In this image, an individual is giving a detailed technical presentation to a small audience in a comfortable, well-lit room. The presentation, titled "Scaling Democracy with GPUs & Algebraic Graph Theory," is projected on a screen at the front. It discusses utilizing CUDA for hardware acceleration to implement the Schulze voting algorithm, includes repository instructions, and highlights Python dependencies and benchmarks. The attendees appear engaged, with one person capturing the information on a laptop. The setting is casual, reflected by the informal seating arrangement and the overall relaxed atmosphere. The outdoor greenery visible through the window adds a pleasant backdrop to the technical discourse.
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github.com/ashvardanian/scaling-democracy/tree/main
README sta Apache-2.0 license
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multiple Python versions using
Anaconda for package management.
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Scaling Democracy
With GPUs & Algebraic Graph Theory
This repository implements the Schulze voting algorithm using CUDA for
hardware acceleration. It's built as a single scaling_democracy.cu CUDA file,
wrapped with PyBind11, and compiled without CMake directly from the
setup.py. To pull, build and benchmark locally:
git clone https://github.com/ashvardanian/scaling-democracy.g
cd scaling-democracy
git submodule update --init --recursive
pip install pybind11 numpy numba cupy-cuda12x
pip install -e.
python benchmark.py
It's a fun project by itself. It might be a good example of using GPUs for
combinatorial problems with order-dependant traversal and also a good
starting point for single-day CUDA hacking experiences with the header-only
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