Towards Heterogeneity-Aware Automatic Optimization of Time-Critical Systems via Graph Machine Learning
সংরক্ষণ করুন:
| প্রকাশিত: | ProQuest Dissertations and Theses (2024) |
|---|---|
| প্রধান লেখক: | |
| প্রকাশিত: |
ProQuest Dissertations & Theses
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text - PDF |
| ট্যাগগুলো: |
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| 045 | 2 | |b d20240101 |b d20241231 | |
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| 100 | 1 | |a Canizales Turcios, Ronaldo Armando | |
| 245 | 1 | |a Towards Heterogeneity-Aware Automatic Optimization of Time-Critical Systems via Graph Machine Learning | |
| 260 | |b ProQuest Dissertations & Theses |c 2024 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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. | |
| 653 | |a Computer science | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Information science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2024) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3148346991/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3148346991/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |