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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. 0201548569B04062001read more
Citations
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Proceedings ArticleDOI
Approximate compaction and padded-sorting on exclusive write PRAMs
TL;DR: This work analyzes the time complexity of these problems on CREW and EREW PRAMs (deterministic and randomized) and gets tight lower and upper bounds depending on the size of free space and extends these lower bounds to approximate compaction and compression.
Journal ArticleDOI
Improved parallel algorithms for finding the most vital edge of a graph with respect to minimum spanning tree
TL;DR: In this article, a fast parallel algorithm that computes the most vital edge of a connected, undirected and weighted graph with nvertices and medges in time O(m) was presented.
Book ChapterDOI
A New Computational Model of Bigdata
TL;DR: This paper proposes a theoretical model called Master/Slave Multiprocessor (MSM for short), very similar to a practical system using MapReduce but with additional constraints relevant to BigData processing, and proposes an adaptive MSM model where the master node still has limited working memory but a large secondary storage.
Models for Parallel Computing Review and Perspectives
TL;DR: Today’s confederacy of computers parallelism has now become insidious, so to endow an investigation of the models seems important for parallel computation.
Reconfigurable Computing for Computational Science: A New Focus in High Performance Computing
TL;DR: Reconfigurable computing, or heterogeneous computing, is offering some hope to the scientific computing community as a means to continued growth in computing capability.
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,Faye A. Briggs +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
W. Daniel Hillis,Guy L. Steele +1 more
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
Steven Fortune,James C. Wyllie +1 more
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.