Canizales Turcios, Ronaldo Armando, authorMcClurg, Jedidiah, advisorRajopadhye, Sanjay, committee memberPasricha, Sudeep, committee member2024-12-232024-12-232024https://hdl.handle.net/10217/239795Modern computing's hardware architecture is increasingly heterogeneous, making optimization challenging; particularly on time-critical systems where correct results are as important as low execution time. First, we explore a study case about the manual optimization of an earthquake engineering-related application, where we parallelized accelerographic records processing. Second, we present egg-no-graph, our novel code-to-graph representation based on equality saturation, which outperforms state-of-the-art methods at estimating execution time. Third, we show how our 150M+ instances heterogeneity-aware dataset was built. Lastly, we redesign a graph-level embedding algorithm, making it converge orders of magnitude faster while maintaining similar accuracy than state-of-the-art on our downstream task, thus being feasible for use on time-critical systems.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.graphsmachine learningprogramming languageshigh-performance computingartificial intelligenceparallel programmingText