Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915659144757248 |
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| author | Peng, Xuanyu Kennedy, Dominic Fan, Yuyou Greenman, Ben Regehr, John D'Antoni, Loris |
| author_facet | Peng, Xuanyu Kennedy, Dominic Fan, Yuyou Greenman, Ben Regehr, John D'Antoni, Loris |
| contents | Static analyses play a fundamental role during compilation: they discover facts that are true in all executions of the code being compiled, and then these facts are used to justify optimizations and diagnostics. Each static analysis is based on a collection of abstract transformers that provide abstract semantics for the concrete instructions that make up a program. It can be challenging to implement abstract transformers that are sound, precise, and efficient, and in fact both LLVM and GCC have suffered from miscompilations caused by unsound abstract transformers. Moreover, even after more than 20 years of development, LLVM lacks abstract transformers for hundreds of instructions in its intermediate representation (IR). We developed NiceToMeetYou, a program synthesis framework for abstract transformers that are aimed at the kinds of non-relational integer abstract domains that are heavily used by today's production compilers. It exploits a simple but novel technique for breaking the synthesis problem into parts: each of our transformers is the meet of a collection of simpler, sound transformers that are synthesized such that each new piece fills a gap in the precision of the final transformer. Our design point is bulk automation: no sketches are required. Transformers are verified by lowering to a previously created SMT dialect of MLIR. Each of our synthesized transformers is provably sound and some (17 percent) are more precise than those provided by LLVM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers Peng, Xuanyu Kennedy, Dominic Fan, Yuyou Greenman, Ben Regehr, John D'Antoni, Loris Programming Languages Static analyses play a fundamental role during compilation: they discover facts that are true in all executions of the code being compiled, and then these facts are used to justify optimizations and diagnostics. Each static analysis is based on a collection of abstract transformers that provide abstract semantics for the concrete instructions that make up a program. It can be challenging to implement abstract transformers that are sound, precise, and efficient, and in fact both LLVM and GCC have suffered from miscompilations caused by unsound abstract transformers. Moreover, even after more than 20 years of development, LLVM lacks abstract transformers for hundreds of instructions in its intermediate representation (IR). We developed NiceToMeetYou, a program synthesis framework for abstract transformers that are aimed at the kinds of non-relational integer abstract domains that are heavily used by today's production compilers. It exploits a simple but novel technique for breaking the synthesis problem into parts: each of our transformers is the meet of a collection of simpler, sound transformers that are synthesized such that each new piece fills a gap in the precision of the final transformer. Our design point is bulk automation: no sketches are required. Transformers are verified by lowering to a previously created SMT dialect of MLIR. Each of our synthesized transformers is provably sound and some (17 percent) are more precise than those provided by LLVM. |
| title | Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers |
| topic | Programming Languages |
| url | https://arxiv.org/abs/2512.06442 |