research-article

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Authors:

Xiaomeng Huang,

Guangwen YangAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 8, Issue 2

Article No.: 11, Pages 1 - 16

https://doi.org/10.1145/2629581

Published: 25 March 2015 Publication History

Abstract

One of the most essential and challenging components in climate modeling is the atmospheric model. To solve multiphysical atmospheric equations, developers have to face extremely complex stencil kernels that are costly in terms of both computing and memory resources. This article aims to accelerate the solution of global shallow water equations (SWEs), which is one of the most essential equation sets describing atmospheric dynamics. We first design a hybrid methodology that employs both the host CPU cores and the field-programmable gate array (FPGA) accelerators to work in parallel. Through a careful adjustment of the computational domains, we achieve a balanced resource utilization and a further improvement of the overall performance. By decomposing the resource-demanding SWE kernel, we manage to map the double-precision algorithm into three FPGAs. Moreover, by using fixed-point and reduced-precision floating point arithmetic, we manage to build a fully pipelined mixed-precision design on a single FPGA, which can perform 428 floating-point and 235 fixed-point operations per cycle. The mixed-precision design with four FPGAs running together can achieve a speedup of 20 over a fully optimized design on a CPU rack with two eight-core processorsand is 8 times faster than the fully optimized Kepler GPU design. As for power efficiency, the mixed-precision design with four FPGAs is 10 times more power efficient than a Tianhe-1A supercomputer node.

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Cited By

A KKumar VPrasanth PDutta RRoy BChakraborty P(2024)FPGAs as Hardware Accelerators in Data Centers: A Survey From the Data Centric Perspective2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT)10.1109/DICCT61038.2024.10533053(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/DICCT61038.2024.10533053
Gan LYuan MYang JZhao WLuk WYang G(2020)High performance reconfigurable computing for numerical simulation and deep learningCCF Transactions on High Performance Computing10.1007/s42514-020-00032-xOnline publication date: 11-Jun-2020
https://doi.org/10.1007/s42514-020-00032-x
Xu JYang GFu HLuk WGan LShi WXue WYang CJiang YHe C(2019)Optimizing Finite Volume Method Solvers on Nvidia GPUsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2019.292608430:12(2790-2805)Online publication date: 1-Dec-2019
https://doi.org/10.1109/TPDS.2019.2926084
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Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

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Published In

cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 8, Issue 2

Special Section on FPL 2013

April 2015

129 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/2746532

Editor:
Steve Wilton
Department of Electrical and Computer Engineering/University of British Columbia/Kaiser 4112, 5500-2332 Main Mall/Vancouver, BC V6T 1Z4 Canada

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Copyright © 2015 ACM.

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Association for Computing Machinery

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Publication History

Published: 25 March 2015

Accepted: 01 March 2014

Revised: 01 March 2014

Received: 01 December 2013

Published in TRETS Volume 8, Issue 2

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Funding Sources

National Natural Science Foundation of China
HiPEAC NoE
Maxeler University Program
National High-Tech R&D(863) Program of China
UKEPSRC
Xilinx
European Union Seventh Framework Programme

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Cited By

A KKumar VPrasanth PDutta RRoy BChakraborty P(2024)FPGAs as Hardware Accelerators in Data Centers: A Survey From the Data Centric Perspective2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT)10.1109/DICCT61038.2024.10533053(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/DICCT61038.2024.10533053
Gan LYuan MYang JZhao WLuk WYang G(2020)High performance reconfigurable computing for numerical simulation and deep learningCCF Transactions on High Performance Computing10.1007/s42514-020-00032-xOnline publication date: 11-Jun-2020
https://doi.org/10.1007/s42514-020-00032-x
Xu JYang GFu HLuk WGan LShi WXue WYang CJiang YHe C(2019)Optimizing Finite Volume Method Solvers on Nvidia GPUsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2019.292608430:12(2790-2805)Online publication date: 1-Dec-2019
https://doi.org/10.1109/TPDS.2019.2926084
Yamaura TNishizawa STomita H(2019)Theoretical Time Evolution of Numerical Errors When Using Floating Point Numbers in Shallow‐Water ModelsJournal of Advances in Modeling Earth Systems10.1029/2019MS00161511:10(3235-3250)Online publication date: 23-Oct-2019
https://doi.org/10.1029/2019MS001615
Peulic AMilankovic IMijailovic NFilipovic N(2019)Biomedical Images Processing Using Maxeler DataFlow EnginesExploring the DataFlow Supercomputing Paradigm10.1007/978-3-030-13803-5_7(197-227)Online publication date: 28-May-2019
https://doi.org/10.1007/978-3-030-13803-5_7
Sustersic TVulovic ATrifunovic NMilankovic IFilipovic N(2019)Face Recognition Using Maxeler DataFlowExploring the DataFlow Supercomputing Paradigm10.1007/978-3-030-13803-5_6(171-196)Online publication date: 28-May-2019
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Huang FZhu QZhou JTao JZhou XJin DTan XWang L(2017)Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark PlatformRemote Sensing10.3390/rs91213019:12(1301)Online publication date: 12-Dec-2017
https://doi.org/10.3390/rs9121301
Milankovic IMijailovic NFilipovic NPeulic A(2017)Acceleration of Image Segmentation Algorithm for (Breast) Mammogram Images Using High-Performance Reconfigurable Dataflow ComputersComputational and Mathematical Methods in Medicine10.1155/2017/79092822017(1-11)Online publication date: 2017
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Huang FTao JXiang YLiu PDong LWang L(2017)Parallel compressive sampling matching pursuit algorithm for compressed sensing signal reconstruction with OpenCLJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2016.07.00272:C(51-60)Online publication date: 1-Jan-2017
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Russell FDüben PNiu XLuk WPalmer T(2017)Exploiting the chaotic behaviour of atmospheric models with reconfigurable architecturesComputer Physics Communications10.1016/j.cpc.2017.08.011221(160-173)Online publication date: Dec-2017
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