Understanding and Improving the Performance Consistency of Distributed Computing Systems

With the increasing adoption of distributed systems in both academia and industry, and with the increasing computational and storage requirements of distributed applications, users inevitably demand more from these systems. Moreover, users also depend on these systems for latency and throughput sensitive applications, such as interactive...

Efficient Iterative Solution of Large Linear Systems on Heterogeneous Computing Systems

This dissertation deals mainly with the design, implementation, and analysis of efficient iterative solution methods for large sparse linear systems on distributed and heterogeneous computing systems as found in Grid computing. First, a case study is performed on iteratively solving large symmetric linear systems on both a multi–cluster and a...

Quantifying Pathology in Diffusion Weighted MRI

In this thesis algorithms are proposed for quantification of pathology in Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) data. Functional evidence for brain diseases can be explained by specific structural loss in the white matter of the brain. That is, certain biomarkers may exist where the disease inhibits improper functioning. Axonal...

Application-Oriented Scheduling in Multicluster Grids

Grid computing appeared in the mid 1990s with the vision of sharing geographically dispersed large computational resources for executing computation-intensive scientific applications. Today, we can name numerous grid projects that run successfully to solve challenging scientific problems such as the grid project of European Organization for...

High-performance Processing in Networked and Grid Environments

In this dissertation, we present several techniques to achieve the high-performance processing in networked and grid environments. Many applications need a high-performance processing system to execute efficiently. High-performance processing mainly stems from parallelism. The parallel nature of grid computing is a very attractive solution to...

Exploiting the flexibility of IDR(s) for grid computing

The IDR(s) method that is proposed in [26] is an efficient limited memory method for solving large nonsymmetric systems of linear equations. In [11] an IDR(s) variant is described that has a single synchronisation point per iteration step, which makes this variant well-suited for parallel and grid computing. In this paper, we combine this IDR(s)...

A framework for the study of grid inter-operation mechanisms

The study of the history of computing infrastructures reveals an integration trend. For example, the explosive growth of the Internet in the 1990s was the result of an integration process started in the 1960s with the emerging networks of computers. By using the Internet, millions of users were capable of accessing information anytime and...

An Advanced Collaborative Infrastructure for the Real-Time Computational Steering of Large CFD Simulations

using graphical means, in certain cases it is even possible to alter parameters of the computation whilst it is running, depending on what the scientist sees in the current visual output. This ability to monitor and change parameters of the computational process at any time and from anywhere is called computational steering. By combining this...

Fast solution of nonsymmetric linear systems on Grid computers using parallel variants of IDR(s)

IDR(s) is a family of fast algorithms for iteratively solving large nonsymmetric linear systems [14]. With cluster computing and in particular with Grid computing, the inner product is a bottleneck operation. In this paper, three techniques are combined in order to alleviate this bottleneck. Firstly, the efficient and stable IDR(s) algorithm...