一个墙时间最小化的平行化策略,用于近似贝叶斯计算
Emad Alamoudi1, Felipe Reck1, Nils Bundgaard2
1Life and Medical Sciences Institute, University of Bonn, Bonn, Germany.
PloS one
|February 22, 2024
概括
展望未来的调度优化了近似贝叶斯计算 (ABC) 序列蒙特卡洛算法,通过预先采样,最大限度地减少计算时间. 这一策略充分利用计算资源,提高了机械模型参数估计的效率.
科学领域:
- 计算统计学 计算统计学
- 统计建模 统计建模
- 高性能计算 高性能计算
背景情况:
- 大致贝叶斯计算 (ABC) 是复杂机械模型中参数估计的流行的方法.
- ABC分析是计算密集型的,需要对高性能计算 (HPC) 基础设施进行并行处理.
- 目前ABC的并行化策略往往导致未充分利用的计算资源和低于最佳的效率.
研究的目的:
- 引入和评估一种新的并行化策略,即"前性调度",用于ABC 序列蒙特卡洛 (SMC) 算法.
- 为了最大限度地减少墙壁时钟的时间和最大限度地利用资源,在计算上昂贵的ABC分析中.
- 为拟议的先发性抽样方法提供理论理由和经验验证.
主要方法:
- 开发一个展望未来的调度策略,包括在ABC-SMC中先发性抽样下一代.
- 预先抽样策略的理论评估和不偏见证明.
- 在各种问题和不同数量的并行核心中实施和经验评估战略.
主要成果:
- 展望未来的调度策略通过利用所有可用的资源,有效地减少了计算单元的置时间.
- 理论评估证实了先发性抽样方法的公正性.
- 经验评估表明,与既定的并行化方法相比,显著加快速度,通常为10-20%,高达50%.
结论:
- 展望日程安排在HPC基础设施上的ABC方法的成本和运行时间效率得到了大幅度的改善.
- 该策略与实用增强相兼容,例如自适应距离函数和总结统计选择.
- 这种优化的并行化方法使得使用ABC的机械模型可以更有效地估计参数.
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