comparisons 145
6.6 Summary 146
7 147
EPILOGUE
7.1 Summary of Findings 147
7.2 Open Issues 150
APPENDIX A: GRAPH MULT IPARTITIONING USING MEAN-FIELD
151
ANNEALING
APPENDIX B: GENERAL LIST HEURISTIC (GL) 153
:C et al.1984) 155
APPENDIX SINGLE ROW ROUTING (TARNG
and 1984) 157
APPENDIX D: SINGLE ROW ROUTING (DU LlU
159
REFERENCES
167
INDEX
Preface
Scheduling si na important problem that arises ni ynam disciplines, hcus sa
economics, ,gnirutcafunam operations ,hcraeser parallel ,gnitupmoc process
control, and ynam .srehto ,gniludehcS ylpmis ,detats sevlovni eht allocations
of sksat ro jobs ot resources ni hcus a yaw that sezimitpo eht esu of these
secruoser according ot emos nevig .airetirc
Over eht sraey a tol of hcraeser sah nekat ecalp dna ynam techniques erew
proposed ot evlos suoirav secnatsni of the scheduling .melborp The
scheduling problem si nwonk ot be etelpmoc-PN (Nondeterministic
Polynomial )emit rof eht lareneg esac dna rof ynam detcirtser .secnatsni
A parallel computer si eno that consists of a collection of processing stinu
that cooperate ot evlos a nevig problem yb gnikrow ylsuoenatlumis no
different strap of that .melborp nI principle, there si on limit ot the number of
snoitca that nac be executed ni ,lellarap ,suht offering na arbitrary eerged of
improvement ni computing ,deeps ,yllaicepse nehw compared ot traditional
.srossecorpinu
Scheduling smelborp ni parallel computing smetsys laed htiw eht gnippam
of sksat .g.e( parts of a )margorp otno na suomonotua target enihcam
consisting of lareves processing ,stnemele os sa ot teem emos performance
sevitcejbo hcus sa minimum noitucexe emit dna elbatpecca daol .gnicnalab
Although ynam saedi erew proposed ot evlos scheduling smelborp rof
uniprocessor ,smetsys ton ynam of these saedi can eb extended ot handle
scheduling smelborp ni parallel computing .smetsys The yticilpitlum of
processors, yromem ,seludom dna other secruoser ni a parallel computing
system increases eht ytixelpmoc of eht problem .ylsuodnemert
The krow presented ni siht book si based no a seires of experiments dna
simulations performed ot yduts eht ytilibaiv of gnisu yzzuF dna Annealing
sdohtem ni gnivlos scheduling smelborp rof parallel computing .smetsys The
krow proposes wen techniques rof both citats dna cimanyd ,gniludehcs using
emerging smgidarap that era :deripsni-yllacigoloib yzzuf ,cigol dleif-naem
,gnilaenna dna simulated .gnilaenna These wen sloot era considered ot eb
"intelligent" esuaceb of their capability ni gnitpada in situ ni esnopser ot
segnahc ni their environment etrhaet w ton predicted ni .ecnavda
Encouraging stluser evah been obtained ni siht book yb using these
.sehcaorppa This snaem that these sdohtem can become elbaiv alternatives ot
xii Preface
classical snoitulos ot eht gniludehcs ,melborp hcihw era yltsom heuristic›
.desab hguohtlA scitsirueh era tsubor dna elbailer ni gnivlos certain secnatsni
of eht scheduling ,melborp yeht t’nod mees ot krow llew nehw eno sdeen ot
obtain snoitulos ot lareneg smrof of eht gniludehcs .melborp
nO eht other ,dnah yzzuf ,cigol laruen ,skrowten detalumis ,gnilaenna
citeneg ,smhtirogla evolutionary computing ,sledom dna other biologically›
inspired seuqinhcet evah neeb yllufsseccus deilppa rof gnivlos a ediw egnar
of combinatorial noitazimitpo .smelborp ehT idea ni gnisu eseht sdohtem ni
siht krow smets morf their sseccus ni gniniatbo lamitpo snoitulos ot other
formidable ,smelborp hcus sa gnilevart namselas dna hparg partitioning
problems, that era yrev similar ni erutan ot eht gniludehcs .melborp
This ,krow hguorht a number of case ,seiduts sepoh ot esaercni eht
awareness ni eht l.ellarap computing ytinummoc of eht potential of hcus wen
smgidarap ni gnivlos computationally intractable .smelborp This could
etareneg erom interest dna concerted effort ni gniyduts eseht smgidarap dna
applying them ot a rediw egnar of lairotanibmoc smelborp taht esira ni
parallel .gnitupmoc
,yllaedI eht reader of eht koob dluohs eb enoemos ohw si familiar htiw
parallel computing dna dluow ekil ot nrael erom tuoba gniludehcs smelborp
rof hcus .smetsys ,revewo Heht koob could eb desu yb a rediw ecneidua hcus
sa graduate ,stneduts senior undergraduate ,stneduts ,srehcraeser instructors,
,dna practitioners, ni Computer Engineering dna .ecneicS eW tried ot ekam
eht lairetam deniatnoc-fles os that eht reader seod ton evah ot consult ynam
external .secnerefer