From Profiling to Optimization: Unveiling the Profile Guided Optimization
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911070497538048 |
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| author | Liu, Bingxin Huang, Yinghui Gao, Jianhua Shi, Jianjun Liu, Yongpeng Sun, Yipin Ji, Weixing |
| author_facet | Liu, Bingxin Huang, Yinghui Gao, Jianhua Shi, Jianjun Liu, Yongpeng Sun, Yipin Ji, Weixing |
| contents | Profile Guided Optimization (PGO) uses runtime profiling to direct compiler optimization decisions, effectively combining static analysis with actual execution behavior to enhance performance. Runtime profiles, collected through instrumentation or hardware- and software-assisted sampling, provide detailed insights into control flow, branch predictions, and memory access patterns. This survey systematically categorizes PGO research by profiling method (instrumentation vs. sampling), optimizations (compile time and link/post-link time), compiler integration (GCC, LLVM), and target architectures. Key algorithms and frameworks are shown in terms of design principles. Performance evaluation on representative examples demonstrates PGO's speedups, overheads, and integration maturity. Finally, we identify open challenges, such as reducing sampling overhead, dynamic input workloads, and supporting cross-architecture portability, and propose future research directions to low-overhead profiling and advanced compilers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Profiling to Optimization: Unveiling the Profile Guided Optimization Liu, Bingxin Huang, Yinghui Gao, Jianhua Shi, Jianjun Liu, Yongpeng Sun, Yipin Ji, Weixing Performance Profile Guided Optimization (PGO) uses runtime profiling to direct compiler optimization decisions, effectively combining static analysis with actual execution behavior to enhance performance. Runtime profiles, collected through instrumentation or hardware- and software-assisted sampling, provide detailed insights into control flow, branch predictions, and memory access patterns. This survey systematically categorizes PGO research by profiling method (instrumentation vs. sampling), optimizations (compile time and link/post-link time), compiler integration (GCC, LLVM), and target architectures. Key algorithms and frameworks are shown in terms of design principles. Performance evaluation on representative examples demonstrates PGO's speedups, overheads, and integration maturity. Finally, we identify open challenges, such as reducing sampling overhead, dynamic input workloads, and supporting cross-architecture portability, and propose future research directions to low-overhead profiling and advanced compilers. |
| title | From Profiling to Optimization: Unveiling the Profile Guided Optimization |
| topic | Performance |
| url | https://arxiv.org/abs/2507.16649 |