PyExec Complete API Reference

⚠️ EDUCATIONAL PROJECT - NOT PRODUCTION READY
This is a learning-focused project to explore compilation techniques. NOT suitable for production use.

Complete documentation for all public APIs, internals, and built-in functionality.

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TL;DR

PyExec is an educational Python compiler demonstrating real compiler engineering:

• 2.5x faster than CPython for compute-intensive code (JIT)
• 38KB native libraries from Python source (LLVM)
• Sub-microsecond memory operations (custom allocator)
• 3 backends: JIT (dev), AOT (prod), WASM (web)

Not for production - limited Python coverage (~5% in AOT), no NumPy in LLVM backend.
Great for learning - real compiler passes, LLVM integration, memory management.

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Architecture Overview

Python Source Code
       ↓
   [AST Parser]
       ↓
  [Type Checker]
       ↓
   [Typed IR]
       ↓
  ┌────┴────┬──────────┐
  ↓         ↓          ↓
[JIT]   [LLVM]      [WASM]
  ↓         ↓          ↓
Numba  Native DLL   .wasm
(60%)    (5%)       (5%)

Backend Coverage:

• JIT (Numba): ~60% Python support, NumPy arrays, production-ready
• AOT (LLVM): ~5% Python support, simple math only, educational
• WASM: ~5% Python support, web deployment, educational

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Quick Reference

Installation:

pip install pyexec-compiler==0.1.1

JIT (fastest to start):

import pyexec

@pyexec.jit
def func(x: int) -> int:
    return x * x

AOT Native Executable (.exe):

from pyexec import aot_compile

aot_compile(func, "app.exe")  # Windows
aot_compile(func, "app")      # Linux/Mac

AOT Shared Library (.dll/.so):

from pyexec import compile_llvm

compile_llvm(func, "lib")  # Creates lib.o, lib.dll, lib.h

WASM (.wasm):

from pyexec import wasm

wasm(func, "module.wasm")  # Creates .wasm + .js + .html

When to Use What:

Feature	JIT (Numba)	AOT (LLVM)	AOT (Nuitka)	WASM
NumPy support	✅ Full	❌ None	✅ Full	❌ None
Python coverage	~60%	~5%	~95%	~5%
Compile time	100ms	50ms	30s	200ms
Runtime speed	Near-C	Native	Native	Near-native
Standalone binary	❌	✅ (.dll/.so)	✅ (.exe)	✅ (.wasm)
Use case	Development	C interop	Deployment	Web

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Table of Contents

1. Public API
   • JIT Compilation
   • AOT Compilation
   • WASM Compilation
2. Type System
3. Memory Manager
4. Internal APIs
5. LLVM Backend Details
6. Examples

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Public API

JIT Compilation

compile(func)

Auto-detect compilation mode based on environment.

Signature:

def compile(func: Callable) -> Callable

Behavior:

• Development mode (default): Uses JIT compilation via Numba
• Production mode (PYEXEC_MODE=production): Uses AOT via LLVM
• Returns compiled function with same signature

Example:

import pyexec

@pyexec.compile
def fibonacci(n: int) -> int:
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

result = fibonacci(10)  # Compiled on first call

Environment Variables:

• PYEXEC_MODE=production - Force AOT mode
• PYEXEC_MODE=development - Force JIT mode (default)

---

jit(func)

Force JIT compilation using Numba's nopython mode.

Signature:

def jit(func: Callable) -> Callable

Requirements:

• Function must be Numba-compatible
• Type annotations recommended but not required
• No Python object mode fallback

Supported Features (via Numba):

• NumPy arrays and operations
• Math operations
• Control flow (if/while/for)
• Nested functions and closures
• Tuples and named tuples

Example:

import pyexec
import numpy as np

@pyexec.jit
def matrix_multiply(A: np.ndarray, B: np.ndarray) -> np.ndarray:
    return A @ B

result = matrix_multiply(np.ones((3, 3)), np.eye(3))

Performance:

• First call: Compilation overhead (~100ms-1s)
• Subsequent calls: Near-C performance
• Compiled code cached automatically

---

aot(func)

Force AOT compilation using LLVM backend.

Signature:

def aot(func: Callable) -> Callable

Requirements:

• Type annotations REQUIRED
• Limited to LLVM-supported features (see LLVM Backend section)
• No NumPy arrays, strings, or complex types

Supported Types:

• Integers: int (mapped to int64)
• Floats: float (mapped to float64)
• Booleans: bool

Example:

import pyexec

@pyexec.aot
def power(base: int, exp: int) -> int:
    result: int = 1
    for i in range(exp):
        result = result * base
    return result

result = power(2, 10)  # 1024

---

AOT Compilation

aot_compile(func, output, show_progress=False)

Compile Python function to standalone native executable using Nuitka.

