Accelerating simulation for agent-based epidemic models using FPGAs
File(s)
Accepted version
Author(s)
Guo, Ce
Luk, Wayne
Weston, Stephen
Type
Conference Paper
Abstract
Agent-based models (ABMs) play a critical role in the containment and mitigation of epidemics. An ABM for epidemics involves a population of agents that represent healthy and infected individuals. Users can simulate the interactions of the agents to analyse spreading patterns, make predictions and conduct what-if tests. A major drawback that limits the application of ABMs is the long simulation time for large populations. This paper proposes new techniques to speed up ABM-based simulation. Specifically, we present an agent-based model for epidemic spreading that enables effective acceleration using reconfigurable hardware. The key idea is to compute the infection probability in a novel way so that the amount of on-chip memory usage is independent of the population size. Also, we propose a parameter pre-computation algorithm, a parallel simulation algorithm and an efficient collaboration method between the host computer and the accelerator. We simulate the proposed model on an Intel Arria 10 FPGA and compare it with a software reference that simulates a conventional ABM on an Intel Corei5-9400F CPU with six cores. The two systems produce similar results, but the FPGA-based system achieves 14 times speedup compared to the software reference.
Date Issued
2021-01-13
Date Acceptance
2020-09-15
Citation
2021
Publisher
IEEE
Copyright Statement
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Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering and Physical Sciences Research Council
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (E
Grant Number
EP/I012036/1
EP/L016796/1
516075101 (EP/N031768/1)
11908 (EP/K034448/1)
Source
17th ACS/IEEE International Conference on Computer Systems and Applications
Publication Status
Published
Start Date
2020-11-02
Finish Date
2020-11-05
Coverage Spatial
Virtual