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Penagos, L. G. (2018). Deep: Dependency Elimination Using Early Predictions. Retrieved from http://purl.flvc.org/fsu/fd/2018_Su_Penagos_fsu_0071N_14784
Conditional branches have traditionally been a performance bottleneck for most processors. The high frequency of branches in code coupled with expensive pipeline flushes on mispredictions make branches expensive instructions worth optimizing. Conditional branches have historically inhibited compilers from applying optimizations across basic block boundaries due to the forks in control flow that they introduce. This thesis describes a systematic way of generating paths (traces) of branch-free code at compile time by decomposing branching and verification operations to eliminate the dependence of a branch on its preceding compare instruction. This explicit decomposition allows us to move comparison instructions past branches and to merge pre and post branch code. These paths generated at compile time can potentially provide additional opportunities for conventional optimizations such as common subexpression elimination, dead assignment elimination and instruction selection. Moreover, this thesis describes a way of coalescing multiple branch instructions within innermost loops to produce longer basic blocks to provide additional optimization opportunities.
A Thesis submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Master of Science.
Bibliography Note
Includes bibliographical references.
Advisory Committee
David Whalley, Professor Directing Thesis; Xin Yuan, Committee Member; Weikuan Yu, Committee Member.
Publisher
Florida State University
Identifier
2018_Su_Penagos_fsu_0071N_14784
Penagos, L. G. (2018). Deep: Dependency Elimination Using Early Predictions. Retrieved from http://purl.flvc.org/fsu/fd/2018_Su_Penagos_fsu_0071N_14784