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1996
IEEE

On the Scalability of 2-D Wavelet Transform Algorithms on Fine-grained Parallel Machines

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On the Scalability of 2-D Wavelet Transform Algorithms on Fine-grained Parallel Machines
: We study the scalability of 2-D discrete wavelet transform algorithms on fine-grained parallel architectures. The principal operation in the 2-D DWT is the filtering operation used to implement the filter banks of the 2-D subband decomposition. We demonstrate that there exist combinationsof the machine size, image size, and wavelet size for which the time-domain algorithms outperform the frequency domain algorithms, and vice-versa. We, therefore, demonstrate that a hybrid approach which combines time- and frequency-domain approaches can yield optimal performance for a broad range of problem and machine sizes. Furthermore, we show the effect of processor speed and the use of separable versus nonseparable wavelets on the crossover points between the algorithm approaches.
Jamshed N. Patel, Ashfaq A. Khokhar, Leah H. Jamie
Added 07 Aug 2010
Updated 07 Aug 2010
Type Conference
Year 1996
Where ICPP
Authors Jamshed N. Patel, Ashfaq A. Khokhar, Leah H. Jamieson
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