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SPECIAL
Project |
SPECIAL |
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TITLE |
Scalable Policy-awarE linked data arChitecture for prIvacy, trAnsparency and compLiance |
Acronym | SPECIAL |
Project ID | 731601 | Call | H2020-ICT-2016-1 |
Programme | H2020 | Rdg | CNECT |
Activity | Big data PPP: privacy-preserving big data technologies | ||
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Abstract
Abstract | |||
The SPECIAL project will address the contradiction between Big Data innovation and privacy-aware data protection by proposing a technical solution that makes both of these goals realistic. We will develop technology that: (i) supports the acquisition of user consent at collection time and the recording of both data and metadata (consent, policies, event data, context) according to legislative and user-specified policies; (ii) caters for privacy-aware, secure workflows that include usage/access control, transparency and compliance verification; (iii) demonstrates robustness in terms of performance, scalability and security all of which are necessary to support privacy preserving innovation in Big Data environments; and (iv) provides a dashboard with feedback and control features that make privacy in Big Data comprehensible and manageable for data subjects, controllers, and processors. SPECIAL shall allow citizens and organisations to share more data, while guaranteeing data protection compliance, thus enabling both trust and the creation of valuable new insights from shared data. Our vision will be realised and validated via real world use cases that - in order to be viable - need to overcome current challenges concerning the processing and sharing of data in a privacy preserving manner. In order to realise this vision, we will combine and significantly extend big data architectures to handle Linked Data, harness them with sticky policies as well as scalable queryable encryption, and develop advanced user interaction and control features: SPECIAL will build on top of the Big Data Europe and PrimeLife Projects, exploit their results, and further advance the state of the art of privacy enhancing technologies. |
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RECIPE
Project |
RECIPE![]() |
Title | REliable power and time-Constrain-aware Predictive management of heterogeneous Exascale systems | Acronym | RECIPE |
Project ID | 801137 | Call | H2020-FETHPC-2017 |
Programme | H2020 | ||
Activity | HTPC, cloud security, multi-cloud, distributed application, heterogeneous cloud, security SLA, decision support, deployment, monitoring, enforcement, security assurance, DevOps, lifecycle management | ||
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Abstract
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. |
Partner
MANGO
Project | MANGO![]() |
Title |
MANGO: exploring Manycore Architectures for Next-GeneratiOn HPC systems |
Acronym | MANGO |
Project ID | 671668 | Call | H2020-FETHPC-2014 |
Programme | H2020 | Rdg | CNECT |
Activity | Real-time HPC, power-performance-predictability, capacity computing, partitionability, reconfigurability | ||
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Abstract
Abstract | |||
MANGO targets to achieve extreme resource efficiency in future QoS-sensitive HPC through ambitious crossboundary architecture exploration for performance/power/predictability (PPP) based on the definition of newgeneration high-performance, power-efficient, heterogeneous architectures with native mechanisms for isolation and quality-of-service, and an innovative two-phase passive cooling system. Its disruptive approach will involve many interrelated mechanisms at various architectural levels, including heterogeneous computing cores, memory architectures, interconnects, run-time resource management, power monitoring and cooling, to the programming models. The system architecture will be inherently heterogeneous as an enabler for efficiency and applicationbased customization, where general-purpose compute nodes (GN) are intertwined with heterogeneous acceleration nodes (HN), linked by an across-boundary homogeneous interconnect. It will provide guarantees for predictability, bandwidth and latency for the whole HN node infrastructure, allowing dynamic adaptation to applications. MANGO will develop a toolset for PPP and explore holistic pro-active thermal and power management for energy optimization including chip, board and rack cooling levels, creating a hitherto inexistent link between HW and SW effects at all layers. Project will build an effective large-scale emulation platform. The architecture will be validated through noticeable examples of application with QoS and high-performance requirements.Ultimately, the combined interplay of the multi-level innovative solutions brought by MANGO will result in a new positioning in the PPP space, ensuring sustainable performance as high as 100 PFLOPS for the realistic levels of power consumption (<15MWatt) delivered to QoS-sensitive applications in large-scale capacity computing scenarios providing essential building blocks at the architectural level enabling the full realization of the ETP4HPC strategic research agenda. |
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