研讨会7:HPBDC高性能大数据与云计算

Xiaoyi Lu, Jianfeng Zhan
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引用次数: 1

摘要

管理和处理大量数据或大数据,并获得有意义的见解,是并行和分布式计算社区面临的一个重大挑战。这对包括医疗保健、生物医学研究、互联网搜索、金融和商业信息学以及科学计算在内的广泛领域产生了重大影响。随着数据收集技术和数据源见证了输入数据量的爆炸式增长,预计未来将需要处理数百或数千pb的大量数据。因此,精心设计处理此类数据的数据密集型计算中间件(如Hadoop、Spark、Flink等),具有高性能和可扩展性,以满足此类大数据应用日益增长的需求是至关重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Workshop 7: HPBDC High-Performance Big Data and Cloud Computing
Managing and processing large volumes of data, or Big Data, and gaining meaningful insights is a significant challenge facing the parallel and distributed computing community. This has significant impact in a wide range of domains including health care, bio-medical research, Internet search, finance and business informatics, and scientific computing. As data-gathering technologies and data sources witness an explosion in the amount of input data, it is expected that in the future massive quantities of data in the order of hundreds or thousands of petabytes will need to be processed. Thus, it is critical that data-intensive computing middleware (such as Hadoop, Spark, Flink, etc.) to process such data are diligently designed, with high performance and scalability, in order to meet the growing demands of such Big Data applications.
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