Algorithmic Differentiation of Pragma-Defined Parallel Regions

Michael Förster

ISBN 10: 3658075961 / ISBN 13: 9783658075965

Editore: Springer Fachmedien Wiesbaden Okt 2014, 2014

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 420 pp. Englisch.

Codice articolo 9783658075965

Dati bibliografici

• Titolo: Algorithmic Differentiation of Pragma-Defined Parallel Regions
• Editore: Springer Fachmedien Wiesbaden Okt 2014
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 3658075961
• ISBN 13: 9783658075965
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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