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Accelerating Data Analytics Kernels with Heterogeneous Computing

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Emerging Technology and Architecture for Big-data Analytics

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Abstract

Heterogeneous computing platforms combining general-purpose processing elements with different accelerators (such as GPU or FPGAs) are ideally suited for efficient processing of compute-intensive data analytics kernels. In this chapter, we focus on the acceleration of data analytics kernels on heterogenous computing systems with FPGAs. The introduction of FPGAs in the context of data analytics is negatively impacted by the difficulty in programming such systems given the increasing complexity of FPGA-based accelerators. This makes high-level synthesis (HLS) an attractive solution to improve designer productivity by abstracting the programming effort above register-transfer level (RTL). HLS offers various architectural design options with different trade-offs via pragmas (loop unrolling, loop pipelining, array partitioning). However, non-negligible HLS runtime renders manual or automated HLS-based exhaustive architectural exploration for implementation of the kernels practically infeasible. To address this challenge, we have developed Lin-Analyzer, a high-level accurate performance analysis tool that enables rapid design space exploration with various pragmas for FPGA-based accelerators without requiring RTL implementations. We show how Lin-Analyzer can enable easy but performance efficient implementation of computational kernels from a variety of data analytics applications onto FPGA-based heterogeneous systems.

Alok completed this project while working at SoC, NUS

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Acknowledgements

This work was partially supported by the Singapore Ministry of Education Academic Research Fund Tier 2 MOE2015-T2-2-088.

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Authors and Affiliations

  1. School of Computing, National University of Singapore, Singapore, Singapore

    Guanwen Zhong & Tulika Mitra

  2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Alok Prakash

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  1. Guanwen Zhong
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  3. Tulika Mitra
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Correspondence to Tulika Mitra .

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Editors and Affiliations

  1. School of Computer Science and Engineering, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Anupam Chattopadhyay

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Chip Hong Chang

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Hao Yu

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Zhong, G., Prakash, A., Mitra, T. (2017). Accelerating Data Analytics Kernels with Heterogeneous Computing. In: Chattopadhyay, A., Chang, C., Yu, H. (eds) Emerging Technology and Architecture for Big-data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-54840-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-54840-1_2

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