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A guide to experimental algorithmics / Catherine C. McGeoch.

By: Material type: TextTextPublication details: Cambridge ; New York : Cambridge University Press, 2012.Description: x, 261 p. : ill. ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781107001732 (hardback)
  • 1107001730 (hardback)
  • 9780521173018 (pbk.)
  • 0521173019 (pbk.)
Subject(s): DDC classification:
  • 005.1 23 M.C.G
LOC classification:
  • QA76.9.A43 M385 2012
Online resources:
Contents:
Machine generated contents note: 1. Introduction; 2. A plan of attack; 3. What to measure; 4. Tuning algorithms, tuning code; 5. The toolbox; 6. Creating analysis-friendly data; 7. Data analysis.
Summary: "Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations, and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I analyze the data? Answering these questions needs ideas from algorithm design and analysis, operating systems and memory hierarchies, and statistics and data analysis. The wide-ranging discussion includes a tutorial on system clocks and CPU timers, a survey of strategies for tuning algorithms and data structures, a cookbook of methods for generating random combinatorial inputs, and a demonstration of variance reduction techniques. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms. A companion website, AlgLab (www.cs.amherst. edu/ccm/alglab) contains downloadable files, programs, and tools for use in projects"--
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Includes bibliographical references and index.

Machine generated contents note: 1. Introduction; 2. A plan of attack; 3. What to measure; 4. Tuning algorithms, tuning code; 5. The toolbox; 6. Creating analysis-friendly data; 7. Data analysis.

"Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations, and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I analyze the data? Answering these questions needs ideas from algorithm design and analysis, operating systems and memory hierarchies, and statistics and data analysis. The wide-ranging discussion includes a tutorial on system clocks and CPU timers, a survey of strategies for tuning algorithms and data structures, a cookbook of methods for generating random combinatorial inputs, and a demonstration of variance reduction techniques. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms. A companion website, AlgLab (www.cs.amherst. edu/ccm/alglab) contains downloadable files, programs, and tools for use in projects"--

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