基于硬件加速仿真的COVID-19随机流行病学模型推理
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
摘要：
流行病学模型是理解和控制大规模流行病的核心。一些流行病学模型需要基于模拟的推理，如近似贝叶斯计算（ABC），以适应其参数的观察。ABC推理非常适合高效的硬件加速。在这项工作中，我们开发了COVID-19随机流行病学模型的并行ABC推理。统计推理框架在Intel Xeon CPU、NVIDIA Tesla V100 GPU和Graphcore Mk 1 IPU上实现并进行了比较，并在其计算架构的背景下讨论了结果。结果显示，GPU是Xeon CPU的4倍，IPU是Xeon CPU的30倍。广泛的性能分析表明，IPU和GPU之间的差异可以归因于更高的通信带宽，内存与计算的紧密性，以及IPU中更高的计算能力。所提出的框架可扩展到16个IPU，进行的实验的扩展开销不超过8%。我们提出了我们的框架在实践中的一个例子，在三个国家的流行病学模型上进行推理，并简要概述了结果。
英文摘要：
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.
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