D4M 3.0:扩展数据库和语言功能

Lauren Milechin, V. Gadepally, S. Samsi, J. Kepner, Alexander Chen, D. Hutchison
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引用次数: 2

摘要

开发D4M工具是为了满足当今的许多数据需求。数百名研究人员使用该工具对非结构化数据进行复杂分析。在过去的几年中,D4M工具箱已经发展到支持与各种新数据库引擎(包括SciDB)的连接。D4M-Graphulo提供了在Apache Accumulo数据库中进行图形分析的能力。最后,现在还提供了使用Julia编程语言的实现。在本文中,我们将介绍D4M工具箱的一些最新功能和即将发布的D4M 3.0版本。我们通过基准测试和缩放结果表明,我们可以使用D4M-SciDB连接器实现快速SciDB提取,使用Graphulo可以在内存有限的规模上启用图形算法,并且D4M的Julia实现实现了相当的性能或超过现有的MATLAB®实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
D4M 3.0: Extended database and language capabilities
The D4M tool was developed to address many of today's data needs. This tool is used by hundreds of researchers to perform complex analytics on unstructured data. Over the past few years, the D4M toolbox has evolved to support connectivity with a variety of new database engines, including SciDB. D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo database. Finally, an implementation using the Julia programming language is also now available. In this article, we describe some of our latest additions to the D4M toolbox and our upcoming D4M 3.0 release. We show through benchmarking and scaling results that we can achieve fast SciDB ingest using the D4M-SciDB connector, that using Graphulo can enable graph algorithms on scales that can be memory limited, and that the Julia implementation of D4M achieves comparable performance or exceeds that of the existing MATLAB® implementation.
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