Ease of Python

Codon adopts Python's syntax, features and libraries, and is fully interoperable with Python


Performance of C

Codon uses LLVM to compile to native machine code, without vanilla Python's runtime overhead



Codon is fully extensible via plugins that can add new libraries, optimizations or even syntax

How it Works


Codon at a Glance

cart = ['Apple', 'Apple', 'Pear']
cost = {'Apple': 1.20, 'Pear': 1.45}
x = sum(cost[item] for item in cart)

If you know Python, there's no learning curve to using Codon. Same syntax, semantics and builtin modules. Unlike Python, Codon compiles to native code and runs orders of magnitude faster.

from python import numpy as np
arr = np.arange(10)
print(arr ** 2)

Codon has out-of-the-box interoperability with Python, meaning you can take full advantage of Python's expansive ecosystem of libraries, from NumPy to Pandas to Tensorflow to Matplotlib.

for i in range(10):
    print(i ** 2)

Since Codon is implemented from the ground up as a new compiler, it doesn't have any of Python's multithreading limitations. Codon has full-fledged OpenMP support to take advantage of multiple cores.

def hypot(a: float, b: float) -> float:
    from math import sqrt
    return sqrt(a**2 + b**2)

Codon adopts the type annotation syntax introduced by newer Python versions. While type annotations can be used to enforce types statically, they are completely optional and can be inferred automatically.

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

Within larger Python codebases, Codon can be integrated on a per-function basis via the @codon annotation to compile and optimize performance-critical segments of code.

from math import e, pi, sqrt
stirling = lambda n: sqrt(2*pi*n) * (n/e)**n
range(10) |> iter |> stirling |> print

Codon's new pipe operator |> can succinctly express complex dataflow, and compiles down to efficient code. Parallel pipelines are also supported via a parallel pipe operator ||>.

Covering a Range of Industries



Codon was initially developed to solve computing challenges in genomics and bioinformatics (see Seq), areas we continue to focus on heavily.


Codon's finance module accelerates and simplifies testing new trading strategies on large historical datasets.


Codon-native versions of popular Python libraries offer enhanced performance, better memory footprints and library-specific compiler optimizations.


Codon can target WebAssembly, allowing Python/Codon programs to run seamlessly in the browser.


Codon allows GPU kernels to be written in plain Python, and facilitates GPU programming without having to resort to CUDA.


We're actively developing modules for a wide range of industries and sectors. Stay tuned for more!

Codon for Data Science

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Exaloop and You

Try Codon

We're launching our early adopter program. Contact us to learn more!