Accelerating Apache Spark with FPGAs. (16th July 2017)

• Record Type: Journal Article
• Authors: Ghasemi, Ehsan; Chow, Paul
• Other Names: Sahuquillo Jesús Escudero guestEditor.; Garcia Pedro Javier guestEditor.; Bellatreche Ladjel guestEditor.; Leung Carson guestEditor.; Xia Yinglong guestEditor.; Baz Didier El guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-16
• Subjects: Apache Spark -- big data -- FPGA -- high‐level synthesis -- Java -- MapReduce; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4222
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.