A data-driven approach to run agent-based multi-modal traffic simulations on heterogeneous CPU-GPU hardware
OPEN ACCESS
Loading...
Author / Producer
Date
2021-03
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Abstract
In order to keep short and acceptable run times of agent-based mobility simulators that are used for scenarios, which are of increasing complexity and scale, there is need for increased computational efficiency. While, this need may be addressed by the use of heterogeneous hardware, existing traffic models may be inefficient or not run on such hardware. To simplify the development of mobility simulators with support for heterogeneous hardware, we propose a novel data-driven approach in which the data layer is built such that multiple types of hardware can yield improved run time performance. Using this novel approach, we port an existing GPU-accelerated, large-scale, multi-modal, mobility simulator to modern many-core CPUs. While, a CPU backend runs 3.89 times slower compared to a GPU backend, the CPU backend is 11.64 times faster than another widely used agent-based simulator. Moreover, the run time of the CPU backend on ARM CPUs is comparable to the run time on x86 CPUs.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
184
Pages / Article No.
720 - 727
Publisher
Elsevier
Event
10th International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03548 - Abhari, Reza S. / Abhari, Reza S.
02890 - Albert Einstein School of Public Policy / Albert Einstein School of Public Policy
Notes
Conference lecture held on March 21, 2021