Software Automatic Tuning

From Concepts to State-Of-The-Art Results

By: Ken Naono (Editor), Keita Teranishi (Editor), John Cavazos (Editor)

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Hardcover | 21 September 2010

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Hardcover
396 Pages

Dimensions(cm)
24.13 x 15.88 x 3.18

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Automatic Performance Tuning is a new software paradigm which enables software to be high performance in any computing environment. Its methodologies have been developed over the past decade, and it is now rapidly growing in terms of its scope and applicability, as well as in its scientific knowledge and technological methods. Software developers and researchers in the area of scientific and technical computing, high performance database systems, optimized compilers, high performance systems software, and low-power computing will find this book to be an invaluable reference to this powerful new paradigm.

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Introduction
Software Automatic Tuning: Concepts and State-of-the-Art Results	p. 3
Achievements in Scientific Computing
ATLAS Version 3.9: Overview and Status	p. 19
Autotuning Method for Deciding Block Size Parameters in Dynamically Load-Balanced BLAS	p. 33
Automatic Tuning for Parallel FFTs	p. 49
Dynamic Programming Approaches to Optimizing the Blocking Strategy for Basic Matrix Decompositions	p. 69
Automatic Tuning of the Division Number in the Multiple Division Divide-and-Conquer for Real Symmetric Eigenproblem	p. 87
Automatically Tuned Mixed-Precision Conjugate Gradient Solver	p. 103
Automatically Tuned Sparse Eigensolvers	p. 121
Systematic Performance Evaluation of Linear Solvers Using Quality Control Techniques	p. 135
Application of Alternating Decision Trees in Selecting Sparse Linear Solvers	p. 153
Toward Automatic Performance Tuning for Numerical Simulations in the SILC Matrix Computation Framework	p. 175
Exploring Tuning Strategies for Quantum Chemistry Computations	p. 193
Automatic Tuning of CUDA Execution Parameters for Stencil Processing	p. 209
Static Task Cluster Size Determination in Homogeneous Distributed Systems	p. 229
Evolution to a General Paradigm
Algorithmic Parameter Optimization of the DFO Method with the OPAL Framework	p. 255
A Bayesian Method of Online Automatic Tuning	p. 275
ABCLibScript: A Computer Language for Automatic Performance Tuning	p. 295
Automatically Tuning Task-Based Programs for Multicore Processors	p. 315
Efficient Program Compilation Through Machine Learning Techniques	p. 335
Autotuning and Specialization: Speeding up Matrix Multiply for Small Matrices with Compiler Technology	p. 353
Index	p. 371
Table of Contents provided by Ingram. All Rights Reserved.

Software Automatic Tuning

From Concepts to State-Of-The-Art Results

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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Research and Innovation

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