CUDA.jl
awesome-quant
| CUDA.jl | awesome-quant | |
|---|---|---|
| 15 | 19 | |
| 1,266 | 19,701 | |
| 2.5% | 3.5% | |
| 9.5 | 7.1 | |
| 3 days ago | 4 days ago | |
| Julia | Python | |
| GNU General Public License v3.0 or later | - |
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.
CUDA.jl
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Ask HN: Best way to learn GPU programming?
It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.
https://juliagpu.org/
I've written CUDA kernels and I knew nothing about it going in.
- What's your main programming language?
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How is Julia Performance with GPUs (for LLMs)?
See https://juliagpu.org/
- Yann Lecun: ML would have advanced if other lang had been adopted versus Python
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C++ is making me depressed / CUDA question
If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.
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Parallélisation distribuée presque triviale d’applications GPU et CPU basées sur des Stencils avec…
GitHub - JuliaGPU/CUDA.jl: CUDA programming in Julia.
- Why Fortran is easy to learn
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Generic GPU Kernels
Should have (2017) in the title.
Indeed cool to program julia directly on the GPU and Julia on GPU and this has further evolved since then, see https://juliagpu.org/
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Announcing The Rust CUDA Project; An ecosystem of crates and tools for writing and executing extremely fast GPU code fully in Rust
I'm excited to eventually see something like JuliaGPU with support for multiple backends.
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[Media] 100% Rust path tracer running on CPU, GPU (CUDA), and OptiX (for denoising) using one of my upcoming projects. There is no C/C++ code at all, the program shares a single rust crate for the core raytracer and uses rust for the viewer and renderer.
That's really cool! Have you looked at CUDA.jl for the Julia language? Maybe you could take some ideas from there. I am pretty sure it does the same thing you do here, and they support any arbitrary code with the limitations that you cannot allocate memory, I/O is disallowed, and badly-typed code(dynamic) will not compile.
awesome-quant
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RustQuant: A Library for Quantitative Finance
No, it looks more like a Rust equivalent of libraries like ffn (financial functions for python) or many of the other ones listed here https://github.com/wilsonfreitas/awesome-quant
Using rust to do exploratory analysis in python seems like a misguided idea. But using rust to productize models that have performance and accuracy sensitivities, the things that C/C++ is still used for, indeed sounds like a good idea.
Most of the python libraries used in finance, like numpy/pandas, call out to C for performance reasons; the libraries are essentially python bindings + syntax to C functions. It would be interesting to think about replacing that backend with rust.
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Open Source Projects
This is a good list https://github.com/wilsonfreitas/awesome-quant
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I’m not a Quant, but a Headhunter - ask me anything
also, what are the best quanty python packages that you like to see an applicant use? there are so many. https://github.com/wilsonfreitas/awesome-quant
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Why building profitable trading bot is hard?
If the financial analyst does not have a (possibly piecewise) software function to at least test with backtesting and paper trading, do they even have an objective relative performance statistic? Your notebook or better should also model fees and have a parametrizable initial balance.
Here's the awesome-quant link directory: https://github.com/wilsonfreitas/awesome-quant
- For Traders Who Want To Be Quants
- A curated list of libraries, packages and resources for Quants
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Hacker News top posts: Feb 22, 2022
A curated list of libraries, packages and resources for Quants\ (0 comments)
What are some alternatives?
cupynumeric - An Aspiring Drop-In Replacement for NumPy at Scale
qlib - Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
backtrader - Python Backtesting library for trading strategies
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
akshare - AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库 [Moved to: https://github.com/akfamily/akshare]