RAPTOR: Practical Numerical Profiling of Scientific Applications
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915505651056640 |
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| author | Hoerold, Faveo Ivanov, Ivan R. Dhruv, Akash Moses, William S. Dubey, Anshu Wahib, Mohamed Domke, Jens |
| author_facet | Hoerold, Faveo Ivanov, Ivan R. Dhruv, Akash Moses, William S. Dubey, Anshu Wahib, Mohamed Domke, Jens |
| contents | The proliferation of low-precision units in modern high-performance architectures increasingly burdens domain scientists. Historically, the choice in HPC was easy: can we get away with 32 bit floating-point operations and lower bandwidth requirements, or is FP64 necessary? Driven by Artificial Intelligence, vendors introduce novel low-precision units for vector and tensor operations, and FP64 capabilities stagnate or are reduced. This forces scientists to re-evaluate their codes, but a trivial search-and-replace approach to go from FP64 to FP16 will not suffice.
We introduce RAPTOR: a numerical profiling tool to guide scientists in their search for code regions where precision lowering is feasible. Using LLVM, we transparently replace high-precision computations using low-precision units, or emulate a user-defined precision. RAPTOR is a novel, feature-rich approach -- with focus on ease of use -- to change, profile, and reason about numerical requirements and instabilities, which we demonstrate with four real-world multi-physics Flash-X applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAPTOR: Practical Numerical Profiling of Scientific Applications Hoerold, Faveo Ivanov, Ivan R. Dhruv, Akash Moses, William S. Dubey, Anshu Wahib, Mohamed Domke, Jens Distributed, Parallel, and Cluster Computing Numerical Analysis The proliferation of low-precision units in modern high-performance architectures increasingly burdens domain scientists. Historically, the choice in HPC was easy: can we get away with 32 bit floating-point operations and lower bandwidth requirements, or is FP64 necessary? Driven by Artificial Intelligence, vendors introduce novel low-precision units for vector and tensor operations, and FP64 capabilities stagnate or are reduced. This forces scientists to re-evaluate their codes, but a trivial search-and-replace approach to go from FP64 to FP16 will not suffice. We introduce RAPTOR: a numerical profiling tool to guide scientists in their search for code regions where precision lowering is feasible. Using LLVM, we transparently replace high-precision computations using low-precision units, or emulate a user-defined precision. RAPTOR is a novel, feature-rich approach -- with focus on ease of use -- to change, profile, and reason about numerical requirements and instabilities, which we demonstrate with four real-world multi-physics Flash-X applications. |
| title | RAPTOR: Practical Numerical Profiling of Scientific Applications |
| topic | Distributed, Parallel, and Cluster Computing Numerical Analysis |
| url | https://arxiv.org/abs/2507.04647 |