Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

Parallel Implementation and Performance Analysis of a 3D Oil Reservoir Data Visualization Tool on the Cell Broadband Engine and CUDA GPU

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Siba, F.N. ; Saudi Aramco R&D Center, Dhahran, Saudi Arabia ; Mohammad, S. ; Kidwai, H.K. ; Qamar, B.
more authors

Usefulness of graphically visualizing and manipulating large data sets in oil and gas exploration and production is as important as ever. This paper describes the development and parallelization of a multi-phase 3D oil-water reservoir visualization tool on the IBM Cell computer and CUDA enabled GPU. An independent Oil reservoir simulator described in [1] was used to generate the pressure and oil / water saturation values over a certain period of time. The oil reservoir visualization tool displays data grids in a 3D environment and allows the user to interact with it. Due to large speed requirements, our aim is to parallelize the computations required to interact with and visualize the grid, mainly transformation [2], zooming, camera movement [3] and compute intensive lighting model [4][5]. This tool also allows the user to playback the simulation results over a time duration and fetches data values upon mouse click at a particular grid point on a particular day. The development environments are nVIDIA CUDA and IBM Cell SDK 3.0 along with QT and OpenGL libraries. Various experiments were run on an ×86 computer with nVIDIA Quadro FX 5800 GPU, and on an IBM Cell BE computer with 1 QS20 Cell blade containing two 9-core Cell processor packages. Our results indicate that the nVIDIA GPU provides on average, speed up of 67× over serial implementation and IBM Cell BE with 16 SPE SIMD implementation 32× over the serial implementation.

Published in:

High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on

Date of Conference:

25-27 June 2012