Codon: A High-Performance Python Compiler

Python is a popular language in the world of data science and machine learning because of its simplicity and the vast array of libraries available to use. However, it can be slow in terms of performance, especially when running on a single thread. Codon is a high-performance Python compiler that compiles Python code to native machine code without any runtime overhead. This article will explore what Codon is, how it works, and how you can use it to speed up your Python code.

What is Codon?

Codon is a Python compiler that is designed to optimize performance. It compiles Python code to native machine code, which allows it to run much faster than interpreted Python code. The main benefit of Codon is that it eliminates the runtime overhead that comes with interpreted languages like Python.

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. Codon grew out of the Seq project, which was a research project at the University of California, Berkeley.

How Does Codon Work?

Codon works by taking your Python code and compiling it to native machine code. It achieves this by analyzing your code and generating LLVM IR (Intermediate Representation), which is a low-level programming language that can be optimized and compiled to native machine code. The LLVM IR is then optimized to produce fast and efficient machine code.

Installing Codon

Pre-built binaries for Linux (x86_64) and macOS (x86_64 and arm64) are available alongside each release. You can download and install Codon with the following command:

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

Alternatively, you can build Codon from source. For more information on how to do this, refer to the official documentation.

Examples

Codon is a Python-compatible language, and many Python programs will work with few if any modifications. For example, the following code that generates a Fibonacci sequence:

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 that can be used to compile and run the program:

# 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

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:

#include <codon.hpp>
#include <vector>
#include <cmath>

using namespace codon;

// Count the number of primes less than n
int count_primes(int n) {
    std::vector<bool> sieve(n, true);
    int count = 0;

    #pragma omp parallel for schedule(dynamic, 100) num_threads(16)
    for (int i = 2; i < n; ++i) {
        if (sieve[i]) {
            ++count;
            for (int j = i*2; j < n; j += i) {
                sieve[j] = false;
            }
        }
    }

    return count;
}

int main() {
    int n = 10000000;
    int num_primes = count_primes(n);
    printf("Number of primes less than %d: %d\n", n, num_primes);
    return 0;
}

This code uses OpenMP to parallelize the for-loop that counts the number of primes less than 'n'. The 'schedule(dynamic, 100)' clause specifies a dynamic scheduling policy with a chunk size of 100, which means that the loop iterations are divided into chunks of 100 and assigned to threads as they become available. The 'num_threads(16)' clause specifies that the loop should be executed by 16 threads.

By parallelizing the loop, the program can take advantage of multi-core processors and compute the prime count much faster than a serial implementation. However, it's important to note that not all algorithms can be easily parallelized, and it's important to consider the potential benefits and tradeoffs before adding parallelization to a program.