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Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

Published: 12 January 2022 Publication History

Abstract

Epidemiology models are central to understanding and controlling large-scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19. The statistical inference framework is implemented and compared on Intel’s Xeon CPU, NVIDIA’s Tesla V100 GPU, Google’s V2 Tensor Processing Unit (TPU), and the Graphcore’s Mk1 Intelligence Processing Unit (IPU), and the results are discussed in the context of their computational architectures. Results show that TPUs are 3×, GPUs are 4×, and IPUs are 30× faster than Xeon CPUs. Extensive performance analysis indicates that the difference between IPU and GPU can be attributed to higher communication bandwidth, closeness of memory to compute, and higher compute power in the IPU. The proposed framework scales across 16 IPUs, with scaling overhead not exceeding 8% for the experiments performed. We present an example of our framework in practice, performing inference on the epidemiology model across three countries and giving a brief overview of the results.

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cover image ACM Journal on Emerging Technologies in Computing Systems
ACM Journal on Emerging Technologies in Computing Systems  Volume 18, Issue 2
April 2022
411 pages
ISSN:1550-4832
EISSN:1550-4840
DOI:10.1145/3508462
  • Editor:
  • Ramesh Karri
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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 12 January 2022
Accepted: 01 June 2021
Revised: 01 March 2021
Received: 01 November 2020
Published in JETC Volume 18, Issue 2

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Author Tags

  1. Simulation-based inference
  2. likelihood-free inference
  3. COVID-19
  4. epidemiology
  5. hardware acceleration
  6. performance analysis

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