CERN Accelerating science

Published Articles
Title	Fine-Grained Processing Towards HL-LHC Computing in ATLAS
Author(s)	Benjamin, Doug (Duke U.) ; Calafiura, Paolo (LBNL, Berkeley) ; Childers, Taylor (Argonne) ; De, Kaushik (Texas U., Arlington) ; Di Girolamo, Alessandro (CERN) ; Fullana, Esteban (Valencia U.) ; Guan, Wen (Wisconsin U., Madison) ; Maeno, Tadashi (Brookhaven Natl. Lab.) ; Magini, Nicolo (U. Genoa (main)) ; Nilsson, Paul (Brookhaven Natl. Lab.) ; Oleynik, Danila (Texas U., Arlington) ; Sun, Shaojun (Wisconsin U., Madison) ; Tsulaia, Vakho (LBNL, Berkeley) ; Van Gemmeren, Peter (Argonne) ; Wenaus, Torre (Brookhaven Natl. Lab.) ; Yang, Wei (SLAC)
Collaboration	ATLAS
Publication	2018
Number of pages	1
In:	14th eScience IEEE International Conference, Amsterdam, Netherlands, 29 Oct - 1 Nov 2018, pp.338
DOI	10.1109/eScience.2018.00083
Subject category	Computing and Computers ; Detectors and Experimental Techniques
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
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.

Corresponding record in: Inspire