Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform‐independent, high‐abstraction programming paradigm for large‐scale data processing by leveraging the Java framework. Though it provides so...
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https://www.riss.kr/link?id=O118959170
2019년
-
1532-0626
1532-0634
SCOPUS;SCIE
학술저널
n/a-n/a [※수록면이 p5 이하이면, Review, Columns, Editor's Note, Abstract 등일 경우가 있습니다.]
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform‐independent, high‐abstraction programming paradigm for large‐scale data processing by leveraging the Java framework. Though it provides so...
Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform‐independent, high‐abstraction programming paradigm for large‐scale data processing by leveraging the Java framework. Though it provides software portability across various machines, Java also limits the performance of distributed environments, such as Spark. While it may be unrealistic to rewrite platforms like Spark in a faster language, a more viable approach to mitigate its poor performance is to accelerate the computations while still working within the Java‐based framework. This paper demonstrates the feasibility of incorporating Field‐Programmable Gate Array (FPGA) acceleration into Spark and presents the performance benefits and bottlenecks of our FPGA‐accelerated Spark environment using a MapReduce implementation of the k‐means clustering algorithm, to show that acceleration is possible even when using a hardware platform that is not well optimized for performance. An important feature of our approach is that the use of FPGAs is completely transparent to the user through the use of library functions, which is a common way by which users access functions provided by Spark. Power users can further develop other computations using high‐level synthesis.
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