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Part 1: For Instructors
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Part 1: For Instructors
by Charles C Weems, Alan Sussman, Arnold L Rosenberg, Anshul Gupta, Sushil K Prasad
Topics in Parallel and Distributed Computing
Cover image
Title page
Table of Contents
Copyright
Contributors
Editor and author biographical sketches
Editors
Authors
Symbol or phrase
Chapter 1: Editors’ introduction and road map
Abstract
1.1 Why this book?
1.2 Chapter introductions
1.3 How to find a topic or material for a course
1.4 Invitation to write for volume 2
Part 1: For Instructors
Chapter 2: Hands-on parallelism with no prerequisites and little time using Scratch
Abstract
2.1 Contexts for application
2.2 Introduction to scratch
2.3 Parallel computing and scratch
2.4 Conclusion
Chapter 3: Parallelism in Python for novices
Abstract
3.1 Introduction
3.2 Background
3.3 Student prerequisites
3.4 General approach: parallelism as a medium
3.5 Course materials
3.6 Processes
3.7 Communication
3.8 Speedup
3.9 Further examples using the Pool/map paradigm
3.10 Conclusion
Chapter 4: Modules for introducing threads
Abstract
4.1 Introduction
4.2 Prime counting
4.3 Mandelbrot
Chapter 5: Introducing parallel and distributed computing concepts in digital logic
Abstract
5.1 Number representation
5.2 Logic gates
5.3 Combinational logic synthesis and analysis
5.4 Combinational building blocks
5.5 Counters and registers
5.6 Other digital logic topics
Chapter 6: Networks and MPI for cluster computing
Abstract
6.1 Why message passing/MPI?
6.2 The message passing concept
6.3 High-performance networks
6.4 Advanced concepts
Part 2: For Students
Chapter 7: Fork-join parallelism with a data-structures focus
Abstract
Acknowledgments
7.1 Meta-introduction: an instructor’s view of this material
7.2 Introduction
7.3 Basic fork-join parallelism
7.4 Analyzing fork-join algorithms
7.5 Fancier fork-join algorithms: prefix, pack, sort
Chapter 8: Shared-memory concurrency control with a data-structures focus
Abstract
8.1 Introduction
8.2 The programming model
8.3 Synchronization with locks
8.4 Race conditions: bad interleavings and data races
8.5 Concurrency programming guidelines
8.6 Deadlock
8.7 Additional synchronization primitives
Acknowledgments
Chapter 9: Parallel computing in a Python-based computer science course
Abstract
9.1 Parallel programming
9.2 Parallel reduction
9.3 Parallel scanning
9.4 Copy-scans
9.5 Partitioning in parallel
9.6 Parallel quicksort
9.7 How to perform segmented scans and reductions
9.8 Comparing sequential and parallel running times
Chapter 10: Parallel programming illustrated through Conway’s Game of Life
Abstract
10.1 Introduction
10.2 Parallel variants
10.3 Advanced topics
10.4 Summary
Appendix A: Chapters and topics
Index
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For Instructors
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