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CoupledDB

High-Performance Indexing for Emerging GPU-Coupled Databases

Total Cost €

0

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.

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

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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