- By:
- McDonough, Ethan; Denny, Joel E; Doerfert, Johannes
- Page Number:
- 99-113
- Book Title:
- OpenMP: Balancing Productivity and Performance Portability (IWOMP 2025)
- Publication Date:
- September 1, 2026
- Conference Name:
- 21st International Workshop on OpenMP (IWOMP 2025)
- Conference Location:
- Charlotte, North Carolina, United States of America
- Conference Sponsor:
- Springer
- View DOI Listing:
- https://doi.org/10.1007/978-3-032-06343-4_7
Abstract
GPU accelerators are ubiquitous, but their ecosystem is far less evolved than the host one. Compiler heuristics are often tuned for CPUs and reused for GPU. Similarly, tooling and more evolved optimization techniques are historically not available on GPU targets. In this work, we address one of these shortcomings and enable profile generation and profile-guided optimizations (PGO) for GPU targets. While this is only a single step towards a CPU equivalent ecosystem for offload devices, it shows how old misconceptions on the limitations of GPUs are often not warranted anymore. Through our implementation in LLVM/Offload, we enable device-side PGO for full scientific applications and open up tooling opportunities, including code coverage analysis and compiler-built-in roofline analysis. Our evaluation highlights the performance implications of profile generation, the insights gained from these profiles, and the (missed) opportunities in utilizing the information for GPU compilation.
