Fast wavelet transform utilizing a multicore-aware framework

Stürmer M, Köstler H, Rüde U (2012)


Publication Language: English

Publication Type: Book chapter / Article in edited volumes

Publication year: 2012

Journal

Publisher: Springer-verlag

Edited Volumes: Applied Parallel and Scientific Computing

Series: Lecture Notes in Computer Science

City/Town: Berlin, Heidelberg, New York

Book Volume: 7134

Pages Range: 313-323

ISBN: 9783642281440

URI: http://link.springer.com/content/pdf/10.1007%2F978-3-642-28145-7_31.pdf

DOI: 10.1007/978-3-642-28145-7_31

Open Access Link: http://link.springer.com/content/pdf/10.1007%2F978-3-642-28145-7_31.pdf

Abstract

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.

Authors with CRIS profile

Related research project(s)

How to cite

APA:

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.

MLA:

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.

BibTeX: Download