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

Lukas Cavigelli, Pascal Alexander Hager
ETH Report, 2012

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

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



Download Download (PDF)   View View   Source Source   
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.
VN:F [1.9.22_1171]
Rating: 5.0/5 (1 vote cast)
Implementing the Approximate Message Passing (AMP) Algorithm on a GPU, 5.0 out of 5 based on 1 rating

You must be logged in to post a comment.

* * *

* * *

* * *

Free GPU computing nodes at

Registered users can now run their OpenCL application at We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 11.4
  • SDK: AMD APP SDK 2.8
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 5.0.35, AMD APP SDK 2.8

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to will be treated according to our Privacy Policy

HGPU group © 2010-2014

All rights belong to the respective authors

Contact us: