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CoupledDB

High-Performance Indexing for Emerging GPU-Coupled Databases

Total Cost €

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EC-Contrib. €

0

Partnership

0

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 CoupledDB project word cloud

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

innovation    parallel    responsive    units    principal    sweeping    lower    foundational    frequently    expertise    feasible    straight    industry    proliferation    host    small    ecosystem    accessed    disruptive    heterogeneous    action    leverage    preliminary    gpu    processed    competitiveness    setting    types    exploits    dichotomy    exclusively    database    parallelism    connected    techniques    stage    indexes    architecture    faster    data    cross    fact    indexing    algorithms    energy    index    vastly    positioning    objects    squander    productivity    ubiquitous    disk    multicore    coupled    gpus    neurons    mobile    computational    powerful    simulations    forefront    soon    scientific    deescalating    structures    programming    skills    confluence    valuable    forwardly    computation    cheaper    cut    trajectories    idle    footprint    graphics    heterogeneity    researcher    handling    model    performance    transfer    hardware    memory    accelerators    greener    incorporating    hot    compute    building    becomes    suggest    vehicle    commonplace   

Project "CoupledDB" data sheet

The following table provides information about the project.

Coordinator
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU 

Organization address
address: HOGSKOLERINGEN 1
city: TRONDHEIM
postcode: 7491
website: www.ntnu.no

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 Norway [NO]
 Project website https://www.ntnu.edu/idi/groups/dart
 Total cost 208˙400 €
 EC max contribution 208˙400 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-05-01   to  2019-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU NO (TRONDHEIM) coordinator 208˙400.00

Map

 Project objective

'Index structures are foundational to the performance of database systems and large-scale simulations. Even small advances in indexing can therefore have widespread, sweeping impact on both industry competitiveness and scientific productivity. The confluence of several hardware trends is setting the stage for disruptive innovation in database indexing: deescalating costs of memory make it feasible to organise most of the 'hot', frequently accessed data in memory rather than on disk; and increasingly commonplace accelerators such as graphics processing units (GPUs) offer large-scale parallelism with a lower energy footprint. Thus, in-memory indexing that exploits GPUs could be much cheaper, faster, and greener.

However, effectively incorporating GPUs into computation is a principal research challenge. To idle the powerful multicore system in favour of exclusively using the GPU connected to it, as done currently, is to squander valuable resources. On the other hand, the GPU has a vastly different computational model, so cannot straight-forwardly leverage multicore techniques. The challenges in handling this dichotomy, in fact, will cross-cut many research areas as the heterogeneity in the compute ecosystem becomes ubiquitous in parallel processing.

Building on preliminary results that suggest common data structures processed by architecture-specific algorithms can support heterogeneity, this action will design indexes for the coupled multicore-GPU database systems that will soon be ubiquitous. The indexes will enable more responsive simulations of complex objects such as neurons and vehicle trajectories and support the recent proliferation of mobile-generated data. Moreover, through the action, the researcher will transfer technical parallel programming skills to the host, while the host will transfer expertise about new data types to the researcher. The project results will contribute to Europe's positioning at the forefront of heterogeneous parallel processing.'

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

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