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Implementing the Approximate Message Passing (AMP) Algorithm on a GPU

Lukas Cavigelli, Pascal Alexander Hager
ETH
ETH Report, 2012
@article{cavigelli2012implementing,

   title={Implementing the Approximate Message Passing (AMP) Algorithm on a GPU},

   author={Cavigelli, L. and Hager, P.A.},

   year={2012}

}

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We consider the recovery of sparse signals from a limited number of noisy observations using the AMP algorithm. In this paper, we present two fast implementations of this algorithm that run on a CPU and on a GPU and which can either be used for arbitrary unstructured measurement matrices or take advantage of the structure of a DCT matrix to give an even faster implementation. Our results show that for small problem sizes, the CPU based implementation is the fastest, but for large problem sizes, a GPU based implementation has the highest throughput.
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