Date of Award


Publication Type


Degree Name



Computer Science

First Advisor

Robert Kent

Second Advisor

Akshaikumar Aggarwal


Applied sciences



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


In order to understand the behavior of OpenMP programs, special tools and adaptive techniques are needed for performance analysis. However, these tools provide low level profile information at the assembly and functions boundaries via instrumentation at the binary or code level, which are very hard to interpret. Hence, this thesis proposes a new model for OpenMP enabled compilers that assesses the performance differences in well defined formulations by dividing OpenMP program conditions into four distinct states which account for all the possible cases that an OpenMP program can take. An improved version of the standard performance metrics is proposed: speedup, overhead and efficiency based on the model categorization that is state's aware. Moreover, an algorithmic approach to find patterns between OpenMP compilers is proposed which is verified along with the model formulations experimentally. Finally, the thesis reveals the mathematical model behind the optimum performance for any OpenMP program.