ABSTRACT Recently, graphics processing unit (GPU) gained lots of attention from academia and industry for its applicability in high-performance computing. It has been successfully applied to many fields, such as image processing, machine learning, object detection, etc. In our previous work, GPU was adopted to accelerate the computation of rough set approximation (RSA), which is the core step in most of the rough sets based tasks, e.g. attribute reduction. The method is essentially a CPU-GPU cooperative paradigm. That is to say, there are lots of data exchanged between host memory and GPU memory, which greatly degrades the performance of the system. This paper introduces a unified GPU framework for parallel attribute reduction, in which two critical steps in attribute reduction, i.e. computation of equivalence class and attributes significance, are both executed on GPU. Moreover, the algorithm is well designed by exploiting the architectural characteristics of the modern GPU architecture. Experiments were carried out on data sets with different sizes. The results show that the proposed algorithm can outperform the CPU-GPU cooperative algorithm on large data sets.

中文翻译：

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图形处理单元上的高性能属性约简

摘要 近年来，图形处理单元（GPU）因其在高性能计算中的适用性而受到学术界和工业界的广泛关注。它已成功应用于许多领域，如图像处理、机器学习、目标检测等。 在我们之前的工作中，GPU 被用来加速粗糙集逼近 (RSA) 的计算，这是大多数情况下的核心步骤。基于粗糙集的任务，例如属性约简。该方法本质上是一种 CPU-GPU 协作范式。也就是说，主机内存和GPU内存之间有大量的数据交换，这大大降低了系统的性能。本文介绍了一个统一的GPU并行属性约简框架，其中属性约简的两个关键步骤，即等价类和属性重要性的计算，两者都在 GPU 上执行。此外，该算法通过利用现代 GPU 架构的架构特性进行了很好的设计。对不同大小的数据集进行了实验。结果表明，该算法在大数据集上的性能优于CPU-GPU协同算法。