GAMVOLVIS

GPU-Assisted Massive Volume Data Visualisation

 Coordinatore UNIVERSITY OF BEDFORDSHIRE 

 Organization address address: PARK SQUARE
city: LUTON
postcode: LU1 3JU

contact info
Titolo: Prof.
Nome: Gordon
Cognome: Clapworthy
Email: send email
Telefono: -745034
Fax: -490750

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 0 €
 EC contributo 173˙185 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-IIF-2008
 Funding Scheme MC-IIF
 Anno di inizio 2009
 Periodo (anno-mese-giorno) 2009-09-01   -   2011-08-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    UNIVERSITY OF BEDFORDSHIRE

 Organization address address: PARK SQUARE
city: LUTON
postcode: LU1 3JU

contact info
Titolo: Prof.
Nome: Gordon
Cognome: Clapworthy
Email: send email
Telefono: -745034
Fax: -490750

UK (LUTON) coordinator 173˙185.81

Mappa


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techniques    visualisation    efficient    cache    memory    single    volume    resolution    gpu    power    performance    data    datasets    quality    pc    gpus    rendering   

 Obiettivo del progetto (Objective)

'High-resolution medical volume data (containing billions of voxels) is used in many clinical applications. However, interactive visualisation of gigabyte-sized volumes on a desktop PC is challenging, due to the heavy computation and the memory consumption. In recent years, the graphics processing unit (GPU) has evolved at an increasing pace, and tremendous improvements have been achieved in its capabilities. GPU performance now substantially exceeds that of CPUs in both the raw computational power provided and its speed of development. Thus, the GPU is now the ideal platform for efficient visualisation for large volume data. However, current methods for rendering large volume datasets are limited, either in performance, accuracy, flexibility or scalability. This project addresses this by presenting techniques to perform volume visualisation of large datasets on off-the-shelf hardware by harnessing the power of GPUs to provide high performance and produce high-quality images. We will achieve this by implementing a multi-resolution framework with two parts: data management on the CPU and real-time rendering on the GPU. We propose a new rendering pipeline by directly treating each volume brick as a single GPU rendering primitive, and efficient cache management to avoid cache thrashing of GPU memory. Forward mapping will allow arbitrariiy many primitives to be rendered on the GPU in a stream-like manner, making our system fully scalable to an arbitrarily large dataset. Further acceleration will be achieved by using a coarsely-fitted proxy geometry, and advanced illumination techniques will be introduced for better quality visualisation at high frame rates. We will also extend our applications from a single GPU to multiple GPUs. The resulting system will allow domain scientists to effectively visualise super-large-scale volume data on moderate PC platforms without being heavily restricted by the data size, which is a key advance from the current state of the art.'

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