Structure clustering is a general but time-consuming work in the study of life science. Up to now, most published tools do not support the clustering analysis on graphics processing unit (GPU) with root mean square deviation metric. In this work, we specially write codes to do the work. It supports multiple threads on multiple GPUs. To show the performance, we apply the program to a 33-residue fragment in protein Pin1 WW domain mutant. The dataset contains 1,400,000 snapshots, which are extracted from an enhanced sampling simulation and distribute widely in the conformational space. Various testing results present that our program is quite efficient. Particularly, with two NVIDIA RTX4090 GPUs and single precision data type, the clustering calculation on 1 million snapshots is completed in a few seconds (including the uploading time of data from memory to GPU and neglecting the reading time from hard disk). This is hundreds of times faster than central processing unit. Our program could be a powerful tool for fast extraction of representative states of a molecule among its thousands to millions of candidate structures.

中文翻译：

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在几秒钟内在 GPU 上聚类 100 万个分子结构


结构聚类是生命科学研究中一项普遍但耗时的工作。到目前为止，大多数已发布的工具不支持使用均方根偏差指标对图形处理单元 （GPU） 进行聚类分析。在这项工作中，我们专门编写代码来完成这项工作。它支持多个 GPU 上的多个线程。为了显示性能，我们将该程序应用于蛋白质 Pin1 WW 结构域突变体中的 33 个残基片段。该数据集包含 1,400,000 个快照，这些快照是从增强的采样模拟中提取的，并广泛分布在构象空间中。各种测试结果表明，我们的程序非常有效。特别是，使用两个 NVIDIA RTX4090 GPU 和单精度数据类型，100 万张快照的聚类计算在几秒钟内完成（包括数据从内存上传到 GPU 的时间，忽略了从硬盘读取的时间）。这比中央处理器快数百倍。我们的程序可以成为一个强大的工具，用于在数千到数百万个候选结构中快速提取分子的代表性状态。