Principles of Parallel Programming
by Lin, Calvin; Snyder, LarryEdition: 1st
Format: Hardcover
Pub. Date: 2008-02-26
Publisher(s): Pearson
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Summary
Written by top researchers Larry Snyder and Calvin Lin, this highly anticipated first edition emphasizes the principles underlying parallel computation, explains the various phenomena, and clarifies why these phenomena represent opportunities or barriers to successful parallel programming.Introduction: Parallelism = Opportunities + Challenges; Introduction: Parallelism = Opportunities + Challenges; Reasoning about Performance; First Steps Towards Parallel Programming; Scalable Algorithmic Techniques; Programming with Threads; Local View Programming Languages; Global View Programming Languages; Assessing Our Knowledge; Future Directions in Parallel Programming; Capstone Project: Designing a Parallel Program.For all readers (particularly software engineers and computer system designers) interested in multi-core architecture and parallel programming.
Author Biography
Lawrence Snyder is Professor of Computer Science and Engineering at the University of Washington in Seattle. He received his PhD from Carnegie Mellon University and has devoted most of his career to parallel computation research, including architecture, algorithms and languages. With Calvin Lin and UW graduate students, he developed the ZPL parallel programming language. He is a fellow of the ACM and IEEE. He is an ardent traveler, enthusiastic theater-goer and occasional skier.
Calvin Lin is an Associate Professor of Computer Sciences at The University of Texas at Austin, where he also serves as Director of the Turing Scholars Honors Program of undergraduate CS majors. He received his PhD from the University of Washington under the supervision of Lawrence Snyder. His current research interests include compilers and micro-architecture. In his spare time, he is an avid ultimate Frisbee player and coach of UT’s Men’s Ultimate Frisbee team.
Calvin Lin is an Associate Professor of Computer Sciences at The University of Texas at Austin, where he also serves as Director of the Turing Scholars Honors Program of undergraduate CS majors. He received his PhD from the University of Washington under the supervision of Lawrence Snyder. His current research interests include compilers and micro-architecture. In his spare time, he is an avid ultimate Frisbee player and coach of UT’s Men’s Ultimate Frisbee team.
Table of Contents
| Introduction: Parallelism = Opportunities + Challenges | |
| The Power and Potential of Parallelism Examining Sequential and Parallel Programs | |
| A Paradigm Shift Parallelism | |
| Using Multiple Instruction Streams | |
| The Goals: Scalable Performance and Portability | |
| Summary | |
| Historical | |
| Context | |
| Exercises | |
| Parallel Computers And Their Model Balancing Machine | |
| Specifics with Portability | |
| A Look at Five Parallel Computers | |
| The RAM: An Abstraction of a Sequential Computer | |
| The PRAM: A Parallel Computer Model | |
| The CTA: A Practical Parallel Computer Model Memory Reference | |
| Mechanisms A Closer Look at Communication | |
| Applying the CTA Model | |
| Summary | |
| Historical Perspective | |
| Exercises | |
| Reasoning about Performance | |
| Introduction | |
| Motivation and Some Basic Concepts | |
| Sources of Performance Loss Parallel Structure | |
| Reasoning about Performance | |
| Performance Trade-Offs Measuring | |
| Performance What should we measure? | |
| Summary | |
| Historical Perspective | |
| Exercises | |
| First Steps Towards Parallel | |
| Programming Task and Data Parallelism Peril-L Count 3s | |
| Example Conceptualizing Parallelism Alphabetizing | |
| Example Comparison of Three Solutions | |
| Summary | |
| Historical Perspective | |
| Exercises | |
| Scalable Algorithmic Techniques | |
| The Inevitability of Trees Blocks of Independent | |
| Computation Schwartzrsquo; Algorithm Assigning | |
| Work To Processes Statically Assigning | |
| Work to Processes Dynamically | |
| The Reduce & Scan Abstractions Trees | |
| Summary | |
| Historical Context | |
| Exercises | |
| Programming with Threads POSIX Threads | |
| Thread Creation and Destruction Mutual Exclusion | |
| Synchronization Safety Issues | |
| Performance Issues | |
| Open MP The Count 3s | |
| Example Semantic Limitations on Reduction Thread | |
| Behavior and Interaction Sections | |
| Summary of OpenMP Java Threads | |
| Summary | |
| Historical Perspectives | |
| Exercises | |
| Local View Programming Languages MPI: The Message Passing Interface | |
| Getting Started Safety Issues | |
| Performance Issues | |
| Co-Array Fortran Unified Parallel C Titanium | |
| Summary | |
| Exercises | |
| Global View Programming Languages | |
| The Z-level Programming Language | |
| Basic Concepts of ZPL Life, An Example Design Principles | |
| Manipulating Arrays Of Different Ranks | |
| Reordering Data With Remap Parallel Execution of ZPL Performance Model | |
| Summary NESL | |
| Historical Context | |
| Exercises | |
| Assessing Our Knowledge | |
| Introduction | |
| Evaluating Existing Approaches Lessons for the Future | |
| Summary | |
| Historical Perspectives | |
| Exercises | |
| Future Directions in Parallel Programming | |
| Attached Processors Grid Computing Transactional Memory | |
| Summary | |
| Exercises | |
| Capstone Project: Designing a Parallel Program | |
| Introduction | |
| Motivation Getting Started | |
| Summary | |
| Historical Perspective | |
| Exercises | |
| More Advanced Concepts | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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