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CoupledDB

High-Performance Indexing for Emerging GPU-Coupled Databases

Total Cost €

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EC-Contrib. €

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Partnership

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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.

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

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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