CS: Fast GPU Implementation of Sparse Signal Recovery from Random Projections

This is the second paper using GPUs to speed up the reconstruction. Here the algorithm being implemented is a matching pursuit one. The paper is entitled: Fast GPU Implementation of Sparse Signal Recovery from Random Projections by Mircea Andrecut. The abstract reads:

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

I am adding it to the compressive sensing hardware page.

Credit: NASA/JPL/Space Science Institute, Enceladus Rev 91 Flyby - Skeet Shoot #9 taken on November 1, 2008.