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An introduction to parallel algorithms
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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
Ligra: a lightweight graph processing framework for shared memory
Julian Shun,Guy E. Blelloch +1 more
TL;DR: This paper presents a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write and significantly more efficient than previously reported results using graph frameworks on machines with many more cores.
MonographDOI
Introduction to Parallel Computing
TL;DR: In this article, a comprehensive introduction to parallel computing is provided, discussing theoretical issues such as the fundamentals of concurrent processes, models of parallel and distributed computing, and metrics for evaluating and comparing parallel algorithms, as well as practical issues, including methods of designing and implementing shared-and distributed-memory programs, and standards for parallel program implementation.
Proceedings ArticleDOI
A lightweight infrastructure for graph analytics
TL;DR: This paper argues that existing DSLs can be implemented on top of a general-purpose infrastructure that supports very fine-grain tasks, implements autonomous, speculative execution of these tasks, and allows application-specific control of task scheduling policies.
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Data-Intensive Text Processing with MapReduce
Jimmy Lin,Chris Dyer +1 more
TL;DR: This half-day tutorial introduces participants to data-intensive text processing with the MapReduce programming model using the open-source Hadoop implementation, with a focus on scalability and the tradeoffs associated with distributed processing of large datasets.
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Limits to Parallel Computation: P-Completeness Theory
TL;DR: In providing an up-to-date survey of parallel computing research from 1994, Topics in Parallel Computing will prove invaluable to researchers and professionals with an interest in the super computers of the future.
References
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Proceedings ArticleDOI
Parallel algorithms for the transitive closure and the connected component problems
TL;DR: Parallel programs are presented that determine the transitive closure of a matrix using n 3 processors and connected components of an undirected graph using n 2 processors and in both cases the desired results are obtained in time 0(log2 n).
Journal ArticleDOI
Deterministic Simulations of PRAMs on Bounded Degree Networks
TL;DR: A deterministic algorithm is presented that simulates an arbitrary PRAM step in O((log n \log m)/\log n) time in the worst case on an expander-based network.
Parallel Algorithms for Graph Theoretic Problems
TL;DR: Algorithms of time complexity 0 log-squared n are developed to solve each of the following problems for graphs with n vertices: finding minimum spanning trees, biconnected components, dominators, bridges, cycles, cycle bases, and shortest cycles.