Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which utilizes all available resources at practically all times by proactive sampling for prospective tasks. Our strategy can be integrated in e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Evaluation of the strategy on different problems and numbers of parallel cores reveals speed-ups of typically 10-20% and up to 50% compared to the best established approach. Thus, the proposed strategy allows to substantially improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.

翻译：近似贝叶斯计算（ABC）是一种应用广泛且颇受欢迎的机制模型未知参数估计方法。由于ABC分析计算成本高昂，通常需要在高性能基础设施上进行并行化处理。然而，现有并行化策略时常导致资源闲置，未能充分发挥其潜力。我们提出了一种前瞻调度策略，这是一种面向ABC序列蒙特卡洛算法的壁钟时间最小化并行化策略，通过为后续任务主动采样实现几乎所有时刻的全资源利用。该策略可集成至自适应距离函数与汇总统计量选择方案等实用关键环节中。针对不同问题及并行核心数量的评估显示，与最成熟的现有方法相比，该策略通常可提升10-20%的加速比，最高可达50%。因此，所提策略能显著提升ABC方法在高性能基础设施上的成本效益与运行效率。