Wiley
Wiley.com
Print this page Share

Algorithms and Parallel Computing

ISBN: 978-0-470-90210-3
364 pages
April 2011
Algorithms and Parallel Computing (0470902108) cover image

Description

There is a software gap between the hardware potential and the performance that can be attained using today's software parallel program development tools. The tools need manual intervention by the programmer to parallelize the code. Programming a parallel computer requires closely studying the target algorithm or application, more so than in the traditional sequential programming we have all learned. The programmer must be aware of the communication and data dependencies of the algorithm or application. This book provides the techniques to explore the possible ways to program a parallel computer for a given application.
See More

Table of Contents

Preface xiii

List of Acronyms xix

1 Introduction 1

1.1 Introduction 1

1.2 Toward Automating Parallel Programming 2

1.3 Algorithms 4

1.4 Parallel Computing Design Considerations 12

1.5 Parallel Algorithms and Parallel Architectures 13

1.6 Relating Parallel Algorithm and Parallel Architecture 14

1.7 Implementation of Algorithms: A Two-Sided Problem 14

1.8 Measuring Benefi ts of Parallel Computing 15

1.9 Amdahl’s Law for Multiprocessor Systems 19

1.10 Gustafson–Barsis’s Law 21

1.11 Applications of Parallel Computing 22

2 Enhancing Uniprocessor Performance 29

2.1 Introduction 29

2.2 Increasing Processor Clock Frequency 30

2.3 Parallelizing ALU Structure 30

2.4 Using Memory Hierarchy 33

2.5 Pipelining 39

2.6 Very Long Instruction Word (VLIW) Processors 44

2.7 Instruction-Level Parallelism (ILP) and Superscalar Processors 45

2.8 Multithreaded Processor 49

3 Parallel Computers 53

3.1 Introduction 53

3.2 Parallel Computing 53

3.3 Shared-Memory Multiprocessors (Uniform Memory Access [UMA]) 54

3.4 Distributed-Memory Multiprocessor (Nonuniform Memory Access [NUMA]) 56

3.5 SIMD Processors 57

3.6 Systolic Processors 57

3.7 Cluster Computing 60

3.8 Grid (Cloud) Computing 60

3.9 Multicore Systems 61

3.10 SM 62

3.11 Communication Between Parallel Processors 64

3.12 Summary of Parallel Architectures 67

4 Shared-Memory Multiprocessors 69

4.1 Introduction 69

4.2 Cache Coherence and Memory Consistency 70

4.3 Synchronization and Mutual Exclusion 76

5 Interconnection Networks 83

5.1 Introduction 83

5.2 Classification of Interconnection Networks by Logical Topologies 84

5.3 Interconnection Network Switch Architecture 91

6 Concurrency Platforms 105

6.1 Introduction 105

6.2 Concurrency Platforms 105

6.3 Cilk++ 106

6.4 OpenMP 112

6.5 Compute Unifi ed Device Architecture (CUDA) 122

7 Ad Hoc Techniques for Parallel Algorithms 131

7.1 Introduction 131

7.2 Defining Algorithm Variables 133

7.3 Independent Loop Scheduling 133

7.4 Dependent Loops 134

7.5 Loop Spreading for Simple Dependent Loops 135

7.6 Loop Unrolling 135

7.7 Problem Partitioning 136

7.8 Divide-and-Conquer (Recursive Partitioning) Strategies 137

7.9 Pipelining 139

8 Nonserial–Parallel Algorithms 143

8.1 Introduction 143

8.2 Comparing DAG and DCG Algorithms 143

8.3 Parallelizing NSPA Algorithms Represented by a DAG 145

8.4 Formal Technique for Analyzing NSPAs 147

8.5 Detecting Cycles in the Algorithm 150

8.6 Extracting Serial and Parallel Algorithm Performance Parameters 151

8.7 Useful Theorems 153

8.8 Performance of Serial and Parallel Algorithms on Parallel Computers 156

9 z-Transform Analysis 159

9.1 Introduction 159

9.2 Definition of z-Transform 159

9.3 The 1-D FIR Digital Filter Algorithm 160

9.4 Software and Hardware Implementations of the z-Transform 161

9.5 Design 1: Using Horner’s Rule for Broadcast Input and Pipelined Output 162

9.6 Design 2: Pipelined Input and Broadcast Output 163

9.7 Design 3: Pipelined Input and Output 164

10 Dependence Graph Analysis 167

10.1 Introduction 167

10.2 The 1-D FIR Digital Filter Algorithm 167

10.3 The Dependence Graph of an Algorithm 168

10.4 Deriving the Dependence Graph for an Algorithm 169

10.5 The Scheduling Function for the 1-D FIR Filter 171

10.6 Node Projection Operation 177

10.7 Nonlinear Projection Operation 179

10.8 Software and Hardware Implementations of the DAG Technique 180

11 Computational Geometry Analysis 185

11.1 Introduction 185

11.2 Matrix Multiplication Algorithm 185

11.3 The 3-D Dependence Graph and Computation Domain D 186

11.4 The Facets and Vertices of D 188

11.5 The Dependence Matrices of the Algorithm Variables 188

