Opendata, web and dolomites


High-Performance Indexing for Emerging GPU-Coupled Databases

Total Cost €


EC-Contrib. €






 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.

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

Project "CoupledDB" data sheet

The following table provides information about the project.


Organization address
postcode: 7491

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
 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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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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