Deconvolution of huge 3-D images: Parallelization strategies on a multi-GPU system
| Authors | |
|---|---|
| Year of publication | 2013 |
| Type | Article in Proceedings |
| Conference | Algorithms and Architectures for Parallel Processing |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1007/978-3-319-03859-9_24 |
| Doi | http://dx.doi.org/10.1007/978-3-319-03859-9_24 |
| Field | Informatics |
| Keywords | deconvolution; gpu; multi-gpu; parallelization; implementation; algorithm; em-mle; richardson-lucy; ictm; wiener |
| Description | In this paper, we discuss strategies to parallelize selected deconvolution methods on a multi-GPU system. We provide a comparison of several approaches to split the deconvolution into subtasks while keeping the amount of costly data transfers as low as possible, and propose own implementation of three deconvolution methods which achieves up to 65x speedup over the CPU one. In the experimental part, we analyse how the individual stages of the computation contribute to the overall computation time as well as how the multi-GPU implementation scales in various setups. Finally, we identify bottlenecks of the system. |
| Related projects: |