Fast wavelet transform utilizing a multicore-aware framework

Beitrag in einem Sammelwerk

Details zur Publikation

Autorinnen und Autoren: Stürmer M, Köstler H, Rüde U
Herausgeber: Jonasson K
Titel Sammelwerk: Applied Parallel and Scientific Computing
Verlag: Springer-verlag
Verlagsort: Berlin, Heidelberg, New York
Jahr der Veröffentlichung: 2012
Titel der Reihe: Lecture Notes in Computer Science
Band: 7134
Seitenbereich: 313-323
ISBN: 9783642281440
ISSN: 0302-9743
Sprache: Englisch


The move to multicore processors creates new demands on software development in order to profit from the improved capabilities. Most important, algorithm and code must be parallelized wherever possible, but also the growing memory wall must be considered. Additionally, high computational performance can only be reached if architecture-specific features are made use of. To address this complexity, we developed a C++ framework that simplifies the development of performance-optimized, parallel, memory-efficient, stencil-based codes on standard multicore processors and the heterogeneous Cell processor developed jointly by Sony, Toshiba, and IBM. We illustrate the implementation and optimization of the Fast Wavelet Transform and its inverse for Haar wavelets within our hybrid framework, using OpenMP, and using the Open Compute Language, and analyze performance results for different platforms. © 2012 Springer-Verlag.

FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Köstler, Harald Prof. Dr.
Lehrstuhl für Informatik 10 (Systemsimulation)
Rüde, Ulrich Prof. Dr.
Lehrstuhl für Informatik 10 (Systemsimulation)
Stürmer, Markus
Lehrstuhl für Informatik 10 (Systemsimulation)


Stürmer, M., Köstler, H., & Rüde, U. (2012). Fast wavelet transform utilizing a multicore-aware framework. In Jonasson K (Eds.), Applied Parallel and Scientific Computing. (pp. 313-323). Berlin, Heidelberg, New York: Springer-verlag.

Stürmer, Markus, Harald Köstler, and Ulrich Rüde. "Fast wavelet transform utilizing a multicore-aware framework." Applied Parallel and Scientific Computing. Ed. Jonasson K, Berlin, Heidelberg, New York: Springer-verlag, 2012. 313-323.


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