Associated joint team between Avalon and RDI2 Lab. in Rutgers University on Sustainable Ultra Scale compuTing, dAta and energy Management
The collaboration proposed by the SUSTAM associate team focuses on aspects of sustainability in ultra-scale systems. Launched in 2017, the SUSTAM associated team aims to design a multi-criteria orchestration framework that manages resources, data and energy consumption in an efficient manner. The SUSTAM associate team will enable a long-term collaboration between the Inria Avalon and the RDI² team (Rutgers University . It will allow the teams to coordinate efforts and pursue common research activities in topics such as sustainable software solutions, resource and big-data management, elasticity of stream and batch applications, and energy efficiency. The involved members will contribute to the design of a common architecture and framework with components and algorithms adapted to various contexts.
Web Site: http://avalon.ens-lyon.fr/sustam/
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.
ELCI is a French software project that brings together academic and industrial partners to design and provide a software environment for the next generation of HPC systems. The principal objective for the project is to facilitate the development of a software environment that meets the demands of the new generation of HPC architectures. This will cover the whole software stack (system and programming environments), numerical solvers and pre/post/co processing software.
ELCI is a French software project that brings together academic and industrial partners to design and provide a software environment for the next generation of HPC systems. The project is funded by the participating partners and by the French FSN “Fond pour la Société Numérique”.
The principal objective for the project is to facilitate the development of a software environment that meets the demands of the new generation of HPC architectures. This will cover the whole software stack (system and programming environments), numerical solvers and pre/post/co processing software.
A co-design approach is employed, that covers the software environment for computer architectures, the requirements of more demanding applications, and is adapted to future hardware architectures (multicore/many core processors, high-speed networks and data storage).
These developments will be validated according to their capacity to deal with the new exascale challenges- larger scalability, higher resiliency, greater security, improved modularity, with better abstraction and interactivity for application cases.
Start Date: September 2014
Duration: 3 years
Avalon Members: T. Gautier, L.Lefevre, C. Perez, I. Rais, J. Richard
More information on the ELCI web site.
LEXISTEMS develops Xact.ai, a solution to provide an universal access to knowledge in Natual Langage (data & data’s structuration limitless).
For organizations, Xact.ai is the most effective way to monetize data assets. Whatever the nature and volume of knowledge bases.
LEXISTEMS’ solutions streamline the use and analysis of natural language in business and personal applications.
A new era is opening. Users are empowered, and organizations leverage the true value of their data assets.
LEXISTEMS and Avalon collaborate on the design and development of NLP algorithms and high-level data structuration.
Start Date: September 2016
Avalon Members: Marcos Assuncao, Eddy Caron and Thomas Pellisier-Tanon
More information on website: LEXISTEMS