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Data-Driven Abstraction, Vivian Mankau Ho
Data-Driven Abstraction, Vivian Mankau Ho
LSU Master's Theses
Given a program analysis problem that consists of a program and a property of interest, we use a data-driven approach to automatically construct a sequence of abstractions that approach an ideal abstraction suitable for solving that problem. This process begins with an infinite concrete domain that maps to a finite abstract domain defined by statistical procedures resulting in a clustering mixture model. Given a set of properties expressed as formulas in a restricted and bounded variant of CTL, we can test the success of the abstraction with respect to a predefined performance level. In addition, we can perform iterative abstraction-refinement …
Compiler And Runtime Optimization Techniques For Implementation Scalable Parallel Applications, Zahra Khatami
Compiler And Runtime Optimization Techniques For Implementation Scalable Parallel Applications, Zahra Khatami
LSU Doctoral Dissertations
The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided by a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and desired application scalability. These compiler techniques should consider both the static information gathered at compile time and dynamic analysis captured at runtime about the system to generate a safe parallel application. On the other hand, runtime information is often speculative. Solely relying on it doesn't guarantee …