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A Parallel Implementation of K-Means Clustering on GPUs

机译：在GPU上并行实现K均值聚类

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Graphics Processing Units (GPU) have recently been the subject of attention in research as an efficient coprocessor for implementing many classes of highly parallel applications. The GPUs design is engineered for graphics applications, where many independent SIMD workloads are simultaneously dispatched to processing elements. While parallelism has been explored in the context of traditional CPU threads and SIMD processing elements, the principles involved in dividing the steps of a parallel algorithm for execution on GPU architectures remains a significant challenge.rnIn this paper, we introduce a first step towards building an efficient GPU-based parallel implementation of a commonly used clustering algorithm called K-Means on an NVIDIA G80 PCI express graphics board using the CUDA processing extensions. Clustering algorithms are important for search, data mining, spam and intrusion detection applications. Modern desktop machines commonly include desktop search software that can be greatly enhanced by these advances, while low-power machines such as laptops can reduce power consumption by utilizing the video chip for these clustering and indexing operations. Our preliminary results show over a 13x performance improvement compared to a baseline 3 GHz Intel Pentium(R) based PC running the same algorithm with an average spec G80 graphics card, the NVIDIA 8600GT. The low cost of these video cards (less than $100 market price as of 2008), and the high performance gains suggest that our approach is both practical and economical for common applications.
机译：作为用于实现许多类高度并行应用程序的有效协处理器，图形处理单元（GPU）最近已成为研究的焦点。 GPU设计是针对图形应用程序设计的，在图形应用程序中，许多独立的SIMD工作负载被同时调度到处理元素。虽然已经在传统的CPU线程和SIMD处理元素的背景下探索了并行性，但是划分并行算法的步骤以在GPU架构上执行所涉及的原理仍然是一个巨大的挑战。使用CUDA处理扩展功能，在NVIDIA G80 PCI Express图形板上高效地基于GPU并行执行常用的群集算法（称为K-Means）。聚类算法对于搜索，数据挖掘，垃圾邮件和入侵检测应用很重要。现代台式机通常包括台式机搜索软件，这些进步可以大大增强这些功能，而低功耗计算机（如笔记本电脑）可以通过将视频芯片用于这些群集和索引操作来降低功耗。我们的初步结果显示，与运行相同算法和平均规格G80显卡NVIDIA 8600GT的基于基准3 GHz Intel Pentium（R）的PC相比，性能提高了13倍。这些视频卡的低成本（截止到2008年，市场价格不到100美元），以及高性能的提高，表明我们的方法对于常见应用既实用又经济。

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