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---
language:
- en
license: mit
tags:
- cuda
- gpu
- number-theory
- computational-mathematics
- continued-fractions
- zaremba
- ramsey
- kronecker-coefficients
- class-numbers
- hausdorff-dimension
- ramanujan-machine
- erdos-straus
- prime-convergents
- flint-hills
- spectral-methods
- bigcompute
library_name: other
pipeline_tag: other
datasets:
- cahlen/zaremba-density
- cahlen/zaremba-conjecture-data
- cahlen/class-numbers-real-quadratic
- cahlen/kronecker-coefficients
- cahlen/hausdorff-dimension-spectrum
- cahlen/continued-fraction-spectra
- cahlen/ramanujan-machine-results
---
# bigcompute.science CUDA Kernels
51 custom CUDA kernels for GPU-accelerated computational mathematics research. These kernels power the experiments at [bigcompute.science](https://bigcompute.science).
All kernels are standalone β€” compile with `nvcc`, run from the command line. No PyTorch dependency.
## Hardware
Developed and tested on:
- **8x NVIDIA B200** (183 GB VRAM each, sm_90)
- **NVIDIA RTX 5090** (32 GB VRAM, sm_120)
Most kernels will run on any CUDA GPU (sm_50+). Compile with your target architecture:
```bash
nvcc -O3 -arch=sm_XX -o kernel kernel.cu -lm
```
## Kernels by Experiment
### Zaremba's Conjecture (25 kernels)
**Density enumeration** (`zaremba-density/`) β€” complete CF tree enumeration with bitset marking:
- `zaremba_density_gpu.cu` β€” production kernel, 65+ runs to 10^12
- `zaremba_density_v2.cu` β€” alternative implementation
- `zaremba_density_gpu_worksteal_v2.cu` β€” work-stealing variant for load balancing
**Transfer operator** (`zaremba-transfer-operator/`) β€” Chebyshev collocation spectral method:
- `transfer_operator.cu` β€” spectral gap computation for Ruelle operator
**Effective bound** (`zaremba-effective-bound/`) β€” Bourgain-Kontorovich proof framework:
- