The ACROSS project will co-design and develop an HPC, BD, and Artificial Intelligence (AI) convergent platform, supporting applications in the aeronautics, climate and weather, and energy domains. To this end, ACROSS will leverage on next generation of pre-exascale infrastructures, still being ready for exascale systems, and on effective mechanisms to…
The UNIFY Associate Team aims to explore innovative approaches to workflow optimization, adaptive data management and processing through hybrid techniques leveraging the strengths of the three aforementioned ecosystems. UNIFY is a collaboration between Inria (KerData and DataMove teams) and Argonne National Laboratory in the US.
SmartFastData is an Inria Associate Team started in 2019 between the KerData team from IRISA (France) and the Network and Data Science Laboratory from Instituto Politécnico Nacional (Mexico). The goal of this project is to build a data management platform that will enable comprehensive joint analytics of past (historical) and present (real-time)…
Data@Exascale is an associated team between the KerData team from INRIA Rennes – Bretagne Atlantique, Argonne National Laboratory (ANL) and the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana Champaign (UIUC). Our research topics address the area of large scale data management for post-petascale supercomputers…
The goal of the ANR JCJC OverFlow project is to study, design, implement and evaluate the Workflow Data Management as a Service. It will treat data storage, metadata management and file transfers as first-class citizens by building on a consistent, global view of the entire distributed datacenter environment. It will…
BigStorage (2015 – 2018) has been an European Training Network (ETN) whose main aiming to train future data scientists in order to enable them and us to apply holistic and interdisciplinary approaches for taking advantage of a data-overwhelmed world, which requires HPC and Cloud infrastructures with a redefinition of storage…
The Z-CloudFlow project aims to enable data-intensive scientific workflows on geographically distributed clouds. Clouds have recently emerged as an interesting infrastructure option for deploying scientific workflows. Building on their elasticity, in recent years, scientific workflows have become an archetype to model experiments on such infrastructures. In addition, the Cloud allows…
This ANR-funded MapReduce project aims to overcome the limitations of current Map-Reduce frameworks such as Hadoop, thereby enabling highly-scalable Map-Reduce-based data processing on various physical platforms such as clouds, desktop grids, or on hybrid infrastructures built by combining these two types of infrastructures.
