Elements of Parallel Computing

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Address Space
algorithm
algorithm decomposition
Author_Eric Aubanel
Bellman Ford Algorithm
Boundary Vertices
Cache Block
Category=UKC
Category=UKG
Category=UMB
Cellular Automata
communication overhead
Convex Hull
convex hull computation
Eikonal Equation
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eq_computing
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Execution Time
FMM
Ghost Elements
Graham Scan
graph
Load Balance
matrix
Matrix Vector Multiplication
memory
merge
Merge Sort
multiplication
Parallel Algorithm Design
Parallel Computing
Parallel Decomposition
Parallel Loop
Parallel Programs
parallel shortest path algorithms
Planar Convex Hull
Prefix Sum
pseudocode programming models
Sequential Algorithms
shared
shared memory parallelism
Single Source Shortest Path
sort
SPMD Programming
task
Task Graph
task graph analysis
vector

Product details

  • ISBN 9781498727891
  • Weight: 446g
  • Dimensions: 178 x 254mm
  • Publication Date: 06 Dec 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Paperback
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Designed for introductory parallel computing courses at the advanced undergraduate or beginning graduate level, Elements of Parallel Computing presents the fundamental concepts of parallel computing not from the point of view of hardware, but from a more abstract view of algorithmic and implementation patterns. The aim is to facilitate the teaching of parallel programming by surveying some key algorithmic structures and programming models, together with an abstract representation of the underlying hardware. The presentation is friendly and informal. The content of the book is language neutral, using pseudocode that represents common programming language models.

The first five chapters present core concepts in parallel computing. SIMD, shared memory, and distributed memory machine models are covered, along with a brief discussion of what their execution models look like. The book also discusses decomposition as a fundamental activity in parallel algorithmic design, starting with a naive example, and continuing with a discussion of some key algorithmic structures. Important programming models are presented in depth, as well as important concepts of performance analysis, including work-depth analysis of task graphs, communication analysis of distributed memory algorithms, key performance metrics, and a discussion of barriers to obtaining good performance.

The second part of the book presents three case studies that reinforce the concepts of the earlier chapters. One feature of these chapters is to contrast different solutions to the same problem, using select problems that aren't discussed frequently in parallel computing textbooks. They include the Single Source Shortest Path Problem, the Eikonal equation, and a classical computational geometry problem: computation of the two-dimensional convex hull. After presenting the problem and sequential algorithms, each chapter first discusses the sources of parallelism then surveys parallel algorithms.

Eric Aubanel is a Professor in the Faculty of Computer Science at the University of New Brunswick, Fredericton , C anada. His area of research is High Performance Computing. He is part of the IBM Center for Advanced Studies - Atlantic and associated with the Atlantic Computational Excellence Network (ACEnet). His research is funded by NSERC.

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