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DAMA

Extreme-Scale Data Management

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "DAMA" data sheet

The following table provides information about the project.

Coordinator
INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE 

Organization address
address: DOMAINE DE VOLUCEAU ROCQUENCOURT
city: LE CHESNAY CEDEX
postcode: 78153
website: www.inria.fr

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Project website https://francielizanon.github.io/
 Total cost 185˙076 €
 EC max contribution 185˙076 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2017
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-11-01   to  2020-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    INSTITUT NATIONAL DE RECHERCHE ENINFORMATIQUE ET AUTOMATIQUE FR (LE CHESNAY CEDEX) coordinator 185˙076.00

Map

 Project objective

This project is concerned with the I/O challenges that arise from the convergence between high performance computing (HPC) and big data, two very different paradigms. This convergence is an important topic for the scientific community today, and extreme-scale machines are expected to observe a heterogeneous workload composed of traditional scientific applications and data analytics tasks. The goal of this action is to provide data management for extreme-scale computing environments for the convergence scenario, to benefit both types of workload. The methodology will be an experimental one, and the instrument will be the development of an I/O middleware, the data manager. The data manager will combine storage capacity available in the supercomputer, including NVRAM devices, transparently. Its activities will be optimized by minimizing data movement and applying coordination to avoid performance interference due to concurrency. The most important characteristic of this project among the state-of-the-art is the intelligence to learn and predict applications needs, so storage capacity and data can be available at a close location before the user needs them. The action will benefit from the researcher's experience on parallel I/O for HPC, allied to the host laboratory expertise in in-situ processing, big data, and machine learning. Through this two-year fellowship, the researcher will have the opportunity to expand her knowledge while conducting highly innovative research, what will improve her perspectives for future employment.

 Publications

year authors and title journal last update
List of publications.
2019 Jean Luca Bez, Francieli Zanon Boito, Ramon Nou, Alberto Miranda, Toni Cortes, Philippe Navaux
Adaptive Request Scheduling for the I/O Forwarding Layer using Reinforcement Learning
published pages: , ISSN: , DOI:
pre-print, submitted 2020-02-17
2019 Luca Bez, Jean; Zanon Boito, Francieli; Nou, Ramon; Miranda, Alberto; Cortes, Toni; Navaux, Philippe,
Detecting I/O Access Patterns of HPC Workloads at Runtime
published pages: , ISSN: , DOI:
The International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Oct 2019, Campo Grande, Brazil 2020-02-17
2019 Zanon Boito, Francieli; Nou, Ramon; Lima Pilla, Laércio; Luca Bez, Jean; Méhaut, Jean-François; Cortes, Toni; Navaux, Philippe
On server-side file access pattern matching
published pages: , ISSN: , DOI:
HPCS 2019 - 17th International Conference on High Performance Computing & Simulation, Jul 2019, Dublin, Ireland. pp.1-8 2020-02-17
2019 Pablo Pavan, Jean Luca Bez, Matheus Serpa, Francieli Zanon Boito, Philippe Navaux
An Unsupervised Learning Approach for I/O Behavior Characterization
published pages: , ISSN: , DOI:
The International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Oct 2019, Campo Grande, Brazil 2020-02-17
2019 Jean Luca Bez, André Ramos Carneiro, Pablo José Pavan, Valéria Soldera Girelli, Francieli Zanon Boito, Bruno Alves Fagundes, Carla Osthoff, Pedro Leite da Silva Dias, Jean-François Méhaut, Philippe OA Navaux
I/O performance of the Santos Dumont supercomputer
published pages: 109434201986852, ISSN: 1094-3420, DOI: 10.1177/1094342019868526
The International Journal of High Performance Computing Applications 2020-02-17

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