When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

Yu Ting Chen; Jason Cong; Zhenman Fang; Jie Lei; Peng Wei

doi:10.1109/FCCM.2016.18

When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

Yu Ting Chen
, Jason Cong
, Zhenman Fang
, Jie Lei
, Peng Wei

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

31 Scopus citations

Abstract

FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement, and captured a great amount of attention from both academia and industry. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks? Although very important, this problem has not been well studied, especially for the integration of fine-grained FPGA accelerators that have short execution time but will be invoked many times. To provide a generalized methodology and insight for efficient integration, we conduct an in-depth analysis of challenges and corresponding solutions of integration at single-thread, single-node multi-thread, and multi-node levels. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.

Original language	English (US)
Title of host publication	Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	29
Number of pages	1
ISBN (Electronic)	9781509023561
DOIs	https://doi.org/10.1109/FCCM.2016.18
State	Published - Aug 16 2016
Externally published	Yes
Event	24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016 - Washington, United States Duration: May 1 2016 → May 3 2016

Publication series

Name	Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016

Conference

Conference	24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016
Country/Territory	United States
City	Washington
Period	5/1/16 → 5/3/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure

Keywords

BWA-MEM
FPGA
MapReduce
Spark

Publisher link

10.1109/FCCM.2016.18

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Cite this

Chen, Y. T., Cong, J., Fang, Z., Lei, J., & Wei, P. (2016). When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration. In Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016 (pp. 29). Article 7544741 (Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FCCM.2016.18

When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration. / Chen, Yu Ting; Cong, Jason; Fang, Zhenman et al.
Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 29 7544741 (Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, YT, Cong, J, Fang, Z, Lei, J & Wei, P 2016, When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration. in Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016., 7544741, Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 29, 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016, Washington, United States, 5/1/16. https://doi.org/10.1109/FCCM.2016.18

Chen YT, Cong J, Fang Z, Lei J, Wei P. When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration. In Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 29. 7544741. (Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016). doi: 10.1109/FCCM.2016.18

Chen, Yu Ting ; Cong, Jason ; Fang, Zhenman et al. / When Spark Meets FPGAs : A Case Study for Next-Generation DNA Sequencing Acceleration. Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 29 (Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016).

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title = "When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration",

abstract = "FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement, and captured a great amount of attention from both academia and industry. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks? Although very important, this problem has not been well studied, especially for the integration of fine-grained FPGA accelerators that have short execution time but will be invoked many times. To provide a generalized methodology and insight for efficient integration, we conduct an in-depth analysis of challenges and corresponding solutions of integration at single-thread, single-node multi-thread, and multi-node levels. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.",

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AU - Wei, Peng

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N2 - FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement, and captured a great amount of attention from both academia and industry. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks? Although very important, this problem has not been well studied, especially for the integration of fine-grained FPGA accelerators that have short execution time but will be invoked many times. To provide a generalized methodology and insight for efficient integration, we conduct an in-depth analysis of challenges and corresponding solutions of integration at single-thread, single-node multi-thread, and multi-node levels. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.

AB - FPGA-enabled datacenters have shown great potential for providing performance and energy efficiency improvement, and captured a great amount of attention from both academia and industry. In this paper we aim to answer one key question: how can we efficiently integrate FPGAs into state-of-the-art big-data computing frameworks? Although very important, this problem has not been well studied, especially for the integration of fine-grained FPGA accelerators that have short execution time but will be invoked many times. To provide a generalized methodology and insight for efficient integration, we conduct an in-depth analysis of challenges and corresponding solutions of integration at single-thread, single-node multi-thread, and multi-node levels. With a step-by-step case study for the next-generation DNA sequencing application, we demonstrate how a straightforward integration with 1000x slowdown can be tuned into an efficient integration with 2.6x overall system speedup and 2.4x energy efficiency improvement.

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When Spark Meets FPGAs: A Case Study for Next-Generation DNA Sequencing Acceleration

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Publisher link

Find It

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