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  4. Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations
 
conference paper

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Tian, Zilu  
•
Lindner, Peter  
•
Nissl, Markus
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June 18, 2023
Proceedings of the ACM on Management of Data
2023 ACM SIGMOD/PODS Conference

We generalize the bulk-synchronous parallel (BSP) processing model to make it better support agent-based simulations. Such simulations frequently exhibit hierarchical structure in their communication patterns which can be exploited to improve performance. We allow for the creation of temporary artificial network partitions during which agents synchronize only locally within their group in a way that does not compromise the correctness of a simulation. We have built a distributed engine, CloudCity, which uses this idea to improve the locality of computation, communication, and synchronization in such simulations. We experimentally evaluate the performance of our system on a benchmark of simulation workloads and compare it against other popular BSP-like systems, obtaining insights into the impact of various system design choices and optimization on simulation engine performance.

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Type
conference paper
DOI
10.1145/3589296
Author(s)
Tian, Zilu  
Lindner, Peter  
Nissl, Markus
Koch, Christoph  
Tannen, Val
Date Issued

2023-06-18

Published in
Proceedings of the ACM on Management of Data
Total of pages

28

Volume

1

Issue

2, Article 151

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DATA  
Event nameEvent placeEvent date
2023 ACM SIGMOD/PODS Conference

Seattle, Washington, USA

June 18 - 23, 2023

Available on Infoscience
April 24, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/197169
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