Overview

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.

Full Product Details

Author:   Ken Naono ,  Keita Teranishi ,  John Cavazos ,  Reiji Suda
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2010 ed.
Dimensions:   Width: 15.50cm , Height: 2.10cm , Length: 23.50cm
Weight:   0.599kg
ISBN:  

9781489998842

ISBN 10:   1489998845
Pages:   377
Publication Date:   20 October 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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

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