Hardware-Accelerated Radix-Tree Based String Sorting for Big Data Applications

Hardware-Accelerated Radix-Tree Based String Sorting for Big Data Applications
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适用于大数据应用的基于硬件加速基数树的字符串排序

DOI:
10.1007/978-3-319-54999-6_4
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发表时间:
2017
期刊:
影响因子:
--
通讯作者:
Thilo Pionteck
Thilo Pionteck
中科院分区:
--
文献类型:
--
作者:
Christopher Blochwitz;Julian Wolff;Jan Moritz Joseph;Stefan Werner;Dennis Heinrich;Sven Groppe;Thilo Pionteck

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本文提出了一种面向大数据应用领域的可扩展字符串排序硬件架构。当前的硬件架构专注于加速排序具有最大字符串长度的小数据集。相比之下,我们提出了一种基于基数树的fpga加速架构,该架构具有对大型字符串集进行排序的能力,而不受字符串长度的实际限制。Radix-Tree是可参数化的,它的设计也是可参数化的,这使得它能够适应特定于应用程序的属性,比如字符串的多样性和所用字母的大小。可扩展的设计具有并行操作的分层处理和内存架构。作为大数据应用的一个例子,使用语义网的数据集来评估最佳参数和配置。对结果进行分析,重点关注吞吐量、内存需求和利用率。与软件系统相比,硬件设计对于基数参数的所有值都更快,并且实现了2.78的最大加速因子。
In this paper, a scalable hardware architecture for string sorting in the application field of Big Data is presented. Current hardware architectures focus on the acceleration of sorting small sets of data with a maximum string length. In contrast, we propose an FPGA-accelerated architecture based on Radix-Trees, which has the ability to sort large sets of strings without practical limitation of the string length. The Radix-Tree is parameterizable and so is the design, which enables the adaptation for application-specific properties, such as diversity of strings and size of the used alphabet. The scalable design has a hierarchical processing and memory architecture, which operate in parallel. Optimal parameters and configurations are evaluated by using a dataset of the Semantic Web, as an example of Big Data applications. The results are analyzed with a focus on throughput, memory requirement, and utilization. The hardware design is faster for all values of the radix parameter and achieves a maximum speed-up factor of 2.78 compared to a software system.
DOI: 10.1145/301970.301973
发表时间: 1999-03-01
期刊: JOURNAL OF THE ACM
影响因子: 2.5
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