An Innovative Workspace for The Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) is an initiative to build the next generation, ground-based gamma-ray observatories. We present a prototype workspace developed at INAF that aims at providing innovative solutions for the CTA community. The workspace leverages open source technologies providing web access to a set of tools widely used by the CTA community. Two different user interaction models, connected to an authentication and authorization infrastructure, have been implemented in this workspace. The first one is a workflow management system accessed via a science gateway (based on the Liferay platform) and the second one is an interactive virtual desktop environment. The integrated workflow system allows to run applications used in astronomy and physics researches into distributed computing infrastructures (ranging from clusters to grids and clouds). The interactive desktop environment allows to use many software packages without any installation on local desktops exploiting their native graphical user interfaces. The science gateway and the interactive desktop environment are connected to the authentication and authorization infrastructure composed by a Shibboleth identity provider and a Grouper authorization solution. The Grouper released attributes are consumed by the science gateway to authorize the access to specific web resources and the role management mechanism in Liferay provides the attribute-role mapping

The MPI+CUDA Gaia AVU-GSR Parallel Solver in perspective of next-generation Exascale Infrastructures and new Green Computing milestones

We ported on the GPU with CUDA the Gaia Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver. The code aims to find the astrometric parameters of ~10^8 stars in the Milky Way, the attitude and the instrumental settings of the Gaia satellite, and the global parameter of the PPN formalism, by solving a system of linear equations, × = , with the LSQR iterative algorithm. The coefficient matrix is large, having ~10^11 × 10^8 elements, and sparse. The CUDA code accelerates ≳ 14 times compared to the current version of the AVU-GSR code, parallelized on the CPU with MPI+OpenMP and in production since 2014. This acceleration factor is ~9.2 times larger than the one obtained with a preliminary GPU porting with OpenACC, equal to ~1.5. We obtained this result by running the codes on the CINECA SuperComputer Marconi100, that has 4 NVIDIA Volta V100 GPUs per node, where the MPI+CUDA application has been recently put in production. This analysis represents a first step to understand the exascale behaviour of a class of applications that follow the same structure of this code, employed in several contexts. In the next months, we plan to run this code on the pre-exascale platform Leonardo of CINECA, with 4 next-generation A100 GPUs per node, to better investigate this behaviour. Computing on highly parallel devices, such as GPUs, might imply a consistent power saving, which might go towards the achievement of a Green Computing milestone

The Global sphere reconstruction (GSR) - Demonstrating an independent implementation of the astrometric core solution for Gaia

Context. The Gaia ESA mission will estimate the astrometric and physical data of more than one billion objects, providing the largest and most precise catalog of absolute astrometry in the history of Astronomy. The core of this process, the so-called global sphere reconstruction, is represented by the reduction of a subset of these objects which will be used to define the celestial reference frame. As the Hipparcos mission showed, and as is inherent to all kinds of absolute measurements, possible errors in the data reduction can hardly be identified from the catalog, thus potentially introducing systematic errors in all derived work. Aims. Following up on the lessons learned from Hipparcos, our aim is thus to develop an independent sphere reconstruction method that contributes to guarantee the quality of the astrometric results without fully reproducing the main processing chain. Methods. Indeed, given the unfeasibility of a complete replica of the data reduction pipeline, an astrometric verification unit (AVU) was instituted by the Gaia Data Processing and Analysis Consortium (DPAC). One of its jobs is to implement and operate an independent global sphere reconstruction (GSR), parallel to the baseline one (AGIS, namely Astrometric Global Iterative Solution) but limited to the primary stars and for validation purposes, to compare the two results, and to report on any significant differences. Results. Tests performed on simulated data show that GSR is able to reproduce at the sub-$\mu$as level the results of the AGIS demonstration run presented in Lindegren et al. (2012). Conclusions. Further development is ongoing to improve on the treatment of real data and on the software modules that compare the AGIS and GSR solutions to identify possible discrepancies above the tolerance level set by the accuracy of the Gaia catalog.Comment: Accepted for publication on Astronomy & Astrophysic

Prospects for Weak Lensing surveys with next-generation arrays

Recently, R. Cen (2006) has suggested that small protogalactic halos at high redshift (z ' 20− 25) could be surrounded by extended (r ≈ 1h−1Mpc) gaseous halos, which could be marginally detected by LOFAR. However, more recent work on the mass function at high z shows that these estimates could be too optimistic. Moreover, the variance over the sky of this signal could be very large, thus undermining the practical usage of these halos for the determination of cosmological parameters using weak lensing

The MPI + CUDA Gaia AVU-GSR Parallel Solver Toward Next-generation Exascale Infrastructures

We ported to the GPU with CUDA the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) Parallel Solver developed for the ESA Gaia mission, by optimizing a previous OpenACC porting of this application. The code aims to find, with a [10,100]$\mu$as precision, the astrometric parameters of $\sim$$10^8$ stars, the attitude and instrumental settings of the Gaia satellite, and the global parameter $\gamma$ of the parametrized Post-Newtonian formalism, by solving a system of linear equations, $A\times x=b$, with the LSQR iterative algorithm. The coefficient matrix $A$ of the final Gaia dataset is large, with $\sim$$10^{11} \times 10^8$ elements, and sparse, reaching a size of $\sim$10-100 TB, typical for the Big Data analysis, which requires an efficient parallelization to obtain scientific results in reasonable timescales. The speedup of the CUDA code over the original AVU-GSR solver, parallelized on the CPU with MPI+OpenMP, increases with the system size and the number of resources, reaching a maximum of $\sim$14x, >9x over the OpenACC application. This result is obtained by comparing the two codes on the CINECA cluster Marconi100, with 4 V100 GPUs per node. After verifying the agreement between the solutions of a set of systems with different sizes computed with the CUDA and the OpenMP codes and that the solutions showed the required precision, the CUDA code was put in production on Marconi100, essential for an optimal AVU-GSR pipeline and the successive Gaia Data Releases. This analysis represents a first step to understand the (pre-)Exascale behavior of a class of applications that follow the same structure of this code. In the next months, we plan to run this code on the pre-Exascale platform Leonardo of CINECA, with 4 next-generation A200 GPUs per node, toward a porting on this infrastructure, where we expect to obtain even higher performances.Comment: 17 pages, 4 figures, 1 table, published on 1st August 2023 in Publications of the Astronomical Society of the Pacific, 135, 07450

Toward porting Astrophysics Visual Analytics Services to the European Open Science Cloud

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps

astrophysical data analysis and visualization toolkit

AstroMD is a visualization software offering several capabilities specifically oriented to the cosmological analysis of three-dimensional structures. It was developed within the framework of the Cosmo.Lab project, financially supported by the European Community, which involves several European Astrophysical Institutions and the CINECA. This tool gives a 3D graphic representation of data, exploiting the most advanced visualization technology based on virtual reality, and has several built-in-tools which allow the user an efficient manipulation and analysis of data, in order to build a leading edge instrument for scientific research. It was developed using the Visualization Toolkit (VTK) by Kitware, a freely available visualization library portable on many platforms. AstroMD is an open source freely available code at the address http://cosmolab.cineca.it/