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.
HAC SPECIS: Inria project lab on High-performance Application and Computers: Studying PErformance and Correctness In Simulation (2016-2020) :
The goal of the HAC SPECIS (High-performance Application and Computers: Studying PErformance and Correctness In Simulation) project is to answer
methodological needs of HPC application and runtime developers and to allow to study real HPC systems both from the correctness and performance point of view. To this end, we gather experts from the HPC, formal verification and performance evaluation community. website : http://hacspecis.gforge.inria.fr/
Start Date: June 2016
Avalon Members: F. Suter, L. Lefevre
In June 2014, The University of Illinois at Urbana-Champaign, Inria, the French national computer science institute, Argonne National Laboratory, Barcelona Supercomputing Center, Jülich Supercomputing Centre and the Riken Advanced Institute for Computational Science formed the Joint Laboratory for Extreme Scale Computing, a follow-up of the Inria-Illinois Joint Laboratory for Petascale Computing.
Research areas include:
- Scientific applications (big compute and big data) that are the drivers of the research in the other topics of the joint-laboratory.
- Modeling and optimizing numerical libraries, which are at the heart of many scientific applications.
- Novel programming models and runtime systems, which allow scientific applications to be updated or reimagined to take full advantage of extreme-scale supercomputers.
- Resilience and Fault-tolerance research, which reduces the negative impact when processors, disk drives, or memory fail in supercomputers that have tens or hundreds of thousands of those components.
- I/O and visualization, which are important part of parallel execution for numerical silulations and data analytics.
- HPC Clouds, that may execute a portion of the HPC workload in the near future.
More on the lab website
Start Date: 2014
Duration: 4 years
Avalon Members: T. Gautier, L. Lefevre, C. Perez, I. Rais, J. Richard