Advances in Randomized Parallel Computing

1. Front Matter

   Pages i-xxvi

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2. Optimal Bounds on Tail Probabilities: A Study of an Approach

   • Aviad Cohen, Yuri Rabinovich, Assaf Schuster, Hadas Shachnai

   Pages 1-24

3. A Survey of Randomness and Parallism in Comparison Problems

   • Danny Krizanc

   Pages 25-39

4. Random Sampling Techniques in Parallel Algorithms

   • Rajeev Raman

   Pages 41-66

5. Randomized Algorithms on the Mesh

   • Lata Narayanan

   Pages 67-83

6. Efficient Randomized Algorithms for Parallel and Distributed Machines

   • David S. L. Wei, Kishirasagar Naik

   Pages 85-111

7. Ultrafast Randomized Parallel Construction- and Approximation Algorithms for Spanning Forests in Dense Graphs

   • Anders Dessmark, Carsten Dorgerloh, Andrzej Lingas, Jürgen Wirtgen

   Pages 113-132

8. Parallel Randomized Techniques for Some Fundamental Geometric Problems

   • Suneeta Ramaswami

   Pages 133-157

9. Capturing the Connectivity of High-Dimensional Geometric Spaces by Parallelizable Random Sampling Techniques

   • David Hsu, Jean-Claude Latombe, Rajeev Motwani, Lydia E. Kavraki

   Pages 159-182

10. Randomized Parallel Prefetching and Buffer Management

    • Mahesh Kallahalla, Peter J. Varman

    Pages 183-208

11. Parallel Algorithms for Finite Automata Problems

    • B. Ravikumar

    Pages 209-239

12. High Performance Linear Algebra Package - LAPACK90

    • Jack Dongarra, Jerzy Waśniewski

    Pages 241-253

13. Back Matter

    Pages 254-286

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The technique of randomization has been employed to solve numerous prob­ lems of computing both sequentially and in parallel. Examples of randomized algorithms that are asymptotically better than their deterministic counterparts in solving various fundamental problems abound. Randomized algorithms have the advantages of simplicity and better performance both in theory and often in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis of algorithms, at least three different measures of performance can be used: the best case, the worst case, and the average case. Often, the average case run time of an algorithm is much smaller than the worst case. 2 For instance, the worst case run time of Hoare's quicksort is O(n ), whereas its average case run time is only O( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) average run time for quicksort is that each input permutation is equally likely. Clearly, any average case analysis is only as good as how valid the assumption made on the input space is. Randomized algorithms achieve superior performances without making any assumptions on the inputs by making coin flips within the algorithm. Any analysis done of randomized algorithms will be valid for all p0:.sible inputs.

• algorithms
• parallel computing
• parallelism
• performance
• Quicksort
• randomized algorithm

• University of Florida, Gainesville, USA

  Panos M. Pardalos, Sanguthevar Rajasekaran

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