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

TL;DR

This paper develops a hardware-accelerated parallel inference framework for stochastic COVID-19 epidemiology models using ABC, demonstrating significant speedups on GPUs and IPUs, and scaling across multiple IPUs for large-scale inference.

Contribution

It introduces a parallel ABC inference framework optimized for different hardware architectures, including IPUs, GPUs, and CPUs, with detailed performance analysis and scalability results.

Findings

GPUs are 4x faster than CPUs

IPUs are 30x faster than CPUs

The framework scales efficiently across 16 IPUs

Abstract

Epidemiology models are central in 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 Xeon CPU, NVIDIA Tesla V100 GPU and the Graphcore Mk1 IPU, and the results are discussed in the context of their computational architectures. Results show that GPUs are 4x and IPUs are 30x 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.