Uploaded June 2026 | Updated September 2026, 3 weeks ago
2026 EuroLLVM Developers' Meeting
llvm.org/devmtg/2026-04
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Title: Lighthouse: infrastructure for end-to-end MLIR-compilers and testing
Speaker: Renato Golin
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Slides: llvm.org/devmtg/2026-04/slides/technical_talk/technical_talk_golin.pdf
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Last year, a new project was added to the LLVM family: Lighthouse. Its main purpose is to guide the development and testing of MLIR based compilers. Like the LLVM test-suite, it should be a common ground for validating upstream assumptions about code, IR, dialects. At the same time, it enables building specific compilers in minutes, using the evolving Python API and MLIR's Python bindings. In this talk, we'll show the project main structure, including its components and how to use them to build a simple compiler. We'll then show the infrastructure that uses those components to validate assumptions in MLIR (canonical forms, invariants, applicability of transforms and passes, correctness tests, etc), and how you can create your own on top of that. Finally, we'll provide a number of pipeline examples, going from generic PyTorch models to performant execution on various targets.
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Videos Edited by Bash Films: BashFilms.com
2026 EuroLLVM Developers' Meeting
llvm.org/devmtg/2026-04
------
Title: Lighthouse: infrastructure for end-to-end MLIR-compilers and testing
Speaker: Renato Golin
------
Slides: llvm.org/devmtg/2026-04/slides/technical_talk/technical_talk_golin.pdf
-----
Last year, a new project was added to the LLVM family: Lighthouse. Its main purpose is to guide the development and testing of MLIR based compilers. Like the LLVM test-suite, it should be a common ground for validating upstream assumptions about code, IR, dialects. At the same time, it enables building specific compilers in minutes, using the evolving Python API and MLIR's Python bindings. In this talk, we'll show the project main structure, including its components and how to use them to build a simple compiler. We'll then show the infrastructure that uses those components to validate assumptions in MLIR (canonical forms, invariants, applicability of transforms and passes, correctness tests, etc), and how you can create your own on top of that. Finally, we'll provide a number of pipeline examples, going from generic PyTorch models to performant execution on various targets.
-----
Videos Edited by Bash Films: BashFilms.com
