Algorithmic Differentiation of Pragma-Defined Parallel Regions : Differentiating Computer Programs Containing OpenMP

Michael Förster

ISBN 10: 3658075961 / ISBN 13: 9783658075965

Published by Springer Vieweg, 2014

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Numerical programs often use parallel programming techniques such as OpenMP to compute the program's output values as efficient as possible. In addition, derivative values of these output values with respect to certain input values play a crucial role. To achieve code that computes not only the output values simultaneously but also the derivative values, this work introduces several source-to-source transformation rules. These rules are based on a technique called algorithmic differentiation. The main focus of this work lies on the important reverse mode of algorithmic differentiation. The inherent data-flow reversal of the reverse mode must be handled properly during the transformation. The first part of the work examines the transformations in a very general way since pragma-based parallel regions occur in many different kinds such as OpenMP, OpenACC, and Intel Phi. The second part describes the transformation rules of the most important OpenMP constructs.

Seller Inventory # 9783658075965

Bibliographic Details

• Title: Algorithmic Differentiation of Pragma-Defined Parallel Regions : Differentiating Computer Programs Containing OpenMP
• Publisher: Springer Vieweg
• Publication Date: 2014
• Language: English
• ISBN 10: 3658075961
• ISBN 13: 9783658075965
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 23D millimeters 210 millimeters width by 148 millimeters height by 23 millimeters depth
• Weight: 541 grams

About this title

Synopsis

Numerical programs often use parallel programming techniques such as OpenMP to compute the program's output values as efficient as possible. In addition, derivative values of these output values with respect to certain input values play a crucial role. To achieve code that computes not only the output values simultaneously but also the derivative values, this work introduces several source-to-source transformation rules. These rules are based on a technique called algorithmic differentiation. The main focus of this work lies on the important reverse mode of algorithmic differentiation. The inherent data-flow reversal of the reverse mode must be handled properly during the transformation. The first part of the work examines the transformations in a very general way since pragma-based parallel regions occur in many different kinds such as OpenMP, OpenACC, and Intel Phi. The second part describes the transformation rules of the most important OpenMP constructs.

About the Author

Michael Förster is currently Research Associate of the Institute Software and Tools for Computational Engineering, RWTH Aachen University.