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This book offers a detailed and self-contained presentation for studyi ng loop transformations, the detection of parallel loops, and how to u se them to detect parallelism in a specific program. It provides caref ul explanation and exposition for all parallel-loop algorithms that ha ve been designed recently in a framework of scheduling algorithms on c yclic graphs, primarily task graph scheduling and loop nest scheduling perspectives. The book is an essential text/reference for the latest developments in automatic parallelization methods used for scheduling, compilers, and program transformations. Professionals, researchers an d graduates in computer science, software engineering, and computer en gineering will find it an authoritative resource and reference. It is also suitable for self-study purposes by professionals and practitione rs.

Contents
Preface
Introduction
Part I Unidimensional Problems
1 Scheduling DAGs without Communications
1.1 Introduction .
1.2 Where Do Task Graphs Come From?
1.3 Scheduling DAGs
1.4 Solving Pb(00) . . . . . . . . . . . .
1.5 Solving Pb(P) .
1.5.1 NP-Completeness of Pb(p)
1.5.2 List Heuristics . . . . . . . .
1.5.3 Implementing a List Schedule.
1.5.4 Critical Path Scheduling .
1.6 Conclusion........
1.7 Bibliographical Notes.
1.8 Exercises.........
2 Scheduling DAGs with Communications
2.1 Introduction .
2.2 A Model with Communication Costs . . . . .
2.3 NP-Completeness of Pb(00) . . . . .
2.4 A Guaranteed Heuristic for Pb(00) . .
2.4.1 Favorite Successors .
2.4.2 Hanen and Munier's Heuristic
2.5 List Heuristics for Pb(p) .
2.5.1 Naive Critical Path .
2.5.2 Modified Critical Path . . . . . . .
2.5.3 Hints for Comparison . . . . . . . . . .
2.6 Two-Step Clustering Heuristics . . . . . . . . . . . . . . . . .
2.6.1 Heuristics for the Clustering Phase . . . . . . . . . . .
2.6.2 From Clustering to Scheduling with Resources.
2.6.3 Clustering Epilogue . . . . ..
2.7 Linear Clustering . . . . .....
2.8 Conclusion.......
2.9 Bibliographical Notes.
2.10 Exercises . . . .
3 Cyclic Scheduling
3.1 Introduction .
3.2 Problem Formulation .
3.2.1 An Example ..
3.2.2 Average Cycle Time . . . . .
3.2.3 Playing with the Example . . . . .
3.2.4 Problem Formulation: Summary ...
3.2.5 Lower Bounds for the Average Cycle Time
3.3 Solving BCS(00) . . . . . . . . . . . . . . . . . .
3.3.1 Scheduling Potential Graphs .
3.3.2 The Bellman-Ford Algorithm .
3.3.3 Optimal Schedule for Unlimited Resources
3.4 Solving BCS(p) .
3.4.1 NP-Completeness of BCS(p) .
3.4.2 Loop Compaction. . . . . . . . .
3.4.3 Loop Shifting . . . . . . . . . . . . . . . . .
3.4.4 The Leiserson-Saxe Retiming Algorithm. . . . .
3.4.5 Minimizing the Number of Constraints in A(Gr ) .••
3.5 Bibliographical Notes ....
3.6 Exercises..............................
Part II Multidimensional Problems
4 Systems of Uniform Recurrence Equations
4.1 Introduction .
4.2 Computability of Uniform Recurrence Equations .
4.2.1 Definition of a SURE . . . . . . . . . . . . .
4.2.2 Computability: Definition and Properties .
4.3 URE and Linear Scheduling . . . . . . . . . . . . . . . . 107
4.3.1 Introduction..................... 107
4.3.2 Construction of Dependence Paths . . . . . . . . 109
4.3.3 Computability Criterion for a Single Equation . 113
4.3.4 Optimal Linear Schedules. . . . . . . . . . . . 115
4.3.5 Conclusion 123
4.4 SURE and Multidimensional Scheduling. . . . . . . . 124
4.4.1 Introduction............... 124
4.4.2 Detecting Zero-Weight Cycles. . . . . . . . . . 126
4.4.3 Construction and Properties of G' .. . . . . . 134
4.4.4 Optimal Multidimensional Schedule for a SURE . 137
4.4.5 Conclusion 142
4.5 Bibliographical Notes. . . . . . . . . . . . . . . . . . . . . .. 143
4.6 Exercises . . . . . . . . . . . . . . . . . . . . . 144
4.6.1 Computability.............. 144
4.6.2 URE and Linear Scheduling. . . . . . 144
4.6.3 SURE and Multidimensional Scheduling 145
5 Parallelism Detection in Nested Loops 149
5.1 Introduction....................... 149
5.2 Dependence Analysis and Dependence Abstraction 152
5.2.1 Nested Loops and Dependence Relations . . 152
5.2.2 Dependence Analysis 157
5.2.3 Approximations of Distance Sets . . . 162
5.3 Allen and Kennedy's Algorithm .. 170
5.3.1 Loop Distribution. . . . . . . 171
5.3.2 Basic Algorithm. . . . . . . . 172
5.4 Unimodular Transformations . . . . 176
5.4.1 Definition and Validity. . . . . . . . . 177
5.4.2 The Hyperplane Method (Uniform Loop Nests) 180
5.4.3 The Hyperplane Method (Direction Vectors) . . 183
5.4.4 Wolf and Lam's Algorithm . . . . . . . 185
5.5 Darte and Vivien's Algorithm . . . . . . . . . . . .. 193
5.5.1 Motivation........... . . . . . . . .. 193
5.5.2 Uniformization...... . . . .. 196
5.5.3 Computability of a PRDG . . . . . . . . . . .. 201
5.5.4 Scheduling a PRDG . . . .. 206
5.5.5 Extension to Medium-Grain Parallelization . 214
5.5.6 Link with ALLEN-KENNEDY and WOLF-LAM 215
5.6 Feautrier's Algorithm. . . . . . . . . . . . . . . . . . . 216
5.6.1 Monodimensional Algorithm. . . . . . . . . 217
5.6.2 Extension to Multidimensional Scheduling . . 222
5.6.3 Extension to Other Dependence Representations .
5.6.4 Comparison with DARTE-VIVIEN .
5.7 Optimality .
5.7.1 The Difficulty of Defining an "Optimality" Notion ..
5.7.2 A Formal Definition . . . . . . . . .
5.7.3 Optimality of ALLEN-KENNEDY .
5.7.4 Optimality of WOLF-LAM. . . .
5.7.5 Optimality of DARTE-VIVIEN
5.7.6 Suboptimality of FEAUTRIER
5.7.7 Conclusion ..
Bibliographical Notes.
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