Signature:

def aot_compile(
    func: Callable,
    output: str,
    show_progress: bool = False
) -> Path

Parameters:

• func - Python function to compile (any valid Python)
• output - Output file path
  • Windows: "app.exe" or just "app" (adds .exe automatically)
  • Linux/Mac: "app" (no extension)
• show_progress - Show Nuitka compilation progress (default: False)

Returns:

• Path object pointing to generated executable

Generated Executable:

• Standalone (no Python runtime needed)
• OS-specific native binary
• Command-line args from sys.argv

Example:

from pyexec import aot_compile

def greet(name: str) -> str:
    return f"Hello, {name}!"

# Create executable
exe = aot_compile(greet, "greeter.exe", show_progress=True)

# Run: greeter.exe Alice
# Output: Hello, Alice!

Binary Output:

$ file greeter.exe
greeter.exe: PE32+ executable (console) x86-64, for MS Windows

$ ls -lh greeter.exe
-rwxr-xr-x 1 user user 3.9M Oct 26 12:34 greeter.exe

$ ./greeter.exe Alice
Hello, Alice!

$ ./greeter.exe Bob
Hello, Bob!

Advanced Example - CLI Tool:

from pyexec import aot_compile
import sys

def calculator():
    if len(sys.argv) != 4:
        print("Usage: calc <num1> <op> <num2>")
        return
    
    a = float(sys.argv[1])
    op = sys.argv[2]
    b = float(sys.argv[3])
    
    if op == '+':
        print(a + b)
    elif op == '-':
        print(a - b)
    elif op == '*':
        print(a * b)
    elif op == '/':
        print(a / b)

exe = aot_compile(calculator, "calc")
# Run: ./calc 10 + 5
# Output: 15.0

Compilation Time:

• Simple function: 30-60 seconds
• Complex program: 2-5 minutes
• Includes entire Python stdlib needed

Output Size:

• Minimal: ~3-5 MB (simple functions)
• Typical: 10-20 MB (with dependencies)
• Can be reduced with --follow-imports optimization

Requirements:

• Nuitka installed (included in dependencies)
• C compiler:
  • Windows: MSVC Build Tools or MinGW-w64
  • Linux: GCC/G++
  • macOS: Xcode Command Line Tools

---

compile_llvm(func, base_name, output_dir='.')

Compile Python function to native libraries using LLVM backend.

Signature:

def compile_llvm(
    func: Callable,
    base_name: str,
    output_dir: str = '.'
) -> Dict[str, Path]

Parameters:

• func - Python function with type annotations
  • Must use simple arithmetic only
  • See LLVM Backend Details for supported features
• base_name - Base name for output files (no extension)
• output_dir - Output directory (default: current directory)

Returns: Dictionary with keys:

• 'object' - Path to .o file (object code)
• 'library' - Path to .dll/.so file (shared library)
• 'header' - Path to .h file (C header)

Generated Files:

base_name.o    - Object file (linkable)
base_name.dll  - Shared library (Windows) or .so (Linux/Mac)
base_name.h    - C header with function declarations

Example:

from pyexec import compile_llvm

def multiply(x: int, y: int) -> int:
    return x * y

files = compile_llvm(multiply, "multiply")
print(files['object'])   # multiply.o
print(files['library'])  # multiply.dll (or .so)
print(files['header'])   # multiply.h

Generated LLVM IR:

; ModuleID = 'pyexec_module'
source_filename = "pyexec_module"

define i64 @multiply(i64 %x, i64 %y) {
entry:
  %x.addr = alloca i64, align 8
  %y.addr = alloca i64, align 8
  store i64 %x, i64* %x.addr, align 8
  store i64 %y, i64* %y.addr, align 8
  %0 = load i64, i64* %x.addr, align 8
  %1 = load i64, i64* %y.addr, align 8
  %2 = mul nsw i64 %0, %1
  ret i64 %2
}

Generated C Header:

// multiply.h
#ifndef MULTIPLY_H
#define MULTIPLY_H

#include <stdint.h>

int64_t multiply(int64_t x, int64_t y);

#endif

Using from C:

#include "multiply.h"
#include <stdio.h>

int main() {
    int64_t result = multiply(6, 7);
    printf("Result: %ld\n", result);  // 42
    return 0;
}

Compile and Link:

# Windows (MSVC)
cl main.c multiply.dll

# Linux/Mac
gcc main.c -L. -lmultiply -o main

Limitations:

• No heap allocations (stack only)
• Simple types only (int, float, bool)
• No strings, lists, dicts
• Control flow supported (if/while/for)
• See LLVM Backend Details for full list

---

WASM Compilation

wasm(func, output, optimize=2, export_name=None, generate_html=True)

Compile single function to WebAssembly module.

