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خانه مقالات-Article مقالات کامپیوتر-Computer Articles هادوپ-Hadoop Analyzing Massive Astrophysical Datasets: Can Pig/Hadoop or a Relational DBMS Help?
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Analyzing Massive Astrophysical Datasets: Can Pig/Hadoop or a Relational DBMS Help?

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Analyzing Massive Astrophysical Datasets: Can Pig/Hadoop or a Relational DBMS Help?

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ABSTRACT

As the datasets used to fuel modern scientific discovery grow increasingly large, they become increasingly difficult to
manage using conventional software. Parallel database management systems (DBMSs) and massive-scale data processing systems such as MapReduce hold promise to address this challenge. However, since these systems have not been expressly designed for scientific applications, their efficacy in this domain has not been thoroughly tested. In this paper, we study the performance of these engines in one specific domain: massive astrophysical simulations. We develop a use case that comprises five representative queries. We implement this use case in one distributed DBMS and in the Pig/Hadoop system. We compare the performance of the tools to each other and to hand-written IDL scripts. We find that certain representative analyses are easy to express in each engine’s highlevel language and both systems provide competitive performance and improved scalability relative to current IDL-based methods

INTRODUCTION
Advances in high-performance computing are having a transformative impact on many scientific disciplines. Advances in compute power and an increased ability to harness this power enable scientists, among other tasks, to run simulations at an unprecedented scale. Simulations are used to model the behavior of complex natural systems ranging from the interaction of subatomic particles to the evolution of the universe. These simulations produce an ever more massive amount of data that must be analyzed, interacted with, and understood by the scientist
While scientists have access to tools and an unprecedented amount of computational resources to run increasingly complex simulations, their ability to analyze the resulting data remains limited

Publisher:IEEE

Year:2009

By:Sarah Loebman, Dylan Nunley, YongChul Kwon, Bill Howe, Magdalena Balazinska, and Jeffrey P. Gardner

File Information:English Language/10 Page/Size:187 K

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ناشر:IEEE

سال:2009

کاری از:Sarah Loebman, Dylan Nunley, YongChul Kwon, Bill Howe, Magdalena Balazinska, and Jeffrey P. Gardner

اطلاعات فایل:زبان انگلیسی/10 صفحه/حجم:187 K

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