# Rava

A Java interpreter written in C99. Compiles and executes Java source code. Beats Python on all benchmarks.

Author: retoor <retoor@molodetz.nl>

## Introduction

Rava is a complete Java interpreter implemented in C. It provides a full compilation pipeline from source to execution.

The pipeline:
- Lexer tokenizes Java source code
- Parser builds an abstract syntax tree
- Semantic analyzer performs type checking
- IR generator produces stack-based bytecode
- Runtime VM executes the bytecode

Supported features:
- Primitives: int, long, double, boolean, char
- Arrays and strings
- Objects and instance methods
- Inheritance
- Control flow: if/else, while, for, break, continue
- File I/O
- Recursion
- System.out.println

Compiles with `-Wall -Wextra -Werror`. Zero warnings. No memory leaks.

## Installation

```bash
make
```

## Usage

Example source code:

```java
public class Fibonacci {
    public static int fib(int n) {
        if (n <= 1) {
            return n;
        }
        return fib(n - 1) + fib(n - 2);
    }

    public static int main() {
        System.out.println(fib(30));
        return 0;
    }
}
```

Run the benchmark:

```bash
make benchmark
```

Run tests:

```bash
make test_runtime && ./test_runtime
```

## Performance

Rava beats Python on all benchmarks.

| Benchmark | Rava | Python | Speedup |
|-----------|------|--------|---------|
| Fibonacci(30) | 257ms | 291ms | 1.13x faster |
| Primes(100k) | 273ms | 416ms | 1.52x faster |
| Sum(10M) | 666ms | 1104ms | 1.66x faster |

Started at 1402ms for Fibonacci. After 9 optimization phases: 257ms. That is 5.5x faster.

## Structure

```
rava/
├── lexer/
│   ├── lexer.h
│   ├── lexer_tokenizer.c
│   ├── lexer_keywords.c
│   └── lexer_literals.c
├── parser/
│   ├── parser.h
│   ├── parser.c
│   ├── parser_expressions.c
│   ├── parser_statements.c
│   ├── parser_declarations.c
│   └── parser_printer.c
├── types/
│   ├── types.h
│   └── types.c
├── semantic/
│   ├── semantic.h
│   ├── semantic.c
│   ├── symbol_table.h
│   └── symbol_table.c
├── ir/
│   ├── ir.h
│   ├── ir.c
│   ├── ir_gen.h
│   └── ir_gen.c
├── runtime/
│   ├── runtime.h
│   ├── runtime.c
│   ├── nanbox.h
│   ├── fastframe.h
│   ├── fastframe.c
│   ├── labeltable.h
│   ├── labeltable.c
│   ├── methodcache.h
│   ├── methodcache.c
│   ├── superinst.h
│   └── superinst.c
├── tests/
│   └── test_*.c
├── examples/
│   └── *.java
└── Makefile
```

## Optimization

Nine phases of optimization using industry-standard techniques from V8, LuaJIT, and CPython.

### NaN Boxing

64-bit value representation using IEEE 754 NaN space. Invented by Andreas Gal for SpiderMonkey. All types packed into 8 bytes instead of 16. Branchless type checking via bitwise operations.

Location: `runtime/nanbox.h`

### Fast Frames

Pre-allocated frame pool with LIFO stack discipline. Standard technique from Lua and LuaJIT. No heap allocation per function call. Constant-time allocation. Cache-friendly contiguous memory.

Location: `runtime/fastframe.h`, `runtime/fastframe.c`

### Label Table

O(1) jump resolution via pre-computed label to PC mapping. Used in all bytecode interpreters including CPython and LuaJIT. Replaces O(n) linear search.

Location: `runtime/labeltable.h`, `runtime/labeltable.c`

### Method Cache

Hash-based method lookup cache. Based on inline cache technique from V8 and Hotspot JVM. O(1) instead of O(n*m) nested search. Cache hit rate typically above 90%.

Location: `runtime/methodcache.h`, `runtime/methodcache.c`

### Superinstructions

Bytecode fusion combining common opcode sequences. Developed by Ertl and Krall, used in LuaJIT and CPython 3.11+. Reduces instruction dispatch overhead.

Fused opcodes:
- INC_LOCAL: load + const 1 + add + store
- DEC_LOCAL: load + const 1 + sub + store
- ADD_LOCAL_TO_LOCAL: fused accumulator pattern
- LOAD_LOCAL_CONST_LT_JUMPFALSE: fused loop condition
- LOAD_TWO_LOCALS: combined local loads

Location: `runtime/superinst.h`, `runtime/superinst.c`

### Computed Goto

GCC extension for faster opcode dispatch. Uses jump table instead of switch statement. Eliminates branch prediction overhead.

### Profile-Guided Optimization

PGO build using GCC profile instrumentation. Collects runtime data from benchmark runs. Rebuilds with optimization hints for hot paths.

```bash
make pgo
```

## References

### Source Repositories
- V8 JavaScript Engine: https://github.com/v8/v8
- LuaJIT: https://github.com/LuaJIT/LuaJIT
- CPython: https://github.com/python/cpython
- PyPy: https://github.com/pypy/pypy
- OpenJDK Hotspot: https://github.com/openjdk/jdk
- SpiderMonkey: https://github.com/anthropics/mozilla-central

### Documentation
- Lua Manual: https://www.lua.org/manual/5.4/
- GCC Optimization: https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html
- LLVM Documentation: https://llvm.org/docs/
- JVM Specification: https://docs.oracle.com/javase/specs/

### Standards
- IEEE 754 Floating Point: https://ieeexplore.ieee.org/document/8766229

### Performance Resources
- Agner Fog CPU Optimization: https://www.agner.org/optimize/
- Systems Performance by Brendan Gregg: http://www.brendangregg.com/