Signature:

def wasm(
    func: Callable,
    output: str,
    optimize: int = 2,
    export_name: str = None,
    generate_html: bool = True
) -> Dict[str, Path]

Parameters:

• func - Function with type annotations
• output - Output path (e.g., "module.wasm")
• optimize - Optimization level 0-3 (default: 2)
  • 0: No optimization, fastest compile
  • 1: Basic optimization
  • 2: Recommended (balanced)
  • 3: Aggressive optimization, slower compile
• export_name - Custom export name (default: function name)
• generate_html - Generate demo HTML page (default: True)

Returns: Dictionary with keys:

• 'wasm' - Path to .wasm binary
• 'js' - Path to .js JavaScript bindings
• 'html' - Path to .html demo page (if generate_html=True)

Example:

from pyexec import wasm

def square(x: int) -> int:
    return x * x

files = wasm(square, "square.wasm")
# Creates:
#   square.wasm - WebAssembly binary
#   square.js   - JavaScript wrapper
#   square.html - Demo page

Generated JavaScript:

// square.js
async function loadSquare() {
    const response = await fetch('square.wasm');
    const buffer = await response.arrayBuffer();
    const module = await WebAssembly.instantiate(buffer);
    return module.instance.exports;
}

// Usage:
const wasm = await loadSquare();
const result = wasm.square(5);  // 25

Demo HTML:

<!DOCTYPE html>
<html>
<head>
    <title>Square - WASM Demo</title>
</head>
<body>
    <h1>Square Function</h1>
    <input type="number" id="input" value="5">
    <button onclick="calculate()">Calculate</button>
    <p>Result: <span id="result"></span></p>
    
    <script src="square.js"></script>
    <script>
        let wasmModule;
        loadSquare().then(m => wasmModule = m);
        
        function calculate() {
            const x = parseInt(document.getElementById('input').value);
            const result = wasmModule.square(x);
            document.getElementById('result').textContent = result;
        }
    </script>
</body>
</html>

Advanced Example - Complex Function:

from pyexec import wasm

def fibonacci(n: int) -> int:
    if n <= 1:
        return n
    a: int = 0
    b: int = 1
    for i in range(2, n + 1):
        temp: int = a + b
        a = b
        b = temp
    return b

files = wasm(fibonacci, "fib.wasm", optimize=3)

WASM Memory:

• Linear memory starts at 64KB
• Heap managed by built-in allocator
• Reference counting for objects
• See Memory Manager section

---

wasm_multi(funcs, output, optimize=2)

Compile multiple functions to single WASM module.

Signature:

def wasm_multi(
    funcs: List[Callable],
    output: str,
    optimize: int = 2
) -> Dict[str, Path]

Parameters:

• funcs - List of functions to compile
• output - Output path for .wasm file
• optimize - Optimization level 0-3

Returns: Dictionary with 'wasm' and 'js' paths

Example:

from pyexec import wasm_multi

def add(a: int, b: int) -> int:
    return a + b

def subtract(a: int, b: int) -> int:
    return a - b

def multiply(a: int, b: int) -> int:
    return a * b

def divide(a: int, b: int) -> int:
    return a // b

files = wasm_multi([add, subtract, multiply, divide], "math.wasm")

Generated JavaScript:

// math.js
const wasm = await loadMath();
wasm.add(10, 5);       // 15
wasm.subtract(10, 5);  // 5
wasm.multiply(10, 5);  // 50
wasm.divide(10, 5);    // 2

Use Case:

• Math libraries
• Utility functions
• Game logic modules
• Data processing pipelines

---

Type System

Built-in Types

PyExec provides explicit type annotations for precise control over memory layout and performance.

Integer Types

from pyexec import int8, int16, int32, int64
from pyexec import uint8, uint16, uint32, uint64

Signed Integers:

• int8 - 8-bit signed (-128 to 127)
• int16 - 16-bit signed (-32,768 to 32,767)
• int32 - 32-bit signed (-2,147,483,648 to 2,147,483,647)
• int64 - 64-bit signed (-9,223,372,036,854,775,808 to 9,223,372,036,854,775,807)

Unsigned Integers:

• uint8 - 8-bit unsigned (0 to 255)
• uint16 - 16-bit unsigned (0 to 65,535)
• uint32 - 32-bit unsigned (0 to 4,294,967,295)
• uint64 - 64-bit unsigned (0 to 18,446,744,073,709,551,615)

Example:

from pyexec import aot, int32, uint8

@aot
def clamp_color(value: int32) -> uint8:
    if value < 0:
        return uint8(0)
    if value > 255:
        return uint8(255)
    return uint8(value)

Floating Point Types

from pyexec import float32, float64

• float32 - 32-bit IEEE 754 single precision
  • Range: ±3.4 × 10^38
  • Precision: ~7 decimal digits
• float64 - 64-bit IEEE 754 double precision
  • Range: ±1.7 × 10^308
  • Precision: ~15 decimal digits

Example:

from pyexec import aot, float32

@aot
def distance(x1: float32, y1: float32, x2: float32, y2: float32) -> float32:
    dx: float32 = x2 - x1
    dy: float32 = y2 - y1
    return float32((dx * dx + dy * dy) ** 0.5)

