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* Fix underscore float parsing * Add tests * Update float parsing --------- Co-authored-by: A. R. Shajii <ars@ars.me> |
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README.md
Docs · FAQ · Blog · Chat · Roadmap · Benchmarks
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
- 💡 No learning curve: Be as close to CPython as possible in terms of syntax, semantics and libraries
- 🚀 Top-notch performance: At least on par with low-level languages like C, C++ or Rust
- 💻 Hardware support: Full, seamless support for multicore programming, multithreading (no GIL!), GPU and more
- 📈 Optimizations: Comprehensive optimization framework that can target high-level Python constructs and libraries
- 🔋 Interoperability: Full interoperability with Python's ecosystem of packages and libraries
Non-goals
-
❌ 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 or Python extension backend. Codon also supports calling any Python module via its Python interoperability. See also "Differences with Python" in the docs.
-
❌ 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.
Install
Pre-built binaries for Linux (x86_64) and macOS (x86_64 and arm64) are available alongside each release. Download and install with:
/bin/bash -c "$(curl -fsSL https://exaloop.io/install.sh)"
Or you can build from source.
Examples
Codon is a Python-compatible language, and many Python programs will work with few if any modifications:
def fib(n):
a, b = 0, 1
while a < n:
print(a, end=' ')
a, b = b, a+b
print()
fib(1000)
The codon
compiler has a number of options and modes:
# compile and run the program
codon run fib.py
# 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
# compile and run the program with optimizations enabled
codon run -release fib.py
# 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
# compile to executable with optimizations enabled
codon build -release -exe fib.py
./fib
# 0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
# compile to LLVM IR file with optimizations enabled
codon build -release -llvm fib.py
# outputs file fib.ll
See the docs for more options and examples.
You can import and use any Python package from Codon. For example:
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 docs.)
This prime counting example showcases Codon's OpenMP support, enabled
with the addition of one line. The @par
annotation tells the compiler to parallelize the
following for
-loop, in this case using a dynamic schedule, chunk size of 100, and 16 threads.
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)
Codon supports writing and executing GPU kernels. Here's an example that computes the Mandelbrot set:
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)
.
Documentation
Please see docs.exaloop.io for in-depth documentation.