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Chapter 1. Introduction and General Concepts. References. Selim Akl, Parallel Computation: Models and Methods, Prentice Hall, 1997, Updated online version available through website.
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Chapter 1 Introduction and General Concepts
References • Selim Akl, Parallel Computation: Models and Methods, Prentice Hall, 1997, Updated online version available through website. • Selim Akl, The Design of Efficient Parallel Algorithms, Chapter 2 in “Handbook on Parallel and Distributed Processing” edited by J. Blazewicz, K. Ecker, B. Plateau, and D. Trystram, Springer Verlag, 2000. • Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar, Introduction to Parallel Computing, 2nd Edition, Addison Wesley, 2003. • Harry Jordan and Gita Alaghband, Fundamentals of Parallel Processing: Algorithms Architectures, Languages, Prentice Hall, 2003. • Michael Quinn, Parallel Programming in C with MPI and OpenMP, McGraw Hill, 2004. • Michael Quinn, Parallel Computing: Theory and Practice, McGraw Hill, 1994 • Barry Wilkenson and Michael Allen, Parallel Programming, 2nd Ed.,Prentice Hall, 2005.
Outline • Need for Parallel & Distributed Computing • Flynn’s Taxonomy of Parallel Computers • Two Main Types of MIMD Computers • Examples of Computational Models • Data Parallel & Functional/Control/Job Parallel • Granularity • Analysis of Parallel Algorithms • Elementary Steps: computational and routing steps • Running Time & Time Optimal • Parallel Speedup • Speedup • Cost and Work • Efficiency • Linear and Superlinear Speedup • Speedup and Slowdown Folklore Theorems • Amdahl’s and Gustafon’s Law
Reasons to Study Parallel & DistributedComputing • Sequential computers have severe limits to memory size • Significant slowdowns occur when accessing data that is stored in external devices. • Sequential computational times for most large problems are unacceptable. • Sequential computers can not meet the deadlines for many real-time problems. • Many problems are distributed in nature and natural for distributed computation