Advanced
Performance & JIT

Performance & JIT Compilation

Flowa features a custom JIT compiler that translates bytecode to native machine code for optimal performance.

JIT Compilation

The JIT (Just-In-Time) compiler optimizes hot code paths:

  • Hot loop detection: Frequently executed loops are compiled to native code
  • Native code generation: x64/arm64 machine code for maximum speed
  • Register allocation: Efficient use of CPU registers
  • Constant folding: Compile-time evaluation of constant expressions

Benchmarks

Performance on Apple Silicon (arm64):

  • 100M Loop: 0.34s (LLVM JIT)
  • 10M Loop: 0.06s
  • 5M Printing: 0.04s
// 100M counter loop
let i = 0;
while (i < 100000000) {
    i = i + 1;
}

Performance Tips

1. Cache Array Length

// Slower - recalculates length each iteration
let i = 0;
while (i < len(arr)) {
    print(arr[i]);
    i = i + 1;
}
 
// Faster - cache length
let count = len(arr);
let i = 0;
while (i < count) {
    print(arr[i]);
    i = i + 1;
}

2. Avoid String Concatenation in Loops

// Slower
let result = "";
let i = 0;
while (i < 1000) {
    result = result + tostring(i) + ",";
    i = i + 1;
}
 
// Faster - build array then join
let parts = [];
let i = 0;
while (i < 1000) {
    parts = push(parts, tostring(i));
    i = i + 1;
}
let result = join(parts, ",");

3. Direct Array Access

// Direct access is faster than repeated operations
let value = arr[i];
print(value);
print(value + 1);
 
// Slower - multiple lookups
print(arr[i]);
print(arr[i] + 1);

4. func Call Overhead

// Inlining simple operations can be faster
let result = x * 2;  // Faster
 
func double(n) {
    return n * 2;
}
let result = double(x);  // func call overhead

5. Use Local Variables

// Locals are faster than globals
func process_data(arr) {
    let count = len(arr);  // Local
    let i = 0;
    while (i < count) {
        // Fast local access
        i = i + 1;
    }
}

Memory Management

Flowa uses:

  • Reference counting: Automatic memory management
  • Garbage collection: Reclaims unused memory
  • Object pooling: Reuses common objects

Memory Tips

  • Reuse objects when possible
  • Clear large arrays when done
  • Avoid circular references
  • Close database connections

Profiling

Monitor performance:

func benchmark(fn, iterations) {
    let start = "time_start";  // Get timestamp
    
    let i = 0;
    while (i < iterations) {
        fn();
        i = i + 1;
    }
    
    let end = "time_end";  // Get timestamp
    print("Completed in: " + tostring(end - start) + "ms");
}

Best Practices

  1. Write simple code first - Optimize only when needed
  2. Measure before optimizing - Profile to find bottlenecks
  3. Use appropriate data structures - Arrays for lists, hashes for lookups
  4. Minimize I/O - Batch file/database operations
  5. Cache expensive calculations - Store results for reuse

Next Steps

Time (time)Concurrency (Actors)