Fine-Grained Processing Towards HL-LHC Computing in ATLAS

D. Benjamin, P. Calafiura, T. Childers, K. De, A. Girolamo, E. Fullana, W. Guan, T. Maeno, Nicolò Magini, P. Nilsson, D. Oleynik, Shaojun Sun, V. Tsulaia, P. Gemmeren, T. Wenaus, W. Yang
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Abstract

During LHC's Run-2 ATLAS has been developing and evaluating new fine-grained approaches to workflows and dataflows able to better utilize computing resources in terms of storage, processing and networks. The compute-limited physics of ATLAS has driven the collaboration to aggressively harvest opportunistic cycles from what are often transiently available resources, including HPCs, clouds, volunteer computing, and grid resources in transitional states. Fine-grained processing (with typically a few minutes' granularity, corresponding to one event for the present ATLAS full simulation) enables agile workflows with a light footprint on the resource such that cycles can be more fully and efficiently utilized than with conventional workflows processing O(GB) files per job.
ATLAS中HL-LHC计算的细粒度处理
在LHC Run-2期间,ATLAS一直在开发和评估新的细粒度工作流和数据流方法,这些方法能够更好地利用存储、处理和网络方面的计算资源。ATLAS的计算有限的物理特性促使协作积极地从通常是暂时可用的资源中获取机会周期,包括hpc、云、志愿计算和过渡状态的网格资源。细粒度处理(通常为几分钟的粒度,对应于当前ATLAS完整模拟的一个事件)使灵活的工作流对资源的占用很少,这样与每个作业处理0 (GB)文件的传统工作流相比,可以更充分、更有效地利用周期。
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