Instructions to use cahlen/ramsey-r55-cuda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use cahlen/ramsey-r55-cuda with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("cahlen/ramsey-r55-cuda") - Notebooks
- Google Colab
- Kaggle
Ramsey R(5,5) Simulated Annealing Search
Searches for 2-colorings of K_n with no monochromatic K_5 using simulated annealing with incremental K_5 counting.
Usage
import torch
from kernels import get_kernel
kernel = get_kernel("cahlen/ramsey-r55-cuda")
result = ramsey.search(n=43, num_walkers=50000, max_steps=5000000)
Compile (standalone)
nvcc -O3 -arch=sm_90 -o ramsey_r55 ramsey/ramsey_incremental_v2.cu -lm
Results
All computation results are open:
- Website: bigcompute.science
- Datasets: huggingface.co/cahlen
- Source: github.com/cahlen/idontknow
Citation
@misc{humphreys2026bigcompute,
author = {Humphreys, Cahlen},
title = {bigcompute.science: GPU-Accelerated Computational Mathematics},
year = {2026},
url = {https://bigcompute.science}
}
Human-AI collaborative. Not peer-reviewed. All code and data open.
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