Historically, compilers have operated by applying a fixed set of optimizations in a predetermined order. We call such an ordered list of optimizations a compilation sequence. This paper describes a prototype system that uses biased random search to discover a program-specific compilation sequence that minimizes an explicit, external objective function. The result is a compiler framework that adapts its behavior to the application being compiled, to the pool of available transformations, to the objective function, and to the target machine.

This dissertation is dedicated to my mom, Maria, whose love and support made it possible. ACKNOWLEDGMENTS Eliot Moss has been a great thesis advisor. He has helped me to become a better re-searcher by shaping my critical thinking as well as by improving my expressive skills. I would like to thank the members of my thesis committee, Andy Barto, Emery Berger, and Wayne Burleson for their feedback and advice that helped to improve the overall quality of this dissertation. I gratefully acknowledge the friendships and interactions from all members of the Ar-chitecture and Language Implementation group (ALI). Beginning with my first lab meeting talk, I have received helpful feedback on the best way to present myself and my work. The ongoing discussions in the lab helped to stimulate my research. Thanks especially to M. Tyler Maxwell for some of the amazing diagrams in this dissertation. Robbie Moll was helpful at stimulating my research interests in the applications of machine learning and for believing in me as an instructor. I especially would like to acknowledge Emmanuel Agu, who has been a good friend and with whom I have had many rewarding discussions on research and life. Finally, I am extremely grateful for the love and support of my entire family. Overall, I am extremely lucky to be part of such a close and wonderful family. I would like to express my sincerest gratitude to my mother, Maria. As a young child I remember my mother always telling me that I could accomplish anything that I set my mind to. She was right as always. Her confidence in me gave me the strength both to overcome any difficulties and to maintain high goals. This work was supported by National Physical Science Consortium and Lawrence Liv-ermore National Laboratory.

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Modern architectures allow multiple instructions to be issued at once and have other complex features. To account for this, compilers perform instruction scheduling after generating the output code. The instruction scheduling problem is to find an optimal schedule given the limitations and capabilities of the architecture. While this can be done optimally, a greedy algorithm known as list scheduling is used in practice in most production compilers. List scheduling is generally regarded as being near-optimal in practice, provided a good choice of heuristic is used. However, previous work comparing a list scheduler against an optimal scheduler either makes the assumption that an idealized architectural model is being used or uses too few test cases to strongly prove or