DO INPUTS MATTER? USING DATA-DEPENDENCE PROFILING TO EVALUATE THREAD LEVEL SPECULATION IN THE BLUEGENE/Q

and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author’s prior written permission. To Late Mohan Lal Bandyopadhyay Dadu, wish you were here. In the era of many-core architectures, it is necessary to fully exploit the maximum available par-allelism in computer programs. Thread Level Speculation (TLS) is a hardware/software technique that guarantees correct speculative parallel execution of the program even in the presence of may dependences. This thesis investigates the variability of dependence behaviour of loops across pro-gram inputs with the help of data-dependence profiling. This thesis also presents SpecEval, a new automatic speculative parallelization framework that uses single-input data-dependence profiles to evaluate the TLS hardware support in the IBM’s BlueGene/Q (BG/Q) supercomputer. A perfor-mance evaluation of TLS applied along with the traditional automatic parallelization techniques indicates that various factors such as: the number of loops speculatively parallelized and their cov-erage, mispeculation overhead due to dependences introduced from function calls inside loop body, increase in L1 cache misses due to long running (LR) mode in BG/Q and dynamic instruction path length increase impact the performance of TLS