通过funcX启用pyhf的分布式统计推断
Distributed statistical inference with pyhf enabled through funcX
摘要：
在高能物理中，提供高性能计算环境的设施提供了一个有效地执行大型强子对撞机数据分析所需的统计推断的机会，但可能会带来编排和有效调度的问题。这些设施的计算架构不容易支持Python计算模型，并且物理批处理作业的配置调度通常需要多个作业调度服务的专业知识。pure-Python库pyhf和funcX的组合减少了HEP分析中使用分箱模型执行统计推断的常见问题，传统上需要花费多个小时和定制调度，以按需（拟合）“功能即服务”，可以在几分钟内跨工作人员可扩展地执行，减少了洞察和推断的时间。我们演示了一个可扩展的工作流程，使用funcX同时适合125个信号假设，从发表的ATLAS搜索新的物理使用pyhf与壁时间不到3分钟。我们还显示了其他物理分析与公开发表的概率模型的性能比较，并主张在HPC中心作为服务系统的蓝图。
英文摘要：
In High Energy Physics facilities that provide High Performance Computing environments provide an opportunity to efficiently perform the statistical inference required for analysis of data from the Large Hadron Collider, but can pose problems with orchestration and efficient scheduling. The compute architectures at these facilities do not easily support the Python compute model, and the configuration scheduling of batch jobs for physics often requires expertise in multiple job scheduling services. The combination of the pure-Python libraries pyhf and funcX reduces the common problem in HEP analyses of performing statistical inference with binned models, that would traditionally take multiple hours and bespoke scheduling, to an on-demand (fitting) "function as a service" that can scalably execute across workers in just a few minutes, offering reduced time to insight and inference. We demonstrate execution of a scalable workflow using funcX to simultaneously fit 125 signal hypotheses from a published ATLAS search for new physics using pyhf with a wall time of under 3 minutes. We additionally show performance comparisons for other physics analyses with openly published probability models and argue for a blueprint of fitting as a service systems at HPC centers.
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