scholarly journals Volume rendering using grid computing for large-scale volume data

2009 ◽  
Author(s):  
Nishihashi Kunihiko ◽  
Higaki Toru ◽  
Okabe Kenji ◽  
Raytchev Bisser ◽  
Tamaki Toru ◽  
...  
Keyword(s):  
Grid Computing ◽  
Volume Rendering ◽  
Large Scale ◽  
Volume Data

GPU-ACCELERATED INTERACTIVE VISUALIZATION OF 3D VOLUMETRIC DATA USING CUDA

2013 ◽  
Vol 13 (02) ◽  
pp. 1340003 ◽  
Author(s):  
PIYUSH KUMAR ◽  
ANUPAM AGRAWAL
Keyword(s):  
Ct Scan ◽  
Volume Rendering ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Three Dimensional ◽  
Processing Unit ◽  
Volume Data ◽  
Scan Data ◽  
Visualization Techniques ◽  
The Cost

Improving the image quality and the rendering speed have always been a challenge to the programmers involved in large scale volume rendering especially in the field of medical image processing. The paper aims to perform volume rendering using the graphics processing unit (GPU), in which, with its massively parallel capability has the potential to revolutionize this field. This work is now better with the help of GPU accelerated system. The final results would allow the doctors to diagnose and analyze the 2D computed tomography (CT) scan data using three dimensional visualization techniques. The system is used in multiple types of datasets, from 10 MB to 350 MB medical volume data. Further, the use of compute unified device architecture (CUDA) framework, a low learning curve technology, for such purpose would greatly reduce the cost involved in CT scan analysis; hence bring it to the common masses. The volume rendering has been done on Nvidia Tesla C1060 (there are 240 CUDA cores, which provides execution of data parallely) card and its performance has also been benchmarked.

Multi-Dimensional Transfer Functions Design

2011 ◽  
pp. 223-240
Author(s):  
Hai Lin
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Large Scale ◽  
Spatial Information ◽  
Transfer Functions ◽  
Design Method ◽  
Data Layout ◽  
Volume Data ◽  
Function Design ◽  
Transfer Function Design

Transfer function design is one of the most important procedures in volume rendering. Transfer function maps, which is a function mapping relationship, data values to display attributes, such as color and opacity. This chapter introduces region growing- based multi-dimensional transfer function design method, which can improve the effect of the multi-dimensional transfer function design, and help the users save the time used in the interactive design and decrease the difficult. In order to use the spatial information as independent variable, we combine spatial information to generate multi-dimensional transfer function. This chapter discusses the GPU-based transfer function lookup method and illumination parameter setting problems. In the last part of this chapter, we discuss the data layout of large scale volume data set and its volume rendering methods.

Reliability and Performance Models for Grid Computing

2010 ◽  
pp. 219-245 ◽  
Author(s):  
Yuan-Shun Dai ◽  
Jack Dongarra
Keyword(s):  
Grid Computing ◽  
Resource Sharing ◽  
Large Scale ◽  
Optimization Problems ◽  
Data Dependence ◽  
Performance Models ◽  
Task Partitioning ◽  
Modeling And Analysis ◽  
Failure Correlation ◽  
And Performance

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. It is hard to analyze and model the Grid reliability because of its largeness, complexity and stiffness. Therefore, this chapter introduces the Grid computing technology, presents different types of failures in grid system, models the grid reliability with star structure and tree structure, and finally studies optimization problems for grid task partitioning and allocation. The chapter then presents models for star-topology considering data dependence and treestructure considering failure correlation. Evaluation tools and algorithms are developed, evolved from Universal generating function and Graph Theory. Then, the failure correlation and data dependence are considered in the model. Numerical examples are illustrated to show the modeling and analysis.

Dynamic Dependent Tasks Assignment for Grid Computing

2011 ◽  
Vol 3 (2) ◽  
pp. 44-58 ◽  
Author(s):  
Meriem Meddeber ◽  
Belabbas Yagoubi
Keyword(s):  
Resource Management ◽  
Grid Computing ◽  
Large Scale ◽  
Task Assignment ◽  
Considerable Effect ◽  
Distributed Applications ◽  
Grid Middleware ◽  
Assignment Strategy ◽  
Transfer Cost ◽  
Dependent Tasks

A computational grid is a widespread computing environment that provides huge computational power for large-scale distributed applications. One of the most important issues in such an environment is resource management. Task assignment as a part of resource management has a considerable effect on the grid middleware performance. In grid computing, task execution time is dependent on the machine to which it is assigned, and task precedence constraints are represented by a directed acyclic graph. This paper proposes a hybrid assignment strategy of dependent tasks in Grids which integrate static and dynamic assignment technologies. Grid computing is considered a set of clusters formed by a set of computing elements and a cluster manager. The main objective is to arrive at a method of task assignment that could achieve minimum response time and reduce the transfer cost, inducing by the tasks transfer respecting the dependency constraints.

scholarly journals METADATA RANKING AND PRUNING FOR FAILURE DETECTION IN GRIDS

2008 ◽  
Vol 18 (03) ◽  
pp. 371-390 ◽  
Author(s):  
DEMETRIOS ZEINALIPOUR-YAZTI ◽  
HARRIS PAPADAKIS ◽  
CHRYSSIS GEORGIOU ◽  
MARIOS D. DIKAIAKOS
Keyword(s):  
Grid Computing ◽  
Large Scale ◽  
Information Sources ◽  
Job Scheduling ◽  
Failure Detection ◽  
Evaluation Study ◽  
Full Potential ◽  
Processing Power ◽  
Available Information ◽  
Ranking Information

The objective of Grid computing is to make processing power as accessible and easy to use as electricity and water. The last decade has seen an unprecedented growth in Grid infrastructures which nowadays enables large-scale deployment of applications in the scientific computation domain. One of the main challenges in realizing the full potential of Grids is making these systems dependable. In this paper we present FailRank, a novel framework for integrating and ranking information sources that characterize failures in a grid system. After the failing sites have been ranked, these can be eliminated from the job scheduling resource pool yielding in that way a more predictable, dependable and adaptive infrastructure. We also present the tools we developed towards evaluating the FailRank framework. In particular, we present the FailBase Repository which is a 38GB corpus of state information that characterizes the EGEE Grid for one month in 2007. Such a corpus paves the way for the community to systematically uncover new, previously unknown patterns and rules between the multitudes of parameters that can contribute to failures in a Grid environment. Additionally, we present an experimental evaluation study of the FailRank system over 30 days which shows that our framework identifies failures in 93% of the cases and can achieve this by only fetching 65% of the available information sources. We believe that our work constitutes another important step towards realizing adaptive Grid computing systems.

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