Fernández-Fraga, AlejandroGonzález-Domínguez, JorgeMartín, María J.2024-12-182024-12-182025-02Fernández-Fraga, A., González-Domínguez, J., Martín, María J. (2025). BetaGPU: Harnessing GPU power for parallelized beta distribution functions, SoftwareX, 29(102009), 2025. https://doi.org/10.1016/j.softx.2024.1020092352-7110http://hdl.handle.net/2183/40549Permanent link to code/repository used for this code version: https://github.com/ElsevierSoftwareX/SOFTX-D-24-00555. Link to developer documentation/manual: https://github.com/UDC-GAC/BetaGPU[Abstract]: The efficient computation of Beta distribution functions, particularly the Probability Density Function (PDF) and Cumulative Distribution Function (CDF), is critical in various scientific fields, including bioinformatics and data analysis. This work presents BetaGPU, a high-performance software package written in C++ and CUDA that leverages the parallel processing capabilities of Graphics Processing Units (GPUs) to significantly accelerate these computations, with an OpenMP version for multiCPU systems, and integrated seamlessly with popular statistical programming languages R and Python. This open-source package provides an accessible, accurate, and scalable solution for researchers and practitioners. By offloading intensive calculations to the GPU, this software is significantly faster than traditional single-core CPU-based methods, facilitating faster data analysis and enabling real-time applications. The software’s high performance and ease of use make it an invaluable tool for users in bioinformatics and other data-intensive domains.engAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)http://creativecommons.org/licenses/by-nc/3.0/es/Beta distributionHigh performance computingGPUCUDAROpenMPPythonjournal articleopen access10.1016/j.softx.2024.102009