Open AccessBook
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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Journal ArticleDOI
The Queue-Read Queue-Write PRAM Model: Accounting for Contention in Parallel Algorithms
TL;DR: A work-preserving emulation of the queue-read queue-write parallel random access machine model is presented, with only logarithmic slowdown on Valiant's model, and hence on hypercube-type noncombining networks, even when latency, synchronization, and memory granularity overheads are taken into account.
Book ChapterDOI
On External-Memory MST, SSSP, and Multi-way Planar Graph Separation
TL;DR: An improved algorithm for the problem of computing a minimum spanning tree of a general graph is developed, as well as new algorithms for the single source shortest paths and the multi-way graph separation problems on planar graphs.
Proceedings ArticleDOI
A fast GPU algorithm for graph connectivity
TL;DR: This work presents a GPU-optimized implementation for finding the connected components of a given graph, and tries to minimize the impact of irregularity, both at the data level and functional level.
Proceedings ArticleDOI
Can shared-memory model serve as a bridging model for parallel computation?
TL;DR: The Queuing Shared Memory (QSM) model is introduced, which accounts for limited communication bandwidth while still providing a simple shared-memory abstraction, and can potentially serve as viable alternatives to existing message-passing, distributed-memory bridging models.
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.