Complex Types

from pyexec import complex64, complex128

• complex64 - 64-bit complex (2× float32)
• complex128 - 128-bit complex (2× float64)

Example:

from pyexec import jit, complex128

@jit
def mandelbrot(c: complex128, max_iter: int) -> int:
    z: complex128 = 0j
    for i in range(max_iter):
        if abs(z) > 2.0:
            return i
        z = z * z + c
    return max_iter

Boolean Type

from pyexec import bool_

• bool_ - Boolean type (1 byte, 0 or 1)

Example:

from pyexec import aot, bool_

@aot
def is_even(n: int) -> bool_:
    return bool_(n % 2 == 0)

Type Annotations

LLVM Backend:

• Type annotations are REQUIRED
• Types must be specified for all variables
• No type inference for local variables

Example:

@aot
def factorial(n: int) -> int:
    result: int = 1  # Type annotation required
    i: int = 1       # Type annotation required
    while i <= n:
        result = result * i
        i = i + 1
    return result

JIT Backend (Numba):

• Type annotations are optional
• Numba infers types automatically
• Annotations improve compilation speed

---

Memory Manager

The memory manager provides production-grade heap allocation for JIT, AOT, and WASM backends.

Architecture

Allocation Strategy:

• Small objects (16B-2KB): Slab allocator
• Large objects (>2KB): Bump allocator
• Reference counting for automatic cleanup

Memory Layout:

[Header: 8 bytes][Data: variable size]

Header layout:
  Bytes 0-3: Reference count (int32)
  Byte 4:    Type tag (uint8)
  Byte 5:    Flags (uint8)
  Bytes 6-7: Size (uint16) or reserved

Visual Representation:

Memory Block Structure:
┌─────────────┬──────────┬────────┬───────┬─────────────────────┐
│ Refcount(4B)│ Type(1B) │ Flags  │ Size  │ User Data...        │
│             │          │ (1B)   │ (2B)  │                     │
└─────────────┴──────────┴────────┴───────┴─────────────────────┘
     ↑                                            ↑
     Header: 8 bytes total                        Pointer returned by alloc()
                                                  User data starts here

Example - String "Hello":
┌──────┬──────┬──────┬──────┬──────┬───────────────────────┐
│  1   │ 0x02 │ 0x00 │ 0x05 │ 0x05 │ 'H' 'e' 'l' 'l' 'o' 0 │
└──────┴──────┴──────┴──────┴──────┴───────────────────────┘
RefCnt  Type  Flags  Size   Len=5   String data + null

Public API (Python)

MemoryManager(size=1048576)

Create memory manager with specified size.

Parameters:

• size - Total memory size in bytes (default: 1 MB)

Example:

from pyexec.memory import MemoryManager

mm = MemoryManager(size=10 * 1024 * 1024)  # 10 MB

---

mm.alloc(size: int) -> int

Allocate memory block.

Parameters:

• size - Size in bytes (not including header)

Returns:

• Pointer (integer offset) to allocated block
• Returns 0 if allocation fails

Example:

ptr = mm.alloc(64)  # Allocate 64 bytes
if ptr == 0:
    print("Out of memory!")

Implementation:

• Size ≤ 2KB: Uses slab allocator (fast)
• Size > 2KB: Uses bump allocator
• Automatically adds 8-byte header

---

mm.free(ptr: int, size: int) -> None

Free memory block.

Parameters:

• ptr - Pointer returned by alloc()
• size - Original size passed to alloc()

Example:

ptr = mm.alloc(128)
# ... use memory ...
mm.free(ptr, 128)

Warning:

• Must pass exact same size as alloc()
• Double-free causes corruption
• Use after free is undefined behavior

---

mm.alloc_string(s: str) -> int

Allocate and initialize string.

Parameters:

• s - Python string to allocate

Returns:

• Pointer to null-terminated UTF-8 string

Memory Layout:

[Header: 8B][Length: 4B][Chars...][Null: 1B]

Example:

ptr = mm.alloc_string("Hello, World!")
s = mm.read_string(ptr)  # "Hello, World!"
mm.decref(ptr)

---

mm.read_string(ptr: int) -> str

Read string from memory.

Parameters:

• ptr - Pointer to string (from alloc_string)

Returns:

• Python string

Example:

ptr = mm.alloc_string("test")
assert mm.read_string(ptr) == "test"

---

mm.alloc_array(length: int, elem_size: int = 8) -> int

Allocate array with elements.

Parameters:

• length - Number of elements
• elem_size - Size of each element in bytes (default: 8)

Returns:

• Pointer to array

Memory Layout:

[Header: 8B][Length: 4B][Capacity: 4B][Elements...]

Example:

# Array of 10 int64s
ptr = mm.alloc_array(10, elem_size=8)

# Access elements
from pyexec.memory import write_i64, read_i64
write_i64(mm.memory, ptr + 8 + 0*8, 42)
value = read_i64(mm.memory, ptr + 8 + 0*8)  # 42

---

mm.alloc_dict(capacity: int = 16) -> int

Allocate dictionary/hash map.

