Towards Heterogeneity-Aware Automatic Optimization of Time-Critical Systems via Graph Machine Learning
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| Publicado no: | ProQuest Dissertations and Theses (2024) |
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| Acesso em linha: | Citation/Abstract Full Text - PDF |
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| Resumo: | 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. |
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| ISBN: | 9798346870159 |
| Fonte: | ProQuest Dissertations & Theses Global |