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dc.contributor.author
Breitwieser, Lukas
dc.contributor.author
Hesam, Ahmad
dc.contributor.author
Rademakers, Fons
dc.contributor.author
Gómez Luna, Juan
dc.contributor.author
Mutlu, Onur
dc.date.accessioned
2023-03-14T09:16:56Z
dc.date.available
2023-03-14T04:52:58Z
dc.date.available
2023-03-14T09:16:56Z
dc.date.issued
2023-02
dc.identifier.isbn
979-8-4007-0015-6
en_US
dc.identifier.other
10.1145/3572848.3577480
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/603049
dc.description.abstract
Agent-based modeling plays an essential role in gaining insights into biology, sociology, economics, and other fields. However, many existing agent-based simulation platforms are not suitable for large-scale studies due to the low performance of the underlying simulation engines. To overcome this limitation, we present a novel high-performance simulation engine. We identify three key challenges for which we present the following solutions. First, to maximize parallelization, we present an optimized grid to search for neighbors and parallelize the merging of thread-local results. Second, we reduce the memory access latency with a NUMA-aware agent iterator, agent sorting with a space-filling curve, and a custom heap memory allocator. Third, we present a mechanism to omit the collision force calculation under certain conditions. Our evaluation shows an order of magnitude improvement over Biocellion, three orders of magnitude speedup over Cortex3D and NetLogo, and the ability to simulate 1.72 billion agents on a single server. Supplementary Materials, including instructions to reproduce the results, are available at: https://doi.org/10.5281/zenodo.6463816
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.subject
performance evaluation
en_US
dc.subject
scalability
en_US
dc.subject
high-performance simulation
en_US
dc.subject
memory layout optimization
en_US
dc.subject
agent-based modeling
en_US
dc.subject
memory allocation
en_US
dc.subject
performance optimization
en_US
dc.subject
space-filling curve
en_US
dc.subject
NUMA
en_US
dc.subject
parallel computing
en_US
dc.subject
HPC
en_US
dc.title
High-Performance and Scalable Agent-Based Simulation with BioDynaMo
en_US
dc.type
Conference Paper
dc.date.published
2023-02-21
ethz.book.title
PPoPP '23: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
en_US
ethz.pages.start
174
en_US
ethz.pages.end
188
en_US
ethz.event
28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP 2023)
en_US
ethz.event.location
Montreal, Canada
en_US
ethz.event.date
February 25 - March 1, 2023
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.date.deposited
2023-03-14T04:52:59Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2023-03-14T09:16:57Z
ethz.rosetta.lastUpdated
2024-02-02T20:59:09Z
ethz.rosetta.versionExported
true
ethz.COinS
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