FabSim3: An automation toolkit for verified simulations using high performance computing

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows

Scalable HEVC decoder for mobile devices: Trade-off between energy consumption and quality

International audienceScalable video coding offers a large choice of configurations when decoding a compressed video. A single encoded bitstream can be decoded in multiple modes, from a full video quality mode to different degraded video quality modes. In the bitstream, data is separated into layers, each layer containing the information relative to a quality level and depending on information from other layers. In the context of an energy constrained scalable video decoder executed on an embedded multicore platform, this paper investigates the energy consumption of an optimized decoder relative to the decoded layers and decoded video quality. These numbers show that a large set of trade-offs between energy and quality is offered by SHVC and can be used to precisely adapt the decoder to its energy constraints. © 2016 ECSI

French SKA White Book - The French Community towards the Square Kilometre Array

Editor in chief: C. Ferrari; Editors: M. Alves, S. Bosse, S. Corbel, A. Ferrari, K. Ferri\`ere, S. Gauffre, E. Josselin, G. Lagache, S. Lambert, G. Marquette, J.-M. Martin, M.-A. Miville-Desch\^enes, L. Montier, B. Semelin, G. Theureau, S. Vergani, N. Vilmer, P. Zarka; Original file with high resolution figures at SKA-France link: https://ska-france.oca.eu/images/SKA-France-Media/FWB_051017.pdfInternational audienceThe "Square Kilometre Array" (SKA) is a large international radio telescope project characterised, as suggested by its name, by a total collecting area of approximately one square kilometre, and consisting of several interferometric arrays to observe at metric and centimetric wavelengths. The deployment of the SKA will take place in two sites, in South Africa and Australia, and in two successive phases. From its Phase 1, the SKA will be one of the most formidable scientific machines ever deployed by mankind, and by far the most impressive in terms of data throughput and required computing power. With the participation of almost 200 authors from forty research institutes and six private companies, the publication of this French SKA white paper illustrates the strong involvement in the SKA project of the French astronomical community and of a rapidly growing number of major scientific and technological players in the fields of Big Data, high performance computing, energy production and storage, as well as system integration