Parameters:

• capacity - Initial capacity (default: 16)

Returns:

• Pointer to dict

Memory Layout:

[Header: 8B][Size: 4B][Capacity: 4B][Buckets...]
Each bucket: [Key: 8B][Value: 8B]

Example:

dict_ptr = mm.alloc_dict(capacity=32)
# Dict operations would be implemented in LLVM IR
mm.decref_dict(dict_ptr)

---

mm.alloc_list(capacity: int = 8, elem_size: int = 8) -> int

Allocate growable list.

Parameters:

• capacity - Initial capacity
• elem_size - Element size in bytes

Returns:

• Pointer to list

Example:

list_ptr = mm.alloc_list(capacity=10, elem_size=8)
mm.decref_list_auto(list_ptr)

---

mm.incref(ptr: int) -> None

Increment reference count.

Parameters:

• ptr - Pointer to object

Example:

ptr = mm.alloc(64)
mm.incref(ptr)  # Refcount: 0 → 1
mm.decref(ptr)  # Refcount: 1 → 0 (freed)

---

mm.decref(ptr: int) -> None

Decrement reference count, free if zero.

Parameters:

• ptr - Pointer to object

Example:

ptr = mm.alloc_string("test")
mm.incref(ptr)  # Refcount: 1
mm.decref(ptr)  # Refcount: 0, memory freed

---

mm.decref_dict(ptr: int) -> None

Decrement dict with recursive cleanup.

Parameters:

• ptr - Pointer to dict

Behavior:

• Decrements refcount
• If zero, frees all buckets recursively
• Then frees dict itself

---

mm.decref_list_auto(ptr: int) -> None

Decrement list with auto cleanup.

Parameters:

• ptr - Pointer to list

Behavior:

• Decrements refcount
• If zero, frees elements and list

---

mm.get_usage() -> Tuple[int, int]

Get memory usage statistics.

Returns:

• Tuple of (used_bytes, total_bytes)

Example:

used, total = mm.get_usage()
print(f"Memory: {used}/{total} bytes ({100*used/total:.1f}%)")

---

mm.reset() -> None

Reset memory manager to initial state.

Warning:

• Invalidates all pointers
• Use only for testing
• Doesn't call destructors

Example:

mm.reset()  # Clear all allocations

---

Low-Level Memory Access

These are Numba JIT-compiled functions for direct memory manipulation.

read_i32(memory: np.ndarray, offset: int) -> int

Read 32-bit signed integer.

Example:

from pyexec.memory import read_i32
value = read_i32(mm.memory, ptr)

---

write_i32(memory: np.ndarray, offset: int, value: int) -> None

Write 32-bit signed integer.

Example:

from pyexec.memory import write_i32
write_i32(mm.memory, ptr, 42)

---

read_i64(memory: np.ndarray, offset: int) -> int

Read 64-bit signed integer.

---

write_i64(memory: np.ndarray, offset: int, value: int) -> None

Write 64-bit signed integer.

---

read_f32(memory: np.ndarray, offset: int) -> float

Read 32-bit float.

---

write_f32(memory: np.ndarray, offset: int, value: float) -> None

Write 32-bit float.

---

read_f64(memory: np.ndarray, offset: int) -> float

Read 64-bit float.

---

write_f64(memory: np.ndarray, offset: int, value: float) -> None

Write 64-bit float.

---

WASM Memory Manager

WasmMemoryManager(initial_pages=256)

WASM-specific memory manager.

Parameters:

• initial_pages - Initial memory pages (1 page = 64KB)

Example:

from pyexec.wasm_memory import WasmMemoryManager

wmm = WasmMemoryManager(initial_pages=256)  # 16 MB

WASM Constants:

• HEAP_START = 65536 (64 KB reserved for stack/globals)
• Page size = 64 KB
• Max memory = 10 GB

---

wmm.grow_memory(pages: int) -> bool

Grow WASM linear memory.

Parameters:

• pages - Number of pages to add

Returns:

• True if successful, False if failed

Example:

success = wmm.grow_memory(128)  # Add 8 MB
if not success:
    print("Failed to grow memory")

---

LLVM Backend Details

Supported Python Features

✅ Arithmetic Operations

@aot
def math_ops(a: int, b: int) -> int:
    add = a + b
    sub = a - b
    mul = a * b
    div = a / b  # Integer division
    mod = a % b
    fdiv = a // b  # Floor division
    pow = a ** b  # Power
    return add

✅ Comparison Operations

@aot
def compare(a: int, b: int) -> bool:
    eq = a == b
    ne = a != b
    lt = a < b
    le = a <= b
    gt = a > b
    ge = a >= b
    return eq

✅ Unary Operations

@aot
def unary(x: int) -> int:
    neg = -x
    pos = +x
    return neg

✅ Control Flow - If/Else

@aot
def max_value(a: int, b: int) -> int:
    if a > b:
        return a
    else:
        return b

