Type of Document Dissertation Author Kang, Pilsung Author's Email Address email@example.com URN etd-08242010-013812 Title Modular Implementation of Program Adaptation with Existing Scientific Codes Degree PhD Department Computer Science Advisory Committee
Advisor Name Title Varadarajan, Srinidhi Committee Chair Ramakrishnan, Naren Committee Co-Chair Ribbens, Calvin J. Committee Member Tafti, Danesh K. Committee Member Tilevich, Eli Committee Member Keywords
- scientic computing
- parallel programming
- program adaptation
Date of Defense 2010-08-10 Availability unrestricted AbstractOften times, scientific software needs to be adapted for different execution environments, problem sets, and available resources to ensure its efficiency and reliability. Directly modifying source code to implement adaptation is time-consuming, error-prone, and difficult to manage for today's complex software. This thesis studies modular approaches to implementing program adaptation with existing scientific codes, whereby application-specific adaptation strategies can be implemented in separate code which is then transparently combined with a given program. By using the approaches developed in this thesis, scientific programmers can focus on the design and implementation of adaptation schemes, manage an adaptation code separately from the main program components, and compose an adaptive application whose original capabilities are enhanced in diverse aspects such as application performance and stability. The primary objective of the modular approaches in this study is to provide a language-independent development method of adapting existing scientific software, so that applications written in different languages can be supported when implementing adaptation schemes. In particular, the emphasis is on Fortran, which has been a mainstream language for programming scientific applications.
Three research questions are formulated in this thesis, each of which aims to: design and implement high-level abstractions for expressing adaptation strategies, develop a dynamic tuning approach for parallel programs, and support flexible runtime adaptation schemes, respectively. The applicability of the proposed approaches is demonstrated through example applications to real-world scientific software.
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