RT Journal Article
T1 IHP: a dynamic heterogeneous parallel scheme for iterative or time‑step methods—image denoising as case study
A1 Laso, Ruben
A1 Cabaleiro Domínguez, José Carlos
A1 Fernández Rivera, Francisco
A1 Muñiz Castiñeira, María del Carmen
A1 Álvarez Dios, José Antonio
K1 Heterogeneous parallelism
K1 Iterative methods
K1 Time-step methods
K1 Image processing
K1 Finite differences
AB Iterative and time-step methods are spread far and wide in several mathematics and physics domains. At the same time, modern computers include multicore CPUs along with GPUs, so it is important to use all their computing capabilities for their efficient use. Aiming to improve performance of this kind of numerical methods, we introduce in this work a new heterogeneous parallelism CPU + GPU scheme which we call IHP. This new scheme has the advantage of being self-balanced and able to dynamically distribute the workload between CPU and GPU according to their performance on the fly. Also, it can be used with several contending technologies, like CUDA and OpenCL for GPUs or OpenMP and Intel TBB for CPUs. As a case in point, we analyse an image denoising problem based on time-step diffusion methods for brightness and chromaticity. Results show execution significant improvements in execution time using this scheme, with a minimal overhead.
PB Springer
YR 2021
FD 2021-01
LK https://hdl.handle.net/10347/42117
UL https://hdl.handle.net/10347/42117
LA eng
NO Laso, R., Cabaleiro, J.C., Rivera, F.F. et al. IHP: a dynamic heterogeneous parallel scheme for iterative or time-step methods—image denoising as case study. J Supercomput 77, 95–110 (2021). https://doi.org/10.1007/s11227-020-03260-8
NO This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11227-020-03260-8
NO This work has received financial support from the Ministerio de Economía, Industria y Competitividad within the project TIN2016-76373-P. It was also funded by the Consellería de Cultura, Educación e Ordenación Universitaria of Xunta de Galicia (accr. 2019-2022, ED431G2019/04 and reference competitive group 2019-2021, ED431C 2018/19). Thanks to Rafael Asenjo and Department of Computer Architecture of Universidad de Málaga for providing us the source code of LogFit and their help.
DS Minerva
RD 28 abr 2026