A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY SHENGYUE WANG IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Pen-Chung Yew, Adviser

Thread-Level Speculation (TLS) allows potentially dependent threads to execute in parallel, by postponing the detection and verification of inter-thread data dependences until runtime. Although TLS greatly simplifies automatic parallelization, only moderate performance improvements have been achieved for general-purpose applications. Thus adequate compiler techniques must be developed to fully utilize the underlying TLS support. In this thesis, we address the key compiler issues involved in the speculative parallelization and propose several optimization techniques to improve the performance of general-purpose applications under TLS. One important task of a TLS compiler is to identify speculative threads with good performance potentials. We propose a loop selection algorithm that determines which loops to parallelize to maximize the program performance. For applications with limited loop-level parallelism, a partitioning algorithm is proposed to extract threads from sequential codes. To further enhance the performance of parallel threads, we propose three compiler optimizations: instruction scheduling, reduction transformation and iteration