11.6 Nullspace of Dependence Matrix: The Broadcast Subdomain B 189

11.7 Design Space Exploration: Choice of Broadcasting versus Pipelining Variables 192

11.8 Data Scheduling 195

11.9 Projection Operation Using the Linear Projection Operator 200

11.10 Effect of Projection Operation on Data 205

11.11 The Resulting Multithreaded/Multiprocessor Architecture 206

11.12 Summary of Work Done in this Chapter 207

12 Case Study: One-Dimensional IIR Digital Filters 209

12.1 Introduction 209

12.2 The 1-D IIR Digital Filter Algorithm 209

12.3 The IIR Filter Dependence Graph 209

12.4 z-Domain Analysis of 1-D IIR Digital Filter Algorithm 216

13 Case Study: Two- and Three-Dimensional Digital Filters 219

13.1 Introduction 219

13.2 Line and Frame Wraparound Problems 219

13.3 2-D Recursive Filters 221

13.4 3-D Digital Filters 223

14 Case Study: Multirate Decimators and Interpolators 227

14.1 Introduction 227

14.2 Decimator Structures 227

14.3 Decimator Dependence Graph 228

14.4 Decimator Scheduling 230

14.5 Decimator DAG for s1 = [1 0] 231

14.6 Decimator DAG for s2 = [1 −1] 233

14.7 Decimator DAG for s3 = [1 1] 235

14.8 Polyphase Decimator Implementations 235

14.9 Interpolator Structures 236

14.10 Interpolator Dependence Graph 237

14.11 Interpolator Scheduling 238

14.12 Interpolator DAG for s1 = [1 0] 239

14.13 Interpolator DAG for s2 = [1 −1] 241

14.14 Interpolator DAG for s3 = [1 1] 243

14.15 Polyphase Interpolator Implementations 243

15 Case Study: Pattern Matching 245

15.1 Introduction 245

15.2 Expressing the Algorithm as a Regular Iterative Algorithm (RIA) 245

15.3 Obtaining the Algorithm Dependence Graph 246

15.4 Data Scheduling 247

15.5 DAG Node Projection 248

15.6 DESIGN 1: Design Space Exploration When s n[1 1]t 249

15.7 DESIGN 2: Design Space Exploration When s n[1 −1]t 252

15.8 DESIGN 3: Design Space Exploration When s = [1 0]t 253

16 Case Study: Motion Estimation for Video Compression 255

16.1 Introduction 255

16.2 FBMAs 256

16.3 Data Buffering Requirements 257

16.4 Formulation of the FBMA 258

16.5 Hierarchical Formulation of Motion Estimation 259

16.6 Hardware Design of the Hierarchy Blocks 261

17 Case Study: Multiplication over GF(2m) 267

17.1 Introduction 267

17.2 The Multiplication Algorithm in GF(2m) 268

17.3 Expressing Field Multiplication as an RIA 270

17.4 Field Multiplication Dependence Graph 270

17.5 Data Scheduling 271

17.6 DAG Node Projection 273

17.7 Design 1: Using d1 = [1 0]t 275

17.8 Design 2: Using d2 = [1 1]t 275

17.9 Design 3: Using d3 = [1 −1]t 277

17.10 Applications of Finite Field Multipliers 277

18 Case Study: Polynomial Division over GF(2) 279

18.1 Introduction 279

18.2 The Polynomial Division Algorithm 279

18.3 The LFSR Dependence Graph 281

18.4 Data Scheduling 282

18.5 DAG Node Projection 283

18.6 Design 1: Design Space Exploration When s1 = [1 −1] 284

18.7 Design 2: Design Space Exploration When s2 = [1 0] 286

18.8 Design 3: Design Space Exploration When s3 = [1 −0.5] 289

18.9 Comparing the Three Designs 291

19 The Fast Fourier Transform 293

19.1 Introduction 293

19.2 Decimation-in-Time FFT 295

19.3 Pipeline Radix-2 Decimation-in-Time FFT Processor 298

19.4 Decimation-in-Frequency FFT 299

19.5 Pipeline Radix-2 Decimation-in-Frequency FFT Processor 303

20 Solving Systems of Linear Equations 305

20.1 Introduction 305

20.2 Special Matrix Structures 305

20.3 Forward Substitution (Direct Technique) 309

20.4 Back Substitution 312

20.5 Matrix Triangularization Algorithm 312

20.6 Successive over Relaxation (SOR) (Iterative Technique) 317

20.7 Problems 321

21 Solving Partial Differential Equations Using Finite Difference Method 323

21.1 Introduction 323

21.2 FDM for 1-D Systems 324

References 331

Index 337

See More

Author Information

Fayez Gebali, PhD, has taught at the University of Victoria since 1984 and has served as the Associate Dean of Engineering for undergraduate programs since 2002. He has contributed to dozens of journals and technical reports and has completed four books. Dr. Gebali's primary research interests include VLSI design, processor array design, algorithms for computer arithmetic, and communication system modeling.
See More

Buy Both and Save 25%!

+

Algorithms and Parallel Computing (US $118.00)

-and- Large-Scale Computing Techniques for Complex System Simulations (US $111.95)

Total List Price: US $229.95
Discounted Price: US $172.46 (Save: US $57.49)

Buy Both
Cannot be combined with any other offers. Learn more.
Back to Top