Laso, RubenCabaleiro Domínguez, José CarlosFernández Rivera, FranciscoMuñiz Castiñeira, María del CarmenÁlvarez Dios, José Antonio2025-06-182025-06-182021-01Laso, 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/s11227-020-03260-8https://hdl.handle.net/10347/42117This 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/s11227-020-03260-8Iterative 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.engHeterogeneous parallelismIterative methodsTime-step methodsImage processingFinite differencesjournal article10.1007/s11227-020-03260-81573-0484open access