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The discrete wavelet transform (DWT) has been widely used in various scientific and engineering fields. However, the enormous computation of DWT caused by multilevel filtering/down-sampling is a bottleneck that limits the application of DWT used in real-time environment where the data size is large. A stream-based parallel computation framework to accelerate the implementation of DWT is presented in this paper, which is based on employing the consumer-level programmable graphics hardware. Simulation results show that, this stream-based parallel computation framework can achieve a significant performance gain on algorithm acceleration comparing with those completely CPU-based solutions for DWT.