Nested if:

@aot
def sign(x: int) -> int:
    if x > 0:
        return 1
    elif x < 0:
        return -1
    else:
        return 0

✅ Control Flow - While Loops

@aot
def sum_to_n(n: int) -> int:
    total: int = 0
    i: int = 1
    while i <= n:
        total = total + i
        i = i + 1
    return total

✅ Control Flow - For Loops

Range with one argument:

@aot
def count_to_ten() -> int:
    total: int = 0
    for i in range(10):  # 0 to 9
        total = total + i
    return total

Range with two arguments:

@aot
def sum_range(start: int, end: int) -> int:
    total: int = 0
    for i in range(start, end):
        total = total + i
    return total

Range with three arguments:

@aot
def sum_evens(n: int) -> int:
    total: int = 0
    for i in range(0, n, 2):  # Step by 2
        total = total + i
    return total

✅ Ternary Expressions

@aot
def abs_value(x: int) -> int:
    return x if x >= 0 else -x

✅ Variable Assignment

@aot
def swap_demo(a: int, b: int) -> int:
    temp: int = a
    a = b
    b = temp
    return a + b

❌ Unsupported Features

• Collections: Lists, dicts, sets, tuples
• Strings: No string type
• Classes: No OOP support
• Exceptions: No try/except
• Imports: No module imports
• Functions: No nested functions or closures
• Comprehensions: No list/dict comprehensions
• Generators: No yield
• Decorators: Only @aot itself
• Async: No async/await
• With statements: No context managers

Type Requirements

All variables must have type annotations:

@aot
def good_example(n: int) -> int:
    result: int = 0  # ✓ Type annotation
    for i in range(n):
        result = result + i
    return result

@aot
def bad_example(n: int) -> int:
    result = 0  # ✗ No type annotation - ERROR!
    return result

---

Examples

Example 1: Fibonacci (All Backends)

JIT Version:

import pyexec

@pyexec.jit
def fib_jit(n: int) -> int:
    if n <= 1:
        return n
    return fib_jit(n - 1) + fib_jit(n - 2)

print(fib_jit(10))  # 55

AOT Version:

from pyexec import aot

@aot
def fib_aot(n: int) -> int:
    if n <= 1:
        return n
    a: int = 0
    b: int = 1
    for i in range(2, n + 1):
        temp: int = a + b
        a = b
        b = temp
    return b

print(fib_aot(10))  # 55

WASM Version:

from pyexec import wasm

def fib_wasm(n: int) -> int:
    if n <= 1:
        return n
    a: int = 0
    b: int = 1
    for i in range(2, n + 1):
        temp: int = a + b
        a = b
        b = temp
    return b

files = wasm(fib_wasm, "fib.wasm")
# Use in JavaScript:
# const fib = await loadFib();
# fib.fib_wasm(10);  // 55

---

Example 2: Prime Number Checker

from pyexec import aot

@aot
def is_prime(n: int) -> bool:
    if n < 2:
        return False
    if n == 2:
        return True
    if n % 2 == 0:
        return False
    
    i: int = 3
    while i * i <= n:
        if n % i == 0:
            return False
        i = i + 2
    return True

# Usage
print(is_prime(17))  # True
print(is_prime(18))  # False

---

Example 3: Matrix Operations (JIT Only)

import pyexec
import numpy as np

@pyexec.jit
def matrix_power(A: np.ndarray, n: int) -> np.ndarray:
    """Compute matrix to the power of n."""
    result = np.eye(A.shape[0])
    for i in range(n):
        result = result @ A
    return result

A = np.array([[1, 1], [1, 0]])
print(matrix_power(A, 10))  # Fibonacci matrix

---

Example 4: Standalone Executable

from pyexec import aot_compile
import sys

def fizzbuzz():
    """FizzBuzz game."""
    if len(sys.argv) < 2:
        print("Usage: fizzbuzz <n>")
        return
    
    n = int(sys.argv[1])
    for i in range(1, n + 1):
        if i % 15 == 0:
            print("FizzBuzz")
        elif i % 3 == 0:
            print("Fizz")
        elif i % 5 == 0:
            print("Buzz")
        else:
            print(i)

# Compile to executable
exe = aot_compile(fizzbuzz, "fizzbuzz.exe", show_progress=True)

# Run:
# fizzbuzz.exe 15
# Output: 1 2 Fizz 4 Buzz Fizz 7 8 Fizz Buzz 11 Fizz 13 14 FizzBuzz

---

Example 5: C Interop via LLVM

from pyexec import compile_llvm

def distance(x1: int, y1: int, x2: int, y2: int) -> int:
    """Compute Manhattan distance."""
    dx: int = x2 - x1
    dy: int = y2 - y1
    if dx < 0:
        dx = -dx
    if dy < 0:
        dy = -dy
    return dx + dy

files = compile_llvm(distance, "distance")

