# JIT Performance Research Summary

## Findings

### MATLAB JIT Architecture (Based on Analysis)
1. **Static Analysis**: MATLAB performs parse-time analysis to identify loops with constant bounds
2. **Immediate Compilation**: Functions with constant-bound loops are compiled immediately without waiting for hot function detection
3. **Aggressive Optimizations**: MATLAB uses aggressive loop unrolling, inlining, and type specialization

### Our Implementation
1. ✅ **Static Analysis**: Implemented `has_constant_for_loop()` to detect constant-bound for loops at parse time
2. ✅ **Immediate Compilation**: `should_compile()` now checks static analysis first before falling back to profiler
3. ✅ **LLVM Optimizations**: Added aggressive loop optimization passes:
   - Loop unrolling
   - Loop rotation
   - Loop invariant code motion (LICM)
   - Induction variable simplification
   - Instruction combining
   - Global value numbering (GVN)
   - Control flow graph simplification

### Performance Issues Identified
1. **JIT Not Enabled**: Fixed - JIT now enables automatically when LLVM backend is available
2. **Lazy Compilation**: Modified `pt-eval.cc` to attempt compilation before execution
3. **IR Generation**: Direct arithmetic for loop variables (already implemented in `jit-ir-generator.cc`)

## Next Steps
- Monitor benchmark results after rebuild
- Consider additional type specialization
- Investigate function call overhead in JIT-compiled code