Section: Partnerships and Cooperations

International Initiatives

Inria International Labs

JLESC: Joint Laboratory on Extreme Scale Computing

The Joint Laboratory on Extreme Scale Computing is jointly run by Inria, UIUC, ANL, BSC, JSC and RIKEN. It has ben created in 2014 as a follow-up of the Inria-UIUC JLPC — Joint Laboratory for Petascale Computing to collaborate on concurrency-optimized I/O for Extreme-scale platforms (see details in Section  7.4 ). The KerData team is collaborating with teams from ANL and UIUC within this lab since 2009. This collaboration has now been formalized as the Data@Exascale Associate Team with ANL and UIUC (2013–2015).

Associate Team involved in the International Lab: Data@Exascale

Title:

Ulta-scalable I/O and storage for Exascale systems

International Partner:

Argonne National Laboratory (United States) — Mathematics and Computer Science Division (MCS) — Robert Ross

Start year:

2013

URL:

http://www.irisa.fr/kerdata/data-at-exascale/

As the computational power used by large-scale scientific applications increases, the amount of data manipulated for subsequent analysis increases as well. Rapidly storing this data, protecting it from loss and analyzing it to understand the results are significant challenges, made more difficult by decades of improvements in computation capabilities that have been unmatched in storage. For many applications, the overall performance and scalability clearly become driven by the performance of the I/O subsystem. As we anticipate Exascale systems in 2020, there is a growing consensus in the scientific community that revolutionary new approaches are needed in computational science storage. These challenges are at the center of the activities of the Joint Inria-Illinois-ANL-BSC-JSC-RIKEN/AICS Laboratory for Extreme-Scale Computing (JLESC, formerly called JLPC). This project gathers researchers from Inria, Argonne National Lab and the University of Illinois at Urbana Champaign to address 3 goals: 1) investigate new storage architectures for Exascale systems; 2) investigate new approaches to the design of I/O middleware for Exascale systems to optimize data processing and visualization, leveraging dedicated I/O cores and I/O forwarding techniques; 3) explore techniques enabling adaptive cloud data services for HPC.

Inria International Partners

DataCloud@work

Title:

DataCloud@Work — Distributed data management for cloud services

International Partner:

Politehnica University of Bucharest (Romania) — Computer Science and Engineering Department — Valentin Cristea and Nicolae Tapus

Start year:

January 2013. The status of IIP was established right after the end of our former DataCloud@work Associate Team (2010–2012).

URL:

https://www.irisa.fr/kerdata/doku.php?id=cloud_at_work:start

Our research topics address the area of distributed data management for cloud services, focusing on autonomic storage. The goal is explore how to build an efficient, secure and reliable storage IaaS for data-intensive distributed applications running in cloud environments by enabling an autonomic behavior.