Abstract
The Discrete Haar Wavelet Transform has a wide range of applications from signal processing to video and image processing. Data-intensive structure and easy of implementation make Discrete Haar Wavelet Transform convenient to distribute fundamental operations to multi-CPU and multi-GPU systems. In this paper, the wavelet transform was ported in a compute-efficient way to CPU cluster and programmable GPU cluster by utilizing MPI and CUDA respectively. Experimental studies conducted as part of the parallelization strategies for two-dimensional Discrete Haar Wavelet Transform show that the total running time required to process all rows and columns of an image with different size is significantly decreased on the GPU cluster when compared to the its counterparts on a single CPU, single GPU and CPU cluster. Besides the speedup of the GPU based transform, preliminary analysis also showed that the size of the image is an important parameter on the scalability of the GPU cluster.
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