CUDA.jl
awesome-quant
CUDA.jl | awesome-quant | |
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15 | 18 | |
1,133 | 16,056 | |
1.1% | - | |
9.5 | 8.8 | |
7 days ago | 1 day ago | |
Julia | Python | |
GNU General Public License v3.0 or later | - |
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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/
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Yann Lecun: ML would have advanced if other lang had been adopted versus Python
If you look at Julia open source projects you'll see that the projects tend to have a lot more contributors than the Python counterparts, even over smaller time periods. A package for defining statistical distributions has had 202 contributors (https://github.com/JuliaStats/Distributions.jl), etc. Julia Base even has had over 1,300 contributors (https://github.com/JuliaLang/julia) which is quite a lot for a core language, and that's mostly because the majority of the core is in Julia itself.
This is one of the things that was noted quite a bit at this SIAM CSE conference, that Julia development tends to have a lot more code reuse than other ecosystems like Python. For example, the various machine learning libraries like Flux.jl and Lux.jl share a lot of layer intrinsics in NNlib.jl (https://github.com/FluxML/NNlib.jl), the same GPU libraries (https://github.com/JuliaGPU/CUDA.jl), the same automatic differentiation library (https://github.com/FluxML/Zygote.jl), and of course the same JIT compiler (Julia itself). These two libraries are far enough apart that people say "Flux is to PyTorch as Lux is to JAX/flax", but while in the Python world those share almost 0 code or implementation, in the Julia world they share >90% of the core internals but have different higher levels APIs.
If one hasn't participated in this space it's a bit hard to fathom how much code reuse goes on and how that is influenced by the design of multiple dispatch. This is one of the reasons there is so much cohesion in the community since it doesn't matter if one person is an ecologist and the other is a financial engineer, you may both be contributing to the same library like Distances.jl just adding a distance function which is then used in thousands of places. With the Python ecosystem you tend to have a lot more "megapackages", PyTorch, SciPy, etc. where the barrier to entry is generally a lot higher (and sometimes requires handling the build systems, fun times). But in the Julia ecosystem you have a lot of core development happening in "small" but central libraries, like Distances.jl or Distributions.jl, which are simple enough for an undergrad to get productive in a week but is then used everywhere (Distributions.jl for example is used in every statistics package, and definitions of prior distributions for Turing.jl's probabilistic programming language, etc.).
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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?
LoopVectorization.jl - Macro(s) for vectorizing loops.
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.
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
backtrader - Python Backtesting library for trading strategies
cudf - cuDF - GPU DataFrame Library
uniswap-sushiswap-arbitrage-bot - Two bots written in JS that uses flashswaps and normal swaps to arbitrage Uniswap. Includes an automated demostration.
Tullio.jl - ⅀
awesome-discord-communities - A curated list of awesome Discord communities for programmers
GPUCompiler.jl - Reusable compiler infrastructure for Julia GPU backends.
Gekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools.
CudaPy - CudaPy is a runtime library that lets Python programmers access NVIDIA's CUDA parallel computation API.
WorldQuant_alpha101_code - Code implementation of the Quantigic 101 Formulaic Alphas