A visualization-driven project that grew into a high-throughput GPU/CPU pipeline for searching long Cunningham chains (first-kind). More about HPC optimization and AI-assisted iteration than the math itself.
As of March 2026, this project has contributed:
The original target (first-kind CC18 or CC19) has not yet been reached. This was compute-limited.
The campaign dataset lives in a separate repo: cunningham-chain-data. Summary statistics and analysis CSVs are in data/ here; the full raw dataset (~30 MB, 929K CC10+ roots including 44 CC16 and a CC17) is available as a release download.
| What | Where |
|---|---|
| GPU filter engine (CUDA) | src/cuda/ |
| CPU search engine (GMP) | src/cpu/ |
| GP/PARI library + MCP server | gp/ |
| Interactive visualizations | visualizations/ |
| Analysis notes | analysis/notes/ |
| Analysis scripts | analysis/scripts/ |
| Campaign dataset | cunningham-chain-data (separate repo) |
| Analysis CSVs | data/ |
| Failed experiments (17 approaches) | experiments/failed/ |
| Repo map | docs/REPO_MAP.md |
| Search pipeline overview | docs/SEARCH_PIPELINE.md |
Two-phase GPU+CPU pipeline:
See docs/SEARCH_PIPELINE.md for details.
Standalone HTML/JS tools that shaped the search design. Try them live on GitHub Pages:
# GPU engine (requires CUDA toolkit + GMP)
# Adjust -arch=sm_XX for your GPU / CUDA toolchain
# Example: Ada may use sm_89; some setups may still require sm_86
nvcc -O3 -arch=sm_89 src/cuda/cc18_filter_cuda_CpC_v13.cu -o cc18_filter -lgmp -lpthread
# CPU engine (requires GMP)
gcc -O3 -march=native -flto src/cpu/cc_gmp_v33_03.c -o cc_search -lgmp -lpthread -lm
# Run tests
./cc_search --test
gp -q
\r gp/cc_lib_v10.gp
Self-tests (37) run automatically on load.
See gp/HOWTO_cc_lib_v10.md for usage.
Nenad Micic, Belgium — LinkedIn
See LICENSE.