LLVM
LLVM is a modular compiler infrastructure and toolchain, not a parser generator. It provides the building blocks for code representation, optimization, and generation, including support for ASTs, IRs, and MLIR.
What It Is
- A compiler framework that allows you to build custom compilers, interpreters, and code transformation tools.
- Provides intermediate representations (IR), optimization passes, and code generation backends.
- Supports C++ APIs for AST manipulation, IR generation, and integration with other tooling.
When to Use
- High-performance or custom language tooling, where you need control over code analysis, transformations, or JIT compilation.
- C++-heavy environments where the tooling integrates directly with existing compiler workflows.
- Projects needing MLIR (Multi-Level Intermediate Representation) for machine learning or specialized DSL compilation.
Advantages
- Extremely flexible and extensible, can represent complex IRs and ASTs.
- Wide adoption in systems programming, ML compilers, and high-performance DSLs.
- Can be used as a backend for custom languages, or for transforming/executing existing code.
Considerations
- Not a parser: you need a front-end (like Clang, ANTLR, PEGTL, Bison/Flex) to generate AST/IR.
- Complexity: High, requires deep knowledge of compiler design and LLVM APIs.
- Use cases: Custom compilers, DSL tooling, high-performance numerical computing, MLIR-based frameworks.
Reference Projects
- Codon: a high-performance Python compiler using LLVM IR
- Hailide: DSL compiler leveraging LLVM for numerical computations
- Triton: GPU-optimized tensor compiler using LLVM backend
References
Summary
- Integration complexity: High
- Performance: Excellent for compiled code and JIT pipelines
- Typical scenarios: Custom compiler development, MLIR pipelines, DSL execution, high-performance C++ tooling
LLVM is a powerful foundation, but it requires a parser front-end to generate AST/IR. It is primarily used in C++ tooling, MLIR projects, and compiler development.