Opendata, web and dolomites

RECIPE SIGNED

REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 RECIPE project word cloud

Explore the words cloud of the RECIPE project. It provides you a very rough idea of what is the project "RECIPE" about.

goals    geophysical    interact    bsc    power    ibts    computation    underlying    proactive    efficiency    timing    manager    integration    watt    grow    centers    polimi    evolution    hpc    few    cerict    deep    throughput    time    epfl    budgets    magnitude    exascale    mttf    qos    energy    gap    provides    heterogeneous    facilities    15    exploration    close    cases    resource    recipe    domains    machine    critical    20    25    infrastructure    data    world    academic    quantitative    enforcing    decreasing    optimizing    layers    issue    guarantees    psnc    analytics    heterogeneity    runtime    reliability    improvement    middleware    transient    mean    relies    industry    failures    thermal    predictive    performance    chuv    learning    sme    delay    variety    architecture    reasonable    faulty    meteorology    biomedical    deeply    supercomputing    handle    hospital    avenues    ranging    executions    complexity    enormous    methodology    oriented    models    hierarchical    disaggregate    upv    hardware   

Project "RECIPE" data sheet

The following table provides information about the project.

Coordinator
POLITECNICO DI MILANO 

Organization address
address: PIAZZA LEONARDO DA VINCI 32
city: MILANO
postcode: 20133
website: www.polimi.it

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 Italy [IT]
 Total cost 3˙290˙800 €
 EC max contribution 3˙285˙300 € (100%)
 Programme 1. H2020-EU.1.2.2. (FET Proactive)
 Code Call H2020-FETHPC-2017
 Funding Scheme RIA
 Starting year 2018
 Duration (year-month-day) from 2018-05-01   to  2021-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    POLITECNICO DI MILANO IT (MILANO) coordinator 705˙000.00
2    ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE CH (LAUSANNE) participant 465˙250.00
3    UNIVERSITAT POLITECNICA DE VALENCIA ES (VALENCIA) participant 437˙000.00
4    BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION ES (BARCELONA) participant 410˙500.00
5    INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK PL (POZNAN) participant 397˙250.00
6    Centro Regionale Information Communication Technology scrl IT (Benevento) participant 395˙500.00
7    INTELLIGENCE BEHIND THINGS SOLUTIONS SRL IT (MONZA) participant 290˙500.00
8    CENTRE HOSPITALIER UNIVERSITAIRE VAUDOIS CH (LAUSANNE) participant 184˙300.00

Map

 Project objective

The current HPC facilities will need to grow by an order of magnitude in the next few years to reach the Exascale range. The dedicated middleware needed to manage the enormous complexity of future HPC centers, where deep heterogeneity is needed to handle the wide variety of applications within reasonable power budgets, will be one of the most critical aspects in the evolution of HPC infrastructure towards Exascale. This middleware will need to address the critical issue of reliability in face of the increasing number of resources, and therefore decreasing mean time between failures. To close this gap, RECIPE provides: a hierarchical runtime resource management infrastructure optimizing energy efficiency and ensuring reliability for both time-critical and throughput-oriented computation; a predictive reliability methodology to support the enforcing of QoS guarantees in face of both transient and long-term hardware failures, including thermal, timing and reliability models; and a set of integration layers allowing the resource manager to interact with both the application and the underlying deeply heterogeneous architecture, addressing them in a disaggregate way. Quantitative goals for RECIPE include: 25% increase in energy efficiency (performance/watt) with an 15% MTTF improvement due to proactive thermal management; energy-delay product improved up to 25%; 20% reduction of faulty executions. The project will assess its results against the following set of real world use cases, addressing key application domains ranging from well established HPC applications such as geophysical exploration and meteorology, to emerging application domains such as biomedical machine learning and data analytics. To this end, RECIPE relies on a consortium composed of four leading academic partners (POLIMI,UPV,EPFL,CeRICT); two supercomputing centers, BSC and PSNC; a research hospital, CHUV, and an SME, IBTS, which provide effective exploitation avenues through industry-based use cases

 Publications

year authors and title journal last update
List of publications.
2019 Federico Reghenzani, Giuseppe Massari, William Fornaciari
The Misconception of Exponential Tail Upper-Bounding in Probabilistic Real-Time
published pages: 1-1, ISSN: 1943-0663, DOI: 10.1109/les.2018.2889114
IEEE Embedded Systems Letters 2020-01-27

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "RECIPE" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "RECIPE" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.2.2.)

SYNCH (2019)

A SYnaptically connected brain-silicon Neural Closed-loop Hybrid system

Read More  

CHIST-ERA IV (2019)

European Coordinated Research on Long-term ICT and ICT-based Scientific and Technological Challenges

Read More  

EnTimeMent (2019)

EnTimeMent - ENtrainment and synchronization at multiple TIME scales in the MENTal foundations of expressive gesture

Read More