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An introduction to parallel algorithms

TLDR
This book provides an introduction to the design and analysis of parallel algorithms, with the emphasis on the application of the PRAM model of parallel computation, with all its variants, to algorithm analysis.
Abstract
Written by an authority in the field, this book provides an introduction to the design and analysis of parallel algorithms. The emphasis is on the application of the PRAM (parallel random access machine) model of parallel computation, with all its variants, to algorithm analysis. Special attention is given to the selection of relevant data structures and to algorithm design principles that have proved to be useful. Features *Uses PRAM (parallel random access machine) as the model for parallel computation. *Covers all essential classes of parallel algorithms. *Rich exercise sets. *Written by a highly respected author within the field. 0201548569B04062001

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Citations
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Journal ArticleDOI

Time-optimal visibility-related algorithms on meshes with multiple broadcasting

TL;DR: In this paper, a time-optimal solution for the visibility problem for ordered line segments, disks, and iso-oriented rectangles in the plane is presented, which runs in O(log n) time on a mesh with multiple broadcasting of size n/spl times/n.

Models for Parallel Computing : Review and Perspectives

TL;DR: A review of the wealth of models for parallel comput ation that have evolved over the last decades, together with a perspective on models with potential future relevance.
Proceedings ArticleDOI

Trade-offs between communication throughput and parallel time

TL;DR: In this paper, the effect of limited communication throughput on parallel computation in a setting where the number of processors is much smaller than the length of the input was studied, and an almost matching upper bound of O((n/mp)logO(1)n)n).
Journal ArticleDOI

Parallel three-dimensional mesh generation on distributed memory MIMD computers

TL;DR: An automatic mesh generation technique designed to operate effectively on multiple instruction multiple data (MIMD) parallel computers and a final dynamic repartitioning step is applied at the region level to produce a partition ready for finite element analysis.
References
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Book

Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes

TL;DR: This chapter discusses sorting on a Linear Array with a Systolic and Semisystolic Model of Computation, which automates the very labor-intensive and therefore time-heavy and expensive process of manually sorting arrays.
Book

Computer Architecture and Parallel Processing

Kai Hwang, +1 more
TL;DR: The authors have divided the use of computers into the following four levels of sophistication: data processing, information processing, knowledge processing, and intelligence processing.
Journal ArticleDOI

Data parallel algorithms

TL;DR: The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was previously thought.
Proceedings ArticleDOI

Parallelism in random access machines

TL;DR: A model of computation based on random access machines operating in parallel and sharing a common memory is presented and can accept in polynomial time exactly the sets accepted by nondeterministic exponential time bounded Turing machines.
Journal ArticleDOI

The Parallel Evaluation of General Arithmetic Expressions

TL;DR: It is shown that arithmetic expressions with n ≥ 1 variables and constants; operations of addition, multiplication, and division; and any depth of parenthesis nesting can be evaluated in time 4 log 2 + 10(n - 1) using processors which can independently perform arithmetic operations in unit time.