November 17th 2020, 14:30–15:30
Title: Optimization of software license placement in the Cloud for economical and efficient deployment
Abstract: Today, the use of software is generally regulated by licenses, whether they are free, paid for and with or without access to their sources. The world of licensing is very vast and poorly understood. Often we only know the version most widely used by the general public (a software purchase is equal to a license). The reality is much more complex, especially for large publishers. In this presentation I will present the impact and importance of managing these licenses when using software in a cloud architecture. I will show a case study to demonstrate the impact of dynamic license management and the need to propose new ways to manage and optimize software assets.
Titre: Optimisation du placement des licences logicielles dans le Cloud pour un déploiement économique et efficient
Résumé: Aujourd’hui, l’utilisation des logiciels est généralement réglementée par des licences, qu’elles soient gratuites, payantes et avec ou sans accès à leurs sources. L’univers des licences est très vaste et mal connu. Souvent on ne connaît que la version la plus répandue au grand public (un achat de logiciel est égale à une licence). La réalité est bien plus complexe surtout chez les grands éditeurs. Dans cette présentation je présenterai l’impact et l’importance de la gestion de ces licences lors de l’utilisation de logiciels dans une architecture Cloud. Je montrerai un cas d’étude pour prouver l’impact de la gestion dynamique des licences et la nécessité de proposer de nouvelles façons de gérer et optimiser un patrimoine logiciel.
The Avalon 2019 report is available as well as all activity reports since 2012.
PRACE, the Partnership for Advanced Computing is the permanent pan-European High Performance Computing service providing world-class systems for world-class science. Systems at the highest performance level (Tier-0) are deployed by Germany, France, Italy, Spain and Switzerland, providing researchers with more than 17 billion core hours of compute time. HPC experts from 25 member states enabled users from academia and industry to ascertain leadership and remain competitive in the Global Race. Currently PRACE is finalizing the transition to PRACE 2, the successor of the initial five year period. The objectives of PRACE-6IP are to build on and seamlessly continue the successes of PRACE and start new innovative and collaborative activities proposed by the consortium. These include: assisting the development of PRACE 2; strengthening the internationally recognised PRACE brand; continuing and extend advanced training which so far provided more than 36 400 person·training days; preparing strategies and best practices towards Exascale computing, work on forward-looking SW solutions; coordinating and enhancing the operation of the multi-tier HPC systems and services; and supporting users to exploit massively parallel systems and novel architectures. A high level Service Catalogue is provided. The proven project structure will be used to achieve each of the objectives in 7 dedicated work packages. The activities are designed to increase Europe’s research and innovation potential especially through: seamless and efficient Tier-0 services and a pan-European HPC ecosystem including national capabilities; promoting take-up by industry and new communities and special offers to SMEs; assistance to PRACE 2 development; proposing strategies for deployment of leadership systems; collaborating with the ETP4HPC, CoEs and other European and international organisations on future architectures, training, application support and policies. This will be monitored through a set of KPIs.
The goal of PMSISEE is to support the collaboration between the Avalon (LIP) and Beagle (LIRIS) teams through research activities on programming modelsand tools for HPC applied to the Aevol/R-Aevol simulator of in silico evolution of bacteria.
A population of organisms adapting to a new environment is a dynamic system changing over time at many levels (molecules, networks, individuals, ecosystems). A large amount of empirical and theoretical evidence indicates that in real populations all these levels interact, making the dynamics of adaptation a highly complex phenomenon. In order to understand bacterial evolution, we need large-scale integrative models in which all relevant levels from the molecule to the ecology are simulated. The Aevol/R-Aevol simulator (http://www.aevol.fr) has been developed by the Beagle team to address such questions. Aevol integrates the molecular and cellular levels to address the evolution of genomic complexity. R-Aevol adds the network level to investigate the evolution of network complexity.
In this project we consider the Aevol/R-Aevol simulator, or equivalent code, as the object of the study. At a first glance, it is characterized by several properties: the code is complex due the models to integrate; the amount of computational resources required for simulations is huge when considering the size of the systems (millions of base pairs in the genome, thousands of genes in the genetic network, billions of individuals in the population, billions of generations); load unbalance occurs when running the models under different conditions ( i.e., different parameters). Any gain in performance, will make these simulations very valuable to understand bacterial evolution and to have feedback on the biological models in order to improve them.
The research during the PMSISEE project will be restricted to two main issues related to the software and its algorithms: 1/ analysis and design of specialized models to tackle software complexity in the context of HPC using next generation of parallel supercomputers. This point is based on advances in software engineering of these last twenty years in particular with respect to code composability and re-use using component model; 2/ performance analysis and design of new, or improvement of existing, algorithms for scalable and efficient simulation of evolving bacterial populations on modern parallel architecture. This axis will deal with heuristics for scheduling in order to well balance the work load and reducing communication.