Scalability of 2-d Wavelet Transform Algorithms: Analytical and Experimental Results on Coarse-grained Parallel Computers

We present analytical and experimental results for the scal-ability of 2-D discrete wavelet transform algorithms on coarse-grained parallel architectures. The principal operation in the 2-D DWT is the ltering operation used to implement the lter banks of the 2-D sub-band decomposition. We derive analytical results comparing time domain and frequency domain parallel algorithms for realizing the lter banks. Experiments on the Intel Paragon validate the analytical results. We demonstrate that there exist combinations of the machine size, image size, and wavelet size for which the time-domain algorithms outperform the frequency domain algorithms, and vice-versa.