Towards heterogeneity-aware automatic optimization of time-critical systems via graph machine learning - Canizales Turcios, Ronaldo Armando, author; McClurg, Jedidiah, advisor; Rajopadhye, Sanjay, committee member; Pasricha, Sudeep, committee member (Colorado State University. Libraries, 2024)
Modern 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.
Keywords: graphs; machine learning; programming languages; high-performance computing; artificial intelligence; parallel programming