A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Parallel Computation of Wavelet Transforms Using the Lifting Scheme
The Journal of Supercomputing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
IEEE Transactions on Parallel and Distributed Systems
Fast tridiagonal solvers on the GPU
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Fast adaptive wavelet packet image compression
IEEE Transactions on Image Processing
Lifting factorization-based discrete wavelet transform architecture design
IEEE Transactions on Circuits and Systems for Video Technology
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The split-and-merge method is an algorithm design paradigm sometimes used in the field of parallel computing. It is applied to multilevel algorithms such as the wavelet transforms and some tridiagonal system solvers. In this paper we present the application of the method in the context of general purpose computation on GPUs. The split-and-merge method allows us to efficiently use the CUDA parallel programming model, where a multithreaded program is partitioned into blocks of threads that execute independently from each other. Thus we can solve the data dependency problem at the block boundaries and efficiently take advantage of the memory hierarchy of the GPU. The results obtained show a significant acceleration compared with the direct implementation of the algorithms on the GPU.