Type of Document Master's Thesis Author Bora, Prachi Champalal Author's Email Address email@example.com URN etd-06302003-143933 Title Runtime Algorithm Selection For Grid Environments: A Component Based Framework Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Ribbens, Calvin J. Committee Chair Ramakrishnan, Naren Committee Member Varadarajan, Srinidhi Committee Member Keywords
- Common Component Architecture
- Linear Equations
- Algorithm Selection
- Grid Computing
- Recommender Systems
Date of Defense 2003-06-17 Availability unrestricted AbstractGrid environments are inherently heterogeneous. If the computational power provided by collaborations on the Grid is to be harnessed in the true sense, there is a need for applications that can automatically adapt to changes in the execution environment. The application writer should not be burdened with the job of choosing the right algorithm and implementation every time the resources on which the application runs are changed.
A lot of research has been done in adapting applications to changing conditions. The existing systems do not address the issue of providing a unified interface to permit algorithm selection at runtime. The goal of this research is to design and develop a unified interface to applications in order to permit seamless access to different algorithms providing similar functionalities. Long running, computationally intensive scientific applications can produce huge amounts of performance data. Often, this data is discarded once the application’s execution is complete. This data can be utilized in extracting information about algorithms and their performance. This information can be used to choose algorithms intelligently.
The research described in this thesis aims at designing and developing a component based unified interface for runtime algorithm selection in grid environments. This unified interface is necessary so that the application code does not change if a new algorithm is used to solve the problem. The overhead associated with making the algorithm choice transparent to the application is evaluated. We use a data mining approach to algorithm selection and evaluate its potential effectiveness for scientific applications.
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