Multiple 1D data parallel wavelet transform

With the new trends in signals analysis for permanent improvement of the investigation systems there is a growing demand for fast and smart parallel algorithms to work on ID long datasets. The idea is to compute the transform coefficients witch usually takes a long processing time even with the fast sequential wavelet algorithms like FWT by I. Daubechies (1992), in a close to real time manner using variant number of processors with minimum communication requirements and optimal load balancing. We propose here a parallel method for the wavelet transform of signals based of the SIMD philosophy applied on distributed memory machines. We implemented and optimized the code to work whatever the number of time entries are by introducing a splitting step and dedicating for tests a number of new generation processors on a parallel Linux cluster.