Graphics Processing Units technology (GPU) and CUDA architecture are one of the most used options to adapt machine learning techniques to the huge amounts of complex data that are currently generated. Biclustering techniques are useful for discovering local patterns in datasets. Those of them that have been implemented to use GPU resources in parallel have improved their computational performance. However, this fact does not guarantee that they can successfully process large datasets. There are some important issues that must be taken into account, like the data transfers between CPU and GPU memory or the balanced distribution of workload between the GPU resources. In this paper, a GPU version of one of the fastest biclustering solutions, BiBit, is presented. This implementation, named gBiBit, has been designed to take full advantage of the computational resources offered by GPU devices. Either using a single GPU device or in its multi-GPU mode, gBiBit is able to process large binary datasets. The experimental results have shown that gBiBit improves the computational performance of BiBit, a CPU parallel version and an early GPU version, called ParBiBit and CUBiBit, respectively. gBiBit source code is available at https://github.com/aureliolfdez/gbibit.

图形处理单元技术（GPU）和CUDA体系结构是使机器学习技术适应当前生成的大量复杂数据的最常用选项之一。双集群技术对于发现数据集中的局部模式很有用。它们中的那些已实现并行使用GPU资源，从而提高了它们的计算性能。但是，此事实不能保证他们可以成功处理大型数据集。必须考虑一些重要问题，例如CPU和GPU内存之间的数据传输或GPU资源之间的工作负载平衡分配。本文介绍了最快的双集群解决方案之一BiBit的GPU版本。这个名为gBiBit的实现，旨在充分利用GPU设备提供的计算资源。使用单个GPU设备或在其多GPU模式下，gBiBit都能够处理大型二进制数据集。实验结果表明，gBiBit改进了BiBit的计算性能，BiBit是CPU并行版本和GPU早期版本，分别称为ParBiBit和CUBiBit。gBiBit源代码可从https://github.com/aureliolfdez/gbibit获得。