Doctor of Philosophy / Department of Computing and Information Sciences / Gurdip Singh / Many frameworks have been proposed which provide distributed algorithms encapsulated
as middleware services to simplify application design. The developers of such algorithms are faced with two opposing forces. One is to design generic algorithms that are reusable in a large number of applications. Efficiency considerations, on the other hand, force the algorithms to be customized to specific operational contexts. This problem is often attacked by simply re-implementing all or large portions of an algorithm.
We propose InDiGO, an infrastructure which allows design of generic but customizable
algorithms and provides tools to customize such algorithms for specific applications. InDiGO provides the following capabilities: (a) Tools to generate intermediate representations of an application which can be leveraged for analysis, (b) Mechanisms to allow developers to design customizable algorithms by exposing design knowledge in terms of configurable options, and (c) An optimization engine to analyze an application to derive the information
necessary to optimize the algorithms. Specifically, we optimize algorithms by removing communication which is redundant in the context of a specific application. We perform three types of optimizations: static optimization, dynamic optimization and physical topology-based optimization. We present experimental results to demonstrate the advantages of our infrastructure.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/1007 |
| Date | January 1900 |
| Creators | Kolesnikov, Valeriy |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0019 seconds