Report
Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences
| العنوان: | Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences |
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| المؤلفون: | Li, Shuyue Stella, Peeler, Hannah, Sloss, Andrew N., Reid, Kenneth N., Banzhaf, Wolfgang |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence |
| الوصف: | Genetic improvement is a search technique that aims to improve a given acceptable solution to a problem. In this paper, we present the novel use of genetic improvement to find problem-specific optimized LLVM pass sequences. We develop a pass-level patch representation in the linear genetic programming framework, Shackleton, to evolve the modifications to be applied to the default optimization pass sequences. Our GI-evolved solution has a mean of 3.7% runtime improvement compared to the -O3 optimization level in the default code generation options which optimizes on runtime. The proposed GI method provides an automatic way to find a problem-specific optimization sequence that improves upon a general solution without any expert domain knowledge. In this paper, we discuss the advantages and limitations of the GI feature in the Shackleton Framework and present our results. Comment: 3 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3520304.3534000 |
| URL الوصول: | http://arxiv.org/abs/2204.13261 |
| رقم الانضمام: | edsarx.2204.13261 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3520304.3534000 |
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