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RECIPE

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REliable power and time ConstraInts aware Predictive management of heterogeneous...

REliable power and time ConstraInts aware Predictive management of heterogeneous Exascale systems 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 heterogen... ver más

31/10/2021

POLIMI

3M€

Presupuesto del proyecto: 3M€

Líder del proyecto

POLITECNICO DI MILANO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 9 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-10-31

FETHPC-02-2017: Transition to Exascale Computing Specific Challenge:Take advantage of the full capabilities of exascale computing, in particular thro...

I+D

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

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9 Participantes

POLIMI

705.00K€ | Lider

KANTERON SYSTEMS

329.60K€ | Participante

INSTYTUT CHEMII BIOORGANICZN...

397.25K€ | Participante

EPFL

465.25K€ | Participante

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Duración del proyecto: 42 meses Fecha Inicio: 2018-04-18
Fecha Fin: 2021-10-31

Líder del proyecto

POLITECNICO DI MILANO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 3M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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

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