Software Automatic Tuning (eBook, PDF)

From Concepts to State-of-the-Art Results
Redaktion: Naono, Ken; Suda, Reiji; Cavazos, John; Teranishi, Keita

• Format: PDF

Produktbeschreibung

Software Automatic Tuning: From Concepts to State-of-the-Art Results Ken Naono Keita Teranishi John Cavazos Reiji Suda It is well known that carefully tuned programs run much faster than ones consisting of simply written code, and sometimes the difference of speed is more 100X. To make things more complex, well-tuned code for some machines performs badly on others. "Automatic Performance Tuning" is a technology paradigm that enables software to tune itself to its environments so that it performs well on any computer, even on computers unknown to the programmer. This book summarizes the research efforts to date and state of the art of automatic performance tuning. Software developers and researchers in the area of scientific and technical computing, optimized compilers, high performance systems software, and low-power computing will find this book to be an invaluable reference to this powerful new paradigm. .Presents the first English collaboration on the powerful, new software paradigm of Automatic Performance Tuning; .Offers a comprehensive survey of fundamental concepts and state-of-the-art results from the field; .Enables programmers to create software that will tune itself to its environments so that it performs well on any computer.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 377
• Erscheinungstermin: 9. September 2010
• Englisch
• ISBN-13: 9781441969354
• Artikelnr.: 37357132

Inhaltsangabe

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.