Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular

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.

This paper addresses the problem of universal synchronization primitives that can support scalable thread synchronization for large-scale many-core architectures. The universal synchronization primitives that have been deployed widely in conventional architectures, are the compare-and-swap (CAS) and load-linked/store-conditional (LL/SC) primitives. However, such synchronization primitives are expected to reach their scalability limits in the evolution to many-core architectures with thousands of cores. We introduce a non-blocking full/empty bit primitive, or NB-FEB for short, as a promising synchronization primitive for parallel programming on may-core architectures. We show that the NB-FEB primitive is universal, scalable, feasible and convenient to use. NB-FEB, together with registers, can solve the consensus problem for an arbitrary number of processes