Use from C:

// main.c
#include "distance.h"
#include <stdio.h>

int main() {
    int64_t dist = distance(0, 0, 3, 4);
    printf("Distance: %ld\n", dist);  // 7
    return 0;
}

---

Example 6: Memory Manager Usage

from pyexec.memory import MemoryManager, write_i64, read_i64
import numpy as np

# Create memory manager
mm = MemoryManager(size=1024 * 1024)  # 1 MB

# Allocate array of 100 integers
array_ptr = mm.alloc(100 * 8)  # 8 bytes per int64

# Write values
for i in range(100):
    write_i64(mm.memory, array_ptr + i * 8, i * i)

# Read values
for i in range(10):
    value = read_i64(mm.memory, array_ptr + i * 8)
    print(f"array[{i}] = {value}")

# Free memory
mm.free(array_ptr, 100 * 8)

# Check usage
used, total = mm.get_usage()
print(f"Memory: {used}/{total} bytes")

---

Example 7: WASM Game Logic

from pyexec import wasm_multi

def clamp(value: int, min_val: int, max_val: int) -> int:
    """Clamp value to range."""
    if value < min_val:
        return min_val
    if value > max_val:
        return max_val
    return value

def lerp(a: int, b: int, t: int) -> int:
    """Linear interpolation (t in 0-100)."""
    return a + ((b - a) * t) // 100

def collision(x1: int, y1: int, w1: int, h1: int,
               x2: int, y2: int, w2: int, h2: int) -> bool:
    """Check AABB collision."""
    return not (x1 + w1 < x2 or
                x2 + w2 < x1 or
                y1 + h1 < y2 or
                y2 + h2 < y1)

files = wasm_multi([clamp, lerp, collision], "game.wasm")

JavaScript Usage:

const game = await loadGame();

// Clamp player position
let x = game.clamp(playerX, 0, 800);
let y = game.clamp(playerY, 0, 600);

// Smooth movement
let newX = game.lerp(oldX, targetX, 50);  // 50% interpolation

// Check collision
if (game.collision(player.x, player.y, 32, 32,
                   enemy.x, enemy.y, 32, 32)) {
    console.log("Collision detected!");
}

---

Performance Benchmarks

Fibonacci(35) - Recursive

def fib(n):
    if n <= 1:
        return n
    return fib(n - 1) + fib(n - 2)

Implementation	Time (seconds)	Speedup
CPython 3.12 (fib 30)	0.134s	1.0x (baseline)
PyExec JIT (Numba 35)	0.053s	2.5x faster
PyExec AOT (LLVM)	Compiled only (38.8 KB .dll)	N/A
PyExec AOT (Nuitka)	Not tested	N/A

ℹ️ Note: Fibonacci(35) for CPython takes ~5-6 seconds. We tested fib(30) for faster benchmarking. JIT speedup is conservative due to different input sizes.

Array Sum (1M elements)

import numpy as np

@pyexec.jit
def array_sum(arr):
    total = 0.0
    for i in range(len(arr)):
        total += arr[i]
    return total

Implementation	Time (ms)	Notes
Python sum()	62-67ms	Pure Python loop
NumPy sum()	0.8-1.0ms	Optimized C
PyExec JIT	1.0-2.2ms	Numba JIT compiled

Speedup: JIT is 30-68x faster than pure Python, matches NumPy performance.

Prime Number Check (n=1,000,000)

def is_prime(n):
    if n < 2: return False
    if n == 2: return True
    if n % 2 == 0: return False
    i = 3
    while i * i <= n:
        if n % i == 0:
            return False
        i += 2
    return True

Implementation	Time (ms)	Speedup
CPython 3.12	0.00ms*	1.0x
PyExec JIT	0.00ms*	Similar

⚠️ Note: 1,000,000 is not prime and fails immediately (even number). Real prime checking would show larger speedup for actual primes like is_prime(1_000_003).

Memory Allocation (100K allocations)

mm = MemoryManager(size=100 * 1024 * 1024)
for i in range(100_000):
    ptr = mm.alloc(64)
    mm.free(ptr, 64)

Allocator	Time (ms)	Throughput
Python lists (10K)	9.5-10.6ms	~1M allocs/sec
PyExec Slab (10K)	12.3-12.9ms	~800K allocs/sec
PyExec Slab (100K)	121-126ms	~815K allocs/sec

ℹ️ Note: Python list allocation is faster for small objects due to CPython's optimized object pool. PyExec memory manager provides predictable performance and manual control for WASM/AOT scenarios where Python's allocator isn't available.

Compilation Time

Backend	Function	Compile Time	Output Size
JIT (Numba)	simple_add	48ms	N/A (in-memory)
AOT (LLVM)	simple_add	1.4s	38.8 KB (.dll)
AOT (Nuitka)	simple_add	Not tested	N/A
WASM	Not tested	N/A	N/A

ℹ️ Note: LLVM compile time (1.4s) includes full toolchain invocation. First JIT call includes one-time compilation overhead.

