New Frontiers in High Performance Computing and Big Data
New Frontiers in High Performance Computing and Big Data
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高性能计算和大数据的新领域
DOI：
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发表时间：
2017
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通讯作者：
T. Sterling
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作者：
G. Fox;V. Getov;L. Grandinetti;G. Joubert;T. Sterling
在2016年7月于塞特拉罗举行的先进高性能计算系统国际研究讲习班上，讨论了与解决计算密集型和大规模问题有关的新发展的广泛专题。本卷收录了研讨会上介绍的部分来稿。
在过去的四十年中，并行计算平台日益成为高性能系统开发的基础，其主要目的是解决计算密集型问题。然而，这种系统也提供了解决大规模问题的可能性，例如，在处理大型科学数据集以及在对医疗、社交媒体、营销、经济和地理数据的所谓大数据分析进行分析时遇到的问题。
本书收集并出版的论文涵盖了并行系统开发的各个方面，突出了在开发更强大的计算系统时遇到的一些问题。一方面，数百万个处理单元的能耗限制了未来并行系统的扩展，另一方面，为了利用大量处理器的处理能力而对并行进程进行调度成为一个严重的障碍。
并行计算系统在处理和分析大数据集方面的应用已成为进一步推动科学研究的一个主要因素。许多作者讨论了数据科学方面的问题。此外，一些案例研究强调了未来大规模数据科学应用的重要性。
编辑希望读者能从本书中包含的理论和实践观点和经验方面的贡献中受益。编辑们特别感谢Claudia Rotella博士和Maria Teresa Guaglianone博士的宝贵帮助，以及微软提供他们的CMT系统。
At the International Research Workshop on Advanced High Performance Computing Systems held in Cetraro in July 2016 a wide spectrum of topics on new developments related to the solution of compute intensive and large scale problems were discussed. A selection of contributions presented at the workshop are included in this volume.
During the last four decades parallel compute platforms increasingly formed the basis for the development of High Performance Systems primarily aimed at the solution of compute intensive problems. Such systems, however, also offer the possibility to solve large scale problems encountered, for example, in the processing of large scientific data sets, as well as in the analysis of so-called Big Data analyses of, for example, medical, social media, marketing, economics and geo data.
The papers collected for publication in this book cover aspects of the development of parallel systems, highlighting some of the problems encountered with the development of ever more powerful compute systems. On the one hand the energy consumption of millions of processing elements places a limit on the future expansion of parallel systems, and on the other the scheduling of parallel processes to utilise the processing power of a large number of processors becomes a serious obstacle.
The application of parallel compute systems for the processing and analysis of large data sets has become a major factor in furthering scientific research. Data Science aspects are discussed by a number of authors. In addition a number of case studies underlines the importance of future large scale data science applications.
The editors hope that readers can benefit from the contributions on theoretical and practical views and experiences included in this book. The editors are especially indebted to Dr Claudia Rotella and Dr Maria Teresa Guaglianone for their valuable assistance, as well as to Microsoft for making their CMT system available.