Beceiro, BieitoGonzález-Domínguez, JorgePérez Diéguez, AdriánIbrahim, Khaled Z.Touriño, Juan2026-08-262026-08-262026Beceiro, B., González-Domínguez, J., Diéguez, A.P. et al. A multi-backend autotuning study of feature selection on GPUs. J Supercomput 82, 645 (2026). https://doi.org/10.1007/s11227-026-08770-51573-0484https://hdl.handle.net/2183/49094Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG The code for the CUDA, HIP, and SYCL backends for mRMR used in the experimental evaluation is available at https://gitlab.com/bieito/gpufs. The datasets analyzed in this study are available in the LIBSVM repository, https://www.csie.ntu.edu.twcjlin/libsvmtools/datasets/[Abstract]: Feature selection is an important step in machine learning that can benefit from GPU acceleration. As the number of GPU vendors increases, it is imperative to adapt algorithms such as the minimum Redundancy Maximum Relevance (mRMR) feature selection method to different backends that support several GPU architectures. This work presents a multi-backend implementation of mRMR across CUDA, HIP, and SYCL, and studies its performance when combined with Bayesian optimization and transfer learning to automatically tune execution parameters for different platforms and datasets. Our experimental results show that when tuned, CUDA and HIP achieve comparable performance on NVIDIA architectures, while SYCL exhibits a moderate performance gap. Overall, this work highlights the impact of backend choice and autotuning on GPU-accelerated feature selection and provides insights into deploying mRMR across heterogeneous environments.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/GPUFeature selectionmRMRCUDAHIPSYCLjournal articleopen access10.1007/s11227-026-08770-5