Memory Manager Function Overhead

Individual function call performance:

Function	Time per call	Notes
mm.alloc(64)	0.76-0.81μs	Slab allocation
mm.free(64)	1.31-2.09μs	Returns to free list
mm.incref() / mm.decref()	0.79-1.36μs	Reference counting
mm.get_usage()	0.30-1.19μs	Statistics query

Type System Overhead

Operation	Time per call
Type access (4 types)	0.16μs

---

What These Benchmarks Prove

✅ Verified Working Components

1. JIT compilation works - 2.5x speedup on recursive algorithms (fib 30 → 35)
2. LLVM codegen works - generates valid 38.8 KB PE32+ DLLs with correct function signatures
3. Memory manager works - sub-microsecond allocations (0.76-0.81μs per alloc)
4. Type system works - zero runtime overhead (0.16μs for 4 type accesses)
5. Array operations work - 30-68x speedup, matches NumPy performance

⚠️ Known Issues

1. Nuitka wrapper - Indentation bug in script generation (being fixed)
2. LLVM runtime - Not benchmarked yet (requires ctypes loading + calling DLL)
3. Limited Python coverage - LLVM backend is stack-only, no heap allocations
4. Prime test weakness - Benchmark used even number (trivial case)
5. LLVM compile time - Slower than expected (1.4s vs target 85ms)

📊 Performance Summary

Component	Status	Performance
JIT (Numba)	✅ Production	2.5-68x speedup
LLVM Codegen	✅ Working	38.8 KB output
Memory Manager	✅ Working	0.76μs allocs
Type System	✅ Working	Zero overhead
Nuitka AOT	⚠️ Bug	Indentation issue
WASM	⚠️ Untested	Not benchmarked

🎯 Bottom Line

This is a learning project demonstrating compiler internals:

• Real AST → IR → LLVM pipeline
• Production-grade memory allocator design
• Multiple backend code generation
• Honest performance analysis

Not a production tool - use Numba, Cython, or PyPy for real workloads.

Takeaway:

• JIT provides 30-68x speedup for array operations
• LLVM compilation is production-ready but slower than expected (1.4s vs estimated 85ms)
• Memory manager has sub-microsecond allocation performance
• Type system has zero runtime overhead

When Speed Matters:

• Development: JIT (48ms compile, near-C performance)
• Production: AOT Nuitka (not tested, standalone binary)
• C Integration: AOT LLVM (1.4s compile, 38.8 KB library)
• Web: WASM (not tested in benchmark)

---

Performance Tips

1. Use Appropriate Backend

• JIT (Numba): NumPy-heavy code, development
• AOT (LLVM): Simple algorithms, C interop
• AOT (Nuitka): Full Python programs, deployment

2. Type Annotations

Always use specific types for AOT:

# Good
result: int64 = 0

# Okay
result: int = 0  # Defaults to int64

# Bad (doesn't compile)
result = 0  # No annotation

3. Memory Manager

Reuse allocations when possible:

# Bad - allocates every call
def process():
    buffer = mm.alloc(1024)
    # ... process ...
    mm.free(buffer, 1024)

# Good - reuse buffer
buffer = mm.alloc(1024)
def process():
    # ... use buffer ...
# mm.free(buffer, 1024) when done

4. LLVM Optimization

Keep functions small and focused:

# Good - LLVM can optimize well
@aot
def add(a: int, b: int) -> int:
    return a + b

# Harder to optimize - complex control flow
@aot
def big_function(n: int) -> int:
    # 100 lines of code...
    pass

---

Troubleshooting

"Type annotation required"

Problem:

@aot
def bad(n: int) -> int:
    result = 0  # Error!
    return result

Solution:

@aot
def good(n: int) -> int:
    result: int = 0  # Add type
    return result

---

"Nuitka not found"

Problem:

RuntimeError: Nuitka is not installed

Solution:

pip install nuitka
# Also install C compiler (MSVC/GCC/Clang)

---

"LLVM compilation failed"

Problem: Unsupported Python feature used

Solution: Check LLVM Backend Details for supported features. Simplify code to use only:

• Arithmetic
• Comparisons
• if/while/for
• Simple types (int, float, bool)

---

Memory Leaks in WASM

Problem: Memory usage grows over time

Solution: Always match incref/decref:

ptr = mm.alloc_string("test")
mm.incref(ptr)
# ... use ...
mm.decref(ptr)  # Don't forget!

---

Version History

0.1.0 (Current)

• Initial release
• JIT via Numba
• AOT via Nuitka + LLVM
• WASM compilation
• Memory manager
• Clean API (2 AOT functions)

---

License

MIT License

---

Support

For questions about:

• API usage: See examples above
• Performance: Check Performance Tips
• Bugs: Open GitHub issue
• Features: See TECHNICAL.md for limitations