Master of Science (MS)
First Advisor's Name
Gang Quan, Ph.D
First Advisor's Committee Title
Second Advisor's Name
Sakhrat Khizroev, Ph.D.
Second Advisor's Committee Title
Professor of Electrical Engineering
Third Advisor's Name
Hai Deng, Ph.D.
Third Advisor's Committee Title
Date of Defense
Execution time estimation plays an important role in computer system design. It is particularly critical in real-time system design, where to meet a deadline can be as important as to ensure the logical correctness of a program. To accurately estimate the execution time of a program can be extremely challenging, since the execution time of a program varies with inputs, the underlying computer architectures, and run-time dynamics, among other factors. The problem becomes even more challenging as computing systems moving from single core to multi-core platforms, with more hardware resources shared by multiple processing cores.
The goal of this research is to investigate the relationship between the execution time of a program and the underlying architecture features (e.g. cache size, associativity, memory latency), as well as its run-time characteristics (e.g. cache miss ratios), and based on which, to estimate its execution time on a multi-core platform based on a regression approach. We developed our test platform based on GEM5, an open-source multi-core cycle-accurate simulation tool set. Our experimental results show clearly the strong relationship of the program execution time to architecture features and run-time characteristics. Moreover, we developed different execution time estimation algorithms using the regression approach for different programs with different software characteristics to improve the estimation accuracy.
Alshamlan, Mohammad, "A Regression Approach to Execution Time Estimation for Programs Running on Multicore Systems" (2014). FIU Electronic Theses and Dissertations. 1240.
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