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We consider the problem of sparse signal recovery from a small number ofrandom projections (measurements). This is a well known NP-hard to solvecombinatorial optimization problem. A frequently used approach is based ongreedy iterative procedures, such as the Matching Pursuit (MP) algorithm. Here,we discuss a fast GPU implementation of the MP algorithm, based on the recentlyreleased NVIDIA CUDA API and CUBLAS library. The results show that the GPUversion is substantially faster (up to 31 times) than the highly optimized CPUversion based on CBLAS (GNU Scientific Library).

Fast GPU Implementation of Sparse Signal Recovery from Random Projections