CERN Accélérateur de science

ATLAS Slides
Report number	ATL-SOFT-SLIDE-2018-710
Title	BigPanDA Experience on LCF and HPC for the ATLAS Experiment at the LHC and data intensive science
Author(s)	De, Kaushik (The University of Texas at Arlington) ; Barreiro Megino, Fernando Harald (The University of Texas at Arlington) ; Maeno, Tadashi (Brookhaven National Laboratory (BNL)) ; Oleynik, Danila (Joint Institute for Nuclear Research) ; Mashinistov, Ruslan (P.N. Lebedev Physical Institute) ; Svirin, Pavlo (Brookhaven National Laboratory (BNL))
Corporate author(s)	The ATLAS collaboration
Collaboration	ATLAS Collaboration
Submitted by	[email protected] on 12 Sep 2018
Subject category	Particle Physics - Experiment
Accelerator/Facility, Experiment	CERN LHC ; ATLAS
Free keywords	ATLAS ; BigPanDA ; LCF ; HPC
Abstract	The PanDA software is used for workload management on distributed grid resources by the ATLAS experiment at the LHC. An effort was launched to extend PanDA, called BigPanDA, to access HPC resources, funded by the US Department of Energy (DOE-ASCR). Through this successful effort, ATLAS today uses over 25 million hours monthly on the Titan supercomputer at Oak Ridge National Laboratory. Many challenges were met and overcome in using HPCs for ATLAS simulations. ATLAS uses two different operational modes at Titan. The traditional mode uses allocations - which require software innovations to fit the low latency requirements of experimental science. New techniques were implemented to shape large jobs using allocations on a leadership class machine. In the second mode, high priority work is constantly sent to Titan to backfill high priority leadership class jobs. This has resulted in impressive gains in overall utilization of Titan, while benefiting the physics objectives of ATLAS. For both modes, BigPanDA has integrated traditional grid computing with HPC architecture. This talk will summarize the innovations to successfully use Titan for LHC physics goals