Randomized speed-ups in parallel computation. by U. Vishkin

Cover of: Randomized speed-ups in parallel computation. | U. Vishkin

Published by Courant Institute of Mathematical Sciences, New York University in New York .

Written in English

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Book details

SeriesUltracomputer note -- 66
The Physical Object
Pagination25 p.
Number of Pages25
ID Numbers
Open LibraryOL17980234M

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Vishkin, U. Randomized speed-ups in parallel computation. book, Randomized speed-ups in parallel computation, in Proc. 16th Annual ACM Symposium on Theory of Computing, ACM Press, New York, Page Share Cite Suggested Citation: "10 Randomization in Parallel Algorithms.". from book Fundamentals of Computation Theory: International Conference FCT '87 Kazan, USSR, June 22–26, Proceedings.

Randomized Speed-Ups in Parallel Computation. Vishkin, Randomized speed-ups in parallel computation, in: Proc.

16th ACM Symposium on Theory of Computation () [91 J.C. Wyllie, The complexity of parallel computation, Ph.D. esis, Department of Computer Science, Cornell University, Ithaca, NY (). Cited by: Books about Vishkin: Randomized Speed-Ups in Parallel Computation (Classic Reprint) - by Uzi Vishkin; Finding Euler tours in parallel - by Mikhail Atallah and U Vishkin; Optimal parallel pattern matching in strings - Jun 8, by Uzi Vishkin.

Also, parallel pruning in a backtracking algorithm could make it possible for one process to avoid an unnecessary computation because of the prior work of another process.

Amdahl’s Law Amdahl’s Law is a formula for estimating the maximum speedup from an algorithm that is part sequential and part parallel. Buy Randomized Algorithms (Cambridge International Series on Parallel Computation) by Motwani, Rajeev, Raghavan, Prabhakar (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible s: 7. This paper assumes a parallel RAM (random access machine) model which allows both concurrent reads and concurrent writes of a global memory.

The main result is an optimal randomized parallel algorithm for INTEGER_SORT (i.e., for sorting n integers in the range $[1,n]$). This algorithm costs only logarithmic time and is the first known that is optimal: the product of its time and processor. Vishkin. Randomized speed-ups in parallel computation.

In Proc. 16th Annual ACM Symp. on Theory of Computing,I. Bar-On and U. Vishkin. Optimal parallel generation of a computation tree form. In Proc. 13th Annual International Conference on Parallel Processing,K. Mehlhorn and U. Vishkin. Part of the Lecture Notes in Computer Science book series (LNCS, volume ) Abstract.

In this paper we describe a simple parallel algorithm for list ranking. The algorithm is deterministic and runs in O(log n) time on EREW P-RAM with n/log n processor.

The algorithm matches the performance of the Cole-Vishkin [CV86a] algorithm but is simple. Randomized List Ranking – Randomly assign H / T to each compressible node – Compress H. T links Randomized Speed-ups in Parallel Computation. Vishkin U. () ACM Symposium on Theory of Computation.

Round 2: 15 nodes (64% savings). Ultrafast Randomized Parallel Construction- and Approximation Algorithms for Spanning Forests in Dense Graphs. Advances in Randomized Parallel Computing,   Randomized Speed-ups in Parallel Computation.

Vishkin U. () ACM Symposium on Theory of Computation. Round 4: 5 nodes (88% savings) Challenges – Nodes stored on different computers – Nodes can only access direct neighbors – No "Tournament Bracket" Randomized List Ranking – Randomly assign H / T to each. Our main result is an optimal randomized parallel algorithm for INTEGER SORT (i.e., for sorting n integers in the range [1; n]).

Our algorithm costs only logarithmic time and is the first known that is optimal: the product of its time and processor bounds is upper bounded by a. Abstract. We present a new randomized parallel algorithm Randomized speed-ups in parallel computation.

book term matching. Let n be the number of nodes of the directed acyclic graphs (dags) representing the terms to be matched, then our algorithm uses O(log 2 n) parallel time and M(n) processors, where M(n) is the complexity of n by n matrix multiplication.

The number of processors is a significant improvement over previously known. - Buy Randomized Algorithms (Cambridge International Series on Parallel Computation) book online at best prices in India on Read Randomized Algorithms (Cambridge International Series on Parallel Computation) book reviews & author details and more at Free delivery on qualified s: 6.

This book constitutes the refereed proceedings of the 13th Annual International Symposium on Algorithms and Computation, ISAACheld in Vancouver, BC, Canada in November The 54 revised full papers presented together with 3 invited contributions were carefully reviewed and selected from close to submissions.

The papers cover all relevant topics in algorithmics and computation. Other examples of such parallel simple hybrid algorithms are: computation of the maximum of n elements in asymptotic optimal time Θ(log log n) on a CRCW PRAM with n log log n pro- cessors [ A randomized parallel branch-and-bound procedure.

In ~ Proceedings of the 20th Annual ACM Symposium on Theory of Computing. (Chicago, IlL, May ~), ACM, New York,pp. Randomized List Ranking – Randomly assign H / T to each compressible node – Compress H T links Performance – Compress all chains in log(S) rounds Randomized Speed-ups in Parallel Computation.

Vishkin U. () ACM Symposium on Theory of Computation. Constipation often begins in the first year of life. The aim of this study was to assess the effect of fructooligosaccharides (FOS) in the treatment of infants with constipation.

This randomized, double-blind, placebo-controlled clinical trial included infants with constipation who were randomly assigned to one of two parallel groups: FOS or placebo. Either the FOS supplement or the placebo.

We prove a trade-off between the amount of randomness used by an algorithm and its performance, measured by the time it requires to complete its computation with a given failure probability. The trade-off provides a smooth bridge between the deterministic and randomized complexities of the problems.

