Software Automatic Tuning
From Concepts to State-of-the-Art Results
Published on 20. October 2014
Book
Paperback/Softback
XIV, 377 pages
978-1-4899-9884-2 (ISBN)
Description
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.
More details
Edition
2010 ed.
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XIV, 377 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 22 mm
Weight
593 gr
ISBN-13
978-1-4899-9884-2 (9781489998842)
DOI
10.1007/978-1-4419-6935-4
Schweitzer Classification
Other editions
Additional editions
Ken Naono | Keita Teranishi | John Cavazos
Software Automatic Tuning
From Concepts to State-of-the-Art Results
Book
09/2010
Springer
€160.49
Shipment within 15-20 days
Ken Naono | Keita Teranishi | John Cavazos
Software Automatic Tuning
From Concepts to State-of-the-Art Results
E-Book
09/2010
1st Edition
Springer
€149.79
Available for download
Content
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.