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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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Towards Combining Probabilistic and Interval Uncertainty in Engineering Calculations

TL;DR: This paper provides a survey of algorithms for computing various statistics under interval uncertainty and their computational complexity, which includes both known and new algorithms.
Journal ArticleDOI

A Parallel Priority Queue with Constant Time Operations

TL;DR: This data structure is the first to support multi-insertion and multi-decrease key in constant time, and can be implemented on the EREW PRAM and can perform any sequence of operations inO(n) time andO(mlogn) work, being the total number of keyes inserted and/or updated.
Journal ArticleDOI

GPU-based parallel collision detection for fast motion planning

TL;DR: This work presents parallel algorithms to accelerate collision queries for sample-based motion planning that can compute collision-free paths for rigid and articulated models in less than 100 ms for many benchmarks, almost 50–100 times faster than current CPU-based PRM planners.

Suffix trees and their applications in string algorithms

TL;DR: Special emphasis is given to the most recent developments in this area, such as parallel algorithms for suffix tree construction and generalizations of suffix trees to higher dimensions, which are important in multidimensional pattern matching.

Reevaluating Amdahl's Law and Gustafson's Law

TL;DR: It is concluded that the use of the "serial percentage" concept in parallel performance evaluation is misleading and it is suggested that time-based formulations would be the most appropriate for Parallel performance evaluation.
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.