EDUCATION Year Degree Institution Ph.D. in Computer Science Harvard University (Advisor: John H. Reif) Thesis Title: Randomized Parallel Computation M.E in Automation Indian Institute of Science B.E.

in Electrical Technology Indian Institute of Science in Special Physics. Randomized Speed-ups in Parallel Computation. Vishkin U. () ACM Symposium on Theory of Computation. Round 3: 6 nodes (86% savings) Fast Path Compression Challenges –.

Nodes stored on different computers –. Nodes can only access direct neighbors Randomized List Ranking –. Randomly assign H / T to each. In parallel algorithms, the list ranking problem involves determining the position, or rank, of each item in a linked is, the first item in the list should be assigned the number 1, the second item in the list should be assigned the number 2, etc.

Although it is straightforward to solve this problem efficiently on a sequential computer, by traversing the list in order, it is more. Algorithms and Theory of Computation Handbook is a comprehensive collection of algorithms and data structures that also covers many theoretical issues.

It offers a balanced perspective that reflects the needs of practitioners, including emphasis on applications within discussions on theoretical issues. Chapters include information on finite precision issues as well as discussion of specific 4/5(1).

The principal computation that we consider is the parallel random-access machine (PRAM), in which it is assumed that each processor has random access in unit time to any cell of a global memory.

This model permits the logical structure of parallel computation to be studied in a context divorced from issues of interprocessor communication.

In this paper, we study the parallel execution of two fundamental search methods: backtrack search and branch-and-bound computation. We present universal randomized methods for parallelizing sequential backtracking search and branch-and-bound computation.

These methods execute on message. We address this problem by introducing the notion of parallel homomorphic encryption (PHE)schemes, which are encryption schemes that support computation over encrypted data via evaluation algorithms that can be efficiently executed in parallel.

We also consider delegated PHE schemes which, in addition, can hide the function being evaluated. Graph algorithms, parallel and distributed algorithms, cache-efficient algorithms, algorithmic game theory, sublinear time algorithms.

Computational Complexity: Circuit lower bounds, communication complexity, hardness of approximation. Randomness in Computation. Randomized algorithms, pseudorandomness, expander graphs, error-correcting codes.

In this multicenter, parallel, randomized controlled trial, we evaluated once-daily supplementation with multiple micronutrients and DHA (i.e., multiple micronutrient supplementation, MMS) on maternal biomarkers and infant anthropometric parameters during the second and third trimesters of pregnancy compared with no supplementation.

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. Motwani and Raghavan provide an excellent overview of randomized techniques in algorithm construction, demonstrating their impact on virtually every domain in which computation is done.

This book will surely exert a powerful influence on the way algorithm design is Price: $ Introducation to Parallel Computing is a complete end-to-end source of information on almost all aspects of parallel computing from introduction to architectures to programming paradigms to algorithms to programming standards.

- Selection from Introduction to Parallel Computing, Second Edition [Book]. In parallel and distributed environments, however, load balancing and communication, including disk and network input/output (I/O), can easily dominate computation.

These factors greatly increase the complexity of algorithm design and challenge traditional ways of thinking about the design of parallel and distributed algorithms. Several are randomized. Beyond being the first optimal algorithms for their problems in the model, most are very simple.

1 INTRODUCTION There is a rich history of research on parallel algorithms from the s, or some even earlier. During the 80s and early 90s, there were hundreds of papers and several books and survey articles [19, Chapter 5: Randomized computation Page 3 of 10 A language L2ZPP = RP \coRP has two (poly-time) Monte Carlo algorithms: Nthat never errs when 2=Land N that never errs when 2L.

We run the two algorithms repeatedly in parallel. If Naccepts, we know for sure that 2L. (Nis bound to reject any string outside.) On the other hand, if N rejects.

Chapter 15 (titled "The Structural Decomposition") of the book by Molloy and Reed on Graph coloring and the Probabilistic Method. The Complexity of (Delta + 1) Coloring in Congested Clique, Massively Parallel Computation, and Centralized Local Computation by Chang et al.

(PODC'19). In parallel computing, work stealing is a scheduling strategy for multithreaded computer programs. It solves the problem of executing a dynamically multithreaded computation, one that can "spawn" new threads of execution, on a statically multithreaded computer, with a fixed number of processors (or cores).It does so efficiently both in terms of execution time, memory usage, and inter-processor.

Overview of This Book 17 2 Parallel Models of Computation 19 Introduction 19 The PRAM Model 21 The Boolean Circuit Model 26 Uniform Circuit Families 30 Circuits and PRAMs 33 3 Complexity 38 Search and Decision Problems 38 Complexity Classes There is a common misconception when assessing computers in suggesting that parallel processing increases speed.

This simply isn't true. Parallel processing is intended to increase throughput by addressing queuing delays that may be experienced by "ready" units of work that are waiting for access to the processor.

Each proce. Parallel Algorithm vs Parallel Formulation Randomized Block Distributions 1D/2D/3D. Selective data and computation replication Achieving the above is usually an interplay of decomposition and mapping and is usually done iteratively.

Title: Microsoft PowerPoint - Chapter 3 - Principles of Parallel Algorithm Design. Given a problem, develop a parallel algorithm for it and analyze its time complexity. Explain the use of graph partitioning in parallel computation, give the limitations of the edge cut metric to measure communication cost, and propose improvements to the edge-cut metric.

Given a graph, show how the spectral method partitions the graph.References D. Chiarulli, R. Melhem, J. Teza, G. Gravenstreter, and S. Levitan, Multiprocessor interconnection networks using partitioned optical passive star (pops) topologies and distributed control, Proc. IEEE First International Workshop on Massively Parallel Processing Using Optical Interconnections,pp.

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