更快的排序闪存嵌入式设备

Riley Jackson, R. Lawrence
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引用次数: 5

摘要

嵌入式设备在各种应用中收集和处理数据,包括消费和个人电子产品、医疗保健、环境传感器和物联网(IoT)部署。在设备上处理数据,而不是通过网络发送数据进行分析,通常更快、更节能,并且支持更接近数据收集的决策。排序是一种基本的数据操作。由于内存和CPU资源非常低,对带有闪存的嵌入式设备进行排序尤其具有挑战性。以前的工作开发了定制的算法,以避免写入和最小化内存使用。标准的外部归并排序算法在小型设备上的应用有限,因为它至少需要三个内存缓冲区,并且不支持闪存。这项工作的贡献是扩展了外部归并排序,它只需要两个内存缓冲区,并针对闪存进行了优化。其结果是一种算法在最先进的基础上得到了改进,并适用于更广泛的设备。实验结果表明,在对内存较小的大数据集进行排序时,该算法可减少约30%的I/ o和执行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Faster Sorting for Flash Memory Embedded Devices
Embedded devices collect and process data in a wide variety of applications including consumer and personal electronics, healthcare, environmental sensors, and Internet of Things (IoT) deployments. Processing data on the device rather than sending it over the network for analysis is often faster, more energy efficient, and supports decision-making closer to data collection. A fundamental data manipulation operation is sorting. Sorting on embedded devices with flash memory is especially challenging due to the very low memory and CPU resources. Previous work developed customized algorithms that avoided writes and minimized memory usage. The standard external merge sort algorithm has limited application on small devices as it requires a minimum of three memory buffers and is not flash-aware. The contribution of this work is an extension of external merge sort that requires only two memory buffers and is optimized for flash memory. The result is an algorithm that improves on the state-of-the-art and applies to a wider range of devices. Experimental results demonstrate that when sorting large data sets with small memory the algorithm reduces I/Os and execution time by about 30%.
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