research-article

BlastFunction: A Full-stack Framework Bringing FPGA Hardware Acceleration to Cloud-native Applications

Authors:

Andrea Damiani,

Giorgia Fiscaletti,

Rolando Brondolin,

Marco D. SantambrogioAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 15, Issue 2

Article No.: 17, Pages 1 - 27

https://doi.org/10.1145/3472958

Published: 11 January 2022 Publication History

Abstract

“Cloud-native” is the umbrella adjective describing the standard approach for developing applications that exploit cloud infrastructures’ scalability and elasticity at their best. As the application complexity and user-bases grow, designing for performance becomes a first-class engineering concern. As an answer to these needs, heterogeneous computing platforms gained widespread attention as powerful tools to continue meeting SLAs for compute-intensive cloud-native workloads. We propose BlastFunction, an FPGA-as-a-Service full-stack framework to ease FPGAs’ adoption for cloud-native workloads, integrating with the vast spectrum of fundamental cloud models. At the IaaS level, BlastFunction time-shares FPGA-based accelerators to provide multi-tenant access to accelerated resources without any code rewriting. At the PaaS level, BlastFunction accelerates functionalities leveraging the serverless model and scales functions proactively, depending on the workload’s performance. Further lowering the FPGAs’ adoption barrier, an accelerators’ registry hosts accelerated functions ready to be used within cloud-native applications, bringing the simplicity of a SaaS-like approach to the developers. After an extensive experimental campaign against state-of-the-art cloud scenarios, we show how BlastFunction leads to higher performance metrics (utilization and throughput) against native execution, with minimal latency and overhead differences. Moreover, the scaling scheme we propose outperforms the main serverless autoscaling algorithms in workload performance and scaling operation amount.

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cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 15, Issue 2

June 2022

310 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/3501287

Editor:
Deming Chen
University of Illinois, Urbana-Champaign Urbana, USA

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Publication History

Published: 11 January 2022

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 January 2021

Published in TRETS Volume 15, Issue 2

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Cited By

Mahapatra RGhodrati SAhn BKinzer SWang SXu HKarthikeyan LSharma HYazdanbakhsh AAlian MEsmaeilzadeh HTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)In-Storage Domain-Specific Acceleration for Serverless ComputingProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640413(530-548)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640413
Sun BZhou HSong GZhang Q(2024)Research on the Technology Architecture of Full-stack Cloud-Native Hosted Cloud2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA)10.1109/MiTA60795.2024.10751724(1-7)Online publication date: 14-Jul-2024
https://doi.org/10.1109/MiTA60795.2024.10751724
Fernandes LKharate PSingh B(2024)The Future of High Performance Computing in Biomimetics and Some ChallengesHigh Performance Computing in Biomimetics10.1007/978-981-97-1017-1_15(287-303)Online publication date: 21-Mar-2024
https://doi.org/10.1007/978-981-97-1017-1_15
Thyagaturu AShantharama PNasrallah AReisslein M(2022)Operating Systems and Hypervisors for Network Functions: A Survey of Enabling Technologies and Research StudiesIEEE Access10.1109/ACCESS.2022.319491310(79825-79873)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3194913
Pal DDeng CUstun EYu CZhang Z(2022)Machine Learning for Agile FPGA DesignMachine Learning Applications in Electronic Design Automation10.1007/978-3-031-13074-8_16(471-504)Online publication date: 10-Aug-2022
https://doi.org/10.1007/978-3-031-13074-8_16
Zahran M(2021)The Future of High-Performance Computing2021 17th International Computer Engineering Conference (ICENCO)10.1109/ICENCO49852.2021.9698918(129-134)Online publication date: 29-Dec-2021
https://doi.org/10.1109/ICENCO49852.2021.9698918

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