fbpic VS codon

Compare fbpic vs codon and see what are their differences.

fbpic

Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU (by fbpic)

codon

A high-performance, zero-overhead, extensible Python compiler using LLVM (by exaloop)
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fbpic codon
2 34
165 13,851
-0.6% 0.5%
8.1 7.9
8 days ago 7 days ago
Python C++
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

fbpic

Posts with mentions or reviews of fbpic. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-27.
  • Numba: A High Performance Python Compiler
    11 projects | news.ycombinator.com | 27 Dec 2022
    When I wrote my bachelor thesis years back I worked on a particle-in-cell code [1] that makes heavy use of numba for GPU kernels. At the time it was the most convenient way to do that from python. I remember spending weeks to optimizing these kernels to eek out every last bit of performance I could (which interestingly enough did eventually involve using atomic operations and introducing a lot of variables[2] instead of using arrays everywhere to keep things in registers instead of slower caches).

    I remember the team being really responsive to feature requests back then and I had a lot of fun working with it. IIRC compared to using numpy we managed to get speedups of up to 60x for the most critical pieces of code.

    [1]: https://github.com/fbpic/fbpic

  • Faster Python calculations with Numba: 2 lines of code, 13× speed-up
    5 projects | news.ycombinator.com | 18 Feb 2022
    We used numba to accelerate the code and most importantly write GPU kernels for the heavy parts. I remember spending hours optimising my code to eek out the most performance possible (which eventually meant using atomics and manually unrolling many loops because somehow this was giving us the best performance) but honestly I was really happy that I didn't need to write cuda kernels in C and generally it was pretty easy to work with. I remember back then the documentation was sometimes a little rough around the edges but the numba team was incredibly helpful and responsive. Overall I had a great time.

    [0] https://github.com/fbpic/fbpic

codon

Posts with mentions or reviews of codon. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-22.

What are some alternatives?

When comparing fbpic and codon you can also consider the following projects:

WarpX - WarpX is an advanced, time-based electromagnetic & electrostatic Particle-In-Cell code.

Nuitka - Nuitka is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.

simsopt - Simons Stellarator Optimizer Code

Numba - NumPy aware dynamic Python compiler using LLVM

pure_numba_alias_sampling - Pure numba version of Alias sampling algorithm from L. Devroye's, "Non-Uniform Random Random Variate Generation"

Cython - The most widely used Python to C compiler

autograd - Efficiently computes derivatives of numpy code.

taichi - Productive, portable, and performant GPU programming in Python.

ndarray_comparison - Benchmark of toy calculation on an n-dimensional array using python, numba, cython, pythran and rust

julia - The Julia Programming Language

rust-numpy - PyO3-based Rust bindings of the NumPy C-API

Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).