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Top 14 Python gpu-computing Projects
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catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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prysm
physical optics: integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing... (by brandondube)
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entangle
A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs.
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pla-reverse-gui
PySide6-based GUI for Seed Cracking and RNG w/o CFW assistance in Pokemon: Legends Arceus
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Project mention: CatBoost: Open-source gradient boosting library | news.ycombinator.com | 2024-03-05
Project mention: tf-quant-finance: NEW Derivatives and Hedging - star count:3911.0 | /r/algoprojects | 2023-06-10
Project mention: Can't install Luxcore render in Blender on Macbook M1 | /r/blenderhelp | 2023-06-03
Project mention: Show HN: Elodin – A better framework for physics simulation | news.ycombinator.com | 2024-03-06You are completely correct; right now it is just mechanics that we have built out. But, there isn't any theoretical reason you couldn't use this framework for other types of simulation. In particular, the Monte Carlo runner is super flexible. Since we are based on JAX you can utilize a ton of the tooling that others have built in the physics space like https://github.com/tumaer/JAXFLUIDS or https://github.com/DifferentiableUniverseInitiative/jax_cosm... . The goal right now though is pretty firmly focused on controls engineers and their needs, but we envision this becoming broadly used.
Project mention: Ask HN: What apps have you created for your own use? | news.ycombinator.com | 2023-12-12I've created Kernel Tuner (https://github.com/KernelTuner/kernel_tuner) as a small software development tool, because I was writing a lot of CUDA and OpenCL kernels at the time. I didn't want to manually figure out what best thread block dimensions and work division among threads were on every GPU over and over again.
The tool evolved quite a bit since the first versions. I'm also using it for testing GPU code, teaching, and it has become one of the main drivers behind a lot of the research that I do.
My current concept is to just combine zernike polynomials with a random factor and calculate the PSF from that, which can be somewhat easily be done with the prysm library. These PSFs can then be convolved with circular and gaussian kernels for modelling additional defocus and accounting for other stuff like the AA filter. Then I'd add chromatic aberration by offseting/scaling the PSFs for each channel. Some generated kernels already look pretty good when comparing them to stars in astrophotography images, but others not so much.
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Index
What are some of the best open-source gpu-computing projects in Python? This list will help you:
Project | Stars | |
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1 | catboost | 7,744 |
2 | tf-quant-finance | 4,283 |
3 | lingvo | 2,780 |
4 | PyCUDA | 1,746 |
5 | bindsnet | 1,428 |
6 | BlendLuxCore | 693 |
7 | JAXFLUIDS | 245 |
8 | kernel_tuner | 243 |
9 | prysm | 234 |
10 | gpu-utils | 130 |
11 | entangle | 105 |
12 | gpucsl | 38 |
13 | tensorexperiments | 36 |
14 | pla-reverse-gui | 14 |
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