codon/README.md

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# What is Codon?

Codon is a high-performance Python implementation that compiles to native machine code without
any runtime overhead. Typical speedups over vanilla Python are on the order of 10-100x or more, on
a single thread. Codon's performance is typically on par with (and sometimes better than) that of
C/C++. Unlike Python, Codon supports native multithreading, which can lead to speedups many times
higher still.

*Think of Codon as Python reimagined for static, ahead-of-time compilation, built from the ground
up with best possible performance in mind.*

## Goals

- :bulb: **No learning curve:** Be as close to CPython as possible in terms of syntax, semantics and libraries
- :rocket: **Top-notch performance:** At *least* on par with low-level languages like C, C++ or Rust
- :computer: **Hardware support:** Full, seamless support for multicore programming, multithreading (no GIL!), GPU and more
- :chart_with_upwards_trend: **Optimizations:** Comprehensive optimization framework that can target high-level Python constructs
  and libraries
- :battery: **Interoperability:** Full interoperability with Python's ecosystem of packages and libraries

## Non-goals

- :x: *Drop-in replacement for CPython:* Codon is not a drop-in replacement for CPython. There are some
  aspects of Python that are not suitable for static compilation — we don't support these in Codon.
  There are ways to use Codon in larger Python codebases via its [JIT decorator](https://docs.exaloop.io/codon/interoperability/decorator)
  or [Python extension backend](https://docs.exaloop.io/codon/interoperability/pyext). Codon also supports
  calling any Python module via its [Python interoperability](https://docs.exaloop.io/codon/interoperability/python).
  See also [*"Differences with Python"*](https://docs.exaloop.io/codon/general/differences) in the docs.

- :x: *New syntax and language constructs:* We try to avoid adding new syntax, keywords or other language
  features as much as possible. While Codon does add some new syntax in a couple places (e.g. to express
  parallelism), we try to make it as familiar and intuitive as possible.

## How it works

<p align="center">
 <img src="docs/img/codon-figure.svg" width="90%" alt="Codon figure"/>
</p>

# Quick start

Download and install Codon with this command:

```bash
/bin/bash -c "$(curl -fsSL https://exaloop.io/install.sh)"
```

After following the prompts, the `codon` command will be available to use. For example:

- To run a program: `codon run file.py`
- To run a program with optimizations enabled: `codon run -release file.py`
- To compile to an executable: `codon build -release file.py`
- To generate LLVM IR: `codon build -release -llvm file.py`

Many more options are available and described in [the docs](https://docs.exaloop.io/codon/general/intro).

Alternatively, you can [build from source](https://docs.exaloop.io/codon/advanced/build).

# Examples

## Basics

Codon supports much of Python, and many Python programs will work with few if any modifications.
Here's a simple script `fib.py` that computes the 40th Fibonacci number...

``` python
from time import time

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

t0 = time()
ans = fib(40)
t1 = time()
print(f'Computed fib(40) = {ans} in {t1 - t0} seconds.')
```

... run through Python and Codon:

```
$ python3 fib.py
Computed fib(40) = 102334155 in 17.979357957839966 seconds.
$ codon run -release fib.py
Computed fib(40) = 102334155 in 0.275645 seconds.
```

## Using Python libraries

You can import and use any Python package from Codon via `from python import`. For example:

```python
from python import matplotlib.pyplot as plt
data = [x**2 for x in range(10)]
plt.plot(data)
plt.show()
```

(Just remember to set the `CODON_PYTHON` environment variable to the CPython shared library,
as explained in the [the Python interoperability docs](https://docs.exaloop.io/codon/interoperability/python).)

## Parallelism

Codon supports native multithreading via [OpenMP](https://www.openmp.org/). The `@par` annotation
in the code below tells the compiler to parallelize the following `for`-loop, in this case using
a dynamic schedule, chunk size of 100, and 16 threads.

```python
from sys import argv

def is_prime(n):
    factors = 0
    for i in range(2, n):
        if n % i == 0:
            factors += 1
    return factors == 0

limit = int(argv[1])
total = 0

@par(schedule='dynamic', chunk_size=100, num_threads=16)
for i in range(2, limit):
    if is_prime(i):
        total += 1

print(total)
```

Note that Codon automatically turns the `total += 1` statement in the loop body into an atomic
reduction to avoid race conditions. Learn more in the [multitheading docs](advanced/parallel.md).

Codon also supports writing and executing GPU kernels. Here's an example that computes the
[Mandelbrot set](https://en.wikipedia.org/wiki/Mandelbrot_set):

```python
import gpu

MAX    = 1000  # maximum Mandelbrot iterations
N      = 4096  # width and height of image
pixels = [0 for _ in range(N * N)]

def scale(x, a, b):
    return a + (x/N)*(b - a)

@gpu.kernel
def mandelbrot(pixels):
    idx = (gpu.block.x * gpu.block.dim.x) + gpu.thread.x
    i, j = divmod(idx, N)
    c = complex(scale(j, -2.00, 0.47), scale(i, -1.12, 1.12))
    z = 0j
    iteration = 0

    while abs(z) <= 2 and iteration < MAX:
        z = z**2 + c
        iteration += 1

    pixels[idx] = int(255 * iteration/MAX)

mandelbrot(pixels, grid=(N*N)//1024, block=1024)
```

GPU programming can also be done using the `@par` syntax with `@par(gpu=True)`. See the
[GPU programming docs](https://docs.exaloop.io/codon/advanced/gpu) for more details.

## NumPy support

Codon includes a feature-complete, fully-compiled native NumPy implementation. It uses the same
API as NumPy, but re-implements everything in Codon itself, allowing for a range of optimizations
and performance improvements.

Here's an example NumPy program that approximates $\pi$ using random numbers...

``` python
import time
import numpy as np

rng = np.random.default_rng(seed=0)
x = rng.random(500_000_000)
y = rng.random(500_000_000)

t0 = time.time()
# pi ~= 4 x (fraction of points in circle)
pi = ((x-1)**2 + (y-1)**2 < 1).sum() * (4 / len(x))
t1 = time.time()

print(f'Computed pi~={pi:.4f} in {t1 - t0:.2f} sec')
```

... run through Python and Codon:

```
$ python3 pi.py
Computed pi~=3.1417 in 2.25 sec
$ codon run -release pi.py
Computed pi~=3.1417 in 0.43 sec
```

Codon can speed up NumPy code through general-purpose and NumPy-specific compiler optimizations,
including inlining, fusion, memory allocation elision and more. Furthermore, Codon's NumPy
implementation works with its multithreading and GPU capabilities, and can even integrate with
[PyTorch](https://pytorch.org). Learn more in the [Codon-NumPy docs](https://docs.exaloop.io/codon/interoperability/numpy).

# Documentation

Please see [docs.exaloop.io](https://docs.exaloop.io) for in-depth documentation.