GPU上的连接组件识别和集群更新
Connected component identification and cluster update on GPU
摘要：
聚类识别任务发生在物理和工程中的多种背景下，例如，用于模拟自旋模型的聚类算法、渗流模拟、图像处理中的分割问题或网络分析。虽然已经表明，图形处理单元（GPU）可以导致两到三个数量级的加速相比，在CPU上的串行代码的情况下，本地，因此自然并行化的问题，如单自旋翻转更新模拟自旋模型，情况是相当复杂的集群或连接组件识别的非本地问题。我讨论了集群标记并行化和集群更新算法的不同方法对GPU计算的适用性，并与串行实现的性能进行了比较。
英文摘要：
Cluster identification tasks occur in a multitude of contexts in physics and engineering such as, for instance, cluster algorithms for simulating spin models, percolation simulations, segmentation problems in image processing, or network analysis. While it has been shown that graphics processing units (GPUs) can result in speedups of two to three orders of magnitude as compared to serial codes on CPUs for the case of local and thus naturally parallelized problems such as single-spin flip update simulations of spin models, the situation is considerably more complicated for the non-local problem of cluster or connected component identification. I discuss the suitability of different approaches of parallelization of cluster labeling and cluster update algorithms for calculations on GPU and compare to the performance of serial implementations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
