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dc.contributor.author
Singh, Gagandeep
dc.contributor.author
Diamantopoulos, Dionysios
dc.contributor.author
Gómez Luna, Juan
dc.contributor.author
Hagleitner, Christoph
dc.contributor.author
Stuijk, Sander
dc.contributor.author
Corporaal, Henk
dc.contributor.author
Mutlu, Onur
dc.date.accessioned
2023-01-27T09:49:47Z
dc.date.available
2023-01-27T04:29:40Z
dc.date.available
2023-01-27T09:49:47Z
dc.date.issued
2022-12
dc.identifier.issn
1936-7406
dc.identifier.issn
1936-7414
dc.identifier.other
10.1145/3501804
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/595211
dc.description.abstract
Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU implementations suffer from limited performance and high energy consumption. These implementations are dominated by complex irregular memory access patterns and low arithmetic intensity that pose fundamental challenges to acceleration. To overcome these challenges, we propose and evaluate the use of near-memory acceleration using a reconfigurable fabric with high-bandwidth memory (HBM). We focus on compound stencils that are fundamental kernels in weather prediction models. By using high-level synthesis techniques, we develop NERO, an field-programmable gate array+HBM-based accelerator connected through Open Coherent Accelerator Processor Interface to an IBM POWER9 host system. Our experimental results show that NERO outperforms a 16-core POWER9 system by and when running two different compound stencil kernels. NERO reduces the energy consumption by and for the same two kernels over the POWER9 system with an energy efficiency of 1.61 GFLOPS/W and 21.01 GFLOPS/W. We conclude that employing near-memory acceleration solutions for weather prediction modeling is promising as a means to achieve both high performance and high energy efficiency.
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.subject
FPGA
en_US
dc.subject
Weather modeling
en_US
dc.subject
Near-memory computing
en_US
dc.subject
High-performance computing
en_US
dc.subject
Processing in memory
en_US
dc.title
Accelerating Weather Prediction Using Near-Memory Reconfigurable Fabric
en_US
dc.type
Journal Article
dc.date.published
2022-06-06
ethz.journal.title
ACM Transactions on Reconfigurable Technology and Systems
ethz.journal.volume
15
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
ACM Trans. Reconig. Technol. Syst.
ethz.pages.start
39
en_US
ethz.size
27 p.
en_US
ethz.grant
Open Transprecision Computing
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::09483 - Mutlu, Onur / Mutlu, Onur
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::09483 - Mutlu, Onur / Mutlu, Onur
ethz.grant.agreementno
732631
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
H2020
ethz.date.deposited
2023-01-27T04:29:41Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-01-27T09:49:48Z
ethz.rosetta.lastUpdated
2023-02-07T10:05:04Z
ethz.rosetta.versionExported
true
ethz.COinS
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