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The Cherenkov Telescope Array Observatory (CTAO) is the next generation ground-based observatory for gamma-ray astronomy at very high energies. It is expected to produce about 2 PB of raw data each year and to manage a global data volume which will grow through the years to reach about 100 PB in 2030. In addition, CTAO will require a high computing capacity for data processing and Monte Carlo simulations, of the order of hundreds of millions of CPU HS06 hours per year. To meet these requirements, CTAO will adopt a distributed computing model using 4 academic data centers, and will use the DIRAC framework as its workload management system. In the past ten years, to optimize the instrument design and study its performances, CTAO has used the European Grid Infrastructure (EGI) to run massive Monte Carlo campaigns. In order to handle these campaigns and to automatize simulation and data processing workflows, we have developed a production system prototype based on DIRAC. Recently, we have also developed a user interface allowing for the configuration and submission of complex workflows. In this contribution we present the production system prototype, its user interface for workflow management as well as its application to CTAO workflows. [ABSTRACT FROM AUTHOR]

The Cherenkov Telescope Array (CTA), currently under construction, is the next-generation instrument in the field of very high energy gamma-ray astronomy. The first data are expected by the end of 2018, while the scientific operations will start in 2022 for a duration of about 30 years. In order to characterize the instrument response to the Cherenkov light emitted when cosmic ray showers develop in the atmosphere, detailed Monte Carlo simulations will be regularly performed in parallel to CTA operation. The estimated CPU time associated to these simulations is very high, of the order of 200 millions HS06 hours per year. Reducing the CPU time devoted to simulations would allow either to reduce infrastructure cost or to better cover the large phase space. In this paper, we focus on the main computing step (70% of the whole CPU time) implemented in the CORSIKA program, and specifically on the mod-ule responsible for the propagation of Cherenkov photons in the atmosphere. We present our preliminary studies about different options of code optimization, with a particular focus on vectorization facilities (SIMD instructions). Our proposals take care, as automatically as possible, of the hardware portability constraints introduced by the grid computing environment that hosts these simulations. Performance evaluation in terms of running-time and accuracy is provided. [ABSTRACT FROM AUTHOR]

The Cherenkov Telescope Array (CTA) is the next-generation instrument in the field of very high energy gamma-ray astronomy. It will be composed of two arrays of Imaging Atmospheric Cherenkov Telescopes, located at La Palma (Spain) and Paranal (Chile). The construction of CTA has just started with the installation of the first telescope on site at La Palma and the first data expected by the end of 2018. The scientific operations should begin in 2022 for a duration of about 30 years. The overall amount of data produced during these operations is around 27 PB per year. The associated computing power for data processing and Monte Carlo (MC) simulations is of the order of hundreds of millions of CPU HS06 hours per year. In order to cope with these high computing requirements, we have developed a production system prototype based on the DIRAC framework, that we have intensively exploited during the past 6 years to handle massive MC simulations on the grid for the CTA design and prototyping phases. CTA workflows are composed of several inter-dependent steps, which we used to handle separately within our production system. In order to fully automatize the whole workflows execution, we have partially revised the production system by further enhancing the data-driven behavior and by extending the use of meta-data to link together the different steps of a workflow. In this contribution we present the application of the production system to the last years MC campaigns as well as the recent production system evolution, intended to obtain a fully data-driven and automatized workflow execution for efficient processing of real telescope data. [ABSTRACT FROM AUTHOR]