Introduction
Build a complete compiler for a custom C-like language including lexer, parser, AST, semantic analysis, and LLVM IR code generation. This comprehensive guide covers everything from design through implementation, testing, and deployment.
Compiler Design (Mini Language)
Build a complete compiler for a custom C-like language including lexer, parser, AST, semantic analysis, and LLVM IR code generation.
DifficultyAdvanced
Duration6–8 weeks
Components10 items
Steps6 steps
Theory & Background
Define the language BNF (Backus-Naur Form) grammar. Example mini-language: statements (if/else, while, for, return), expressions (arithmetic, comparison, logical), data types (int, float, bool, string, arrays), functions (definition, calls), variable declarations. Write the complete grammar before coding — it drives all subsequent implementation. Verify grammar is unambiguous (no shift/reduce conflicts in YACC/PLY).
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Components & Requirements
10 components required for this project.
| # | Component | Purpose | Qty |
|---|---|---|---|
| 1 | Python 3.10+ or C++17 | Compiler implementation language | x1 |
| 2 | PLY (Python Lex-Yacc) | Lexer and parser generator | x1 |
| 3 | LLVM / llvmlite | IR generation and optimization backend | x1 |
| 4 | Graphviz | AST visualization | x1 |
| 5 | pytest + hypothesis | Test suite and fuzzing | x1 |
| 6 | GCC (host compiler) | Reference implementation comparison | x1 |
| 7 | Make build system | Build automation | x1 |
| 8 | Valgrind (if using C++) | Memory leak detection | x1 |
| 9 | ANTLR4 (alternative parser) | Alternative grammar-based parser generator | x1 |
| 10 | VS Code + extensions | Development environment | x1 |
Step-by-Step Implementation
Follow these 6 steps carefully.
1
Language Grammar Definition
Define the language BNF (Backus-Naur Form) grammar. Example mini-language: statements (if/else, while, for, return), expressions (arithmetic, comparison, logical), data types (int, float, bool, string, arrays), functions (definition, calls), variable declarations. Write the complete grammar before coding — it drives all subsequent implementation. Verify grammar is unambiguous (no shift/reduce conflicts in YACC/PLY).
2
Recursive Descent Parser
Build the parser using PLY
3
Abstract Syntax Tree Design
AST represents program structure as a tree. Each node type: ASTNode base class with accept(visitor) method. Concrete nodes: Program(statements), Function(name, params, body), BinOp(left, op, right), IfStatement(condition, then_body, else_body), WhileLoop(condition, body), VarDecl(name, type, initializer), FuncCall(name, args), Literal(value, type). Implement a pretty-printer visitor to display the AST — essential for debugging parser.
4
Semantic Analysis and Type Checking
Walk the AST performing semantic analysis: symbol table construction (track variable names, types, scope), type checking (BinOp between incompatible types → TypeError), undefined variable detection (use before declaration → NameError), function signature validation (argument count and types match declaration), return type checking (all function paths return correct type). Implement scope stacking: enter function → push new scope, exit → pop scope.
5
LLVM IR Code Generation
Using llvmlite, generate LLVM IR from the typed AST. Each AST node has a codegen() method returning an LLVM value. LLVM IR is a typed, SSA-form assembly language. Function codegen: create llvmlite IRBuilder, allocate parameters as alloca (stack storage), generate body statements. BinOp codegen: builder.add/sub/mul for integer ops. IfStatement codegen: create basic blocks for then/else/merge, use builder.branch/conditional_branch.
6
Optimization and Target Code Generation
LLVM provides optimization passes automatically: constant folding, dead code elimination, inline expansion, loop unrolling. Apply optimization: llvm.PassManager with standard passes. Generate target code: llvm.Target.from_triple(
Code & Implementation
Core code for compiler_codegen.py:
compiler_codegen.py
Python
Testing & Troubleshooting
Test Compiler Design (Mini Language) by verifying each subsystem individually before full integration.
Troubleshooting Tips
Verify power voltages, check ground connections, use serial monitor for debug.
Real-World Applications
Domain-specific language (DSL) development
Configuration language processor
Query language implementation (SQL subset)
Hardware description language tools
Game scripting engine language
Scientific computing language
Build system language parser
Template engine language
Extensions & Next Steps
- Add garbage collection support (mark-and-sweep GC)
- Implement generics/polymorphism in the type system
- Add an incremental parser for IDE integration (error recovery)
- Build a language server protocol (LSP) implementation
- Implement a JIT compilation backend
Interactive Playground
Coming SoonAn interactive simulator will be available here — simulate circuits and run code in-browser without hardware.
Frequently Asked Questions
What is SSA (Static Single Assignment) form and why does LLVM use it?
SSA form: every variable is assigned exactly once, and every use of a variable refers to its single definition. In normal code, x = 1; x = x + 2 — x assigned twice. In SSA: x1 = 1; x2 = x1 + 2. At control flow merges (if-else paths), Φ (phi) nodes select the value from the appropriate path. SSA simplifies optimization: constant propagation, dead code elimination, and register allocation are simpler because data flow is explicit. LLVM IR is in SSA form.
What is the difference between a compiler and an interpreter?
Compiler: translates entire source program to machine code (or IR) before execution. Execution of compiled code is fast (no interpretation overhead). Examples: GCC, Clang. Interpreter: reads and executes source or bytecode line-by-line at runtime. More flexible (can modify program during execution), but slower. Examples: CPython, Ruby. JIT (Just-In-Time) compiler: interprets initially, compiles hot code paths to native code during execution. Examples: V8 (JavaScript), JVM HotSpot. Modern compilers often use all three stages.
What is register allocation and why is it one of the hardest problems in compiler design?
CPUs have a limited number of registers (e.g., x86-64 has 16 general-purpose registers). A program may use thousands of variables. Register allocation maps variables to registers for the duration of their live range (from first use to last use). When more variables are live simultaneously than registers available, some must be spilled to memory. Optimal register allocation is NP-complete (equivalent to graph coloring). Compilers use heuristic approximations (linear scan, graph coloring) that achieve near-optimal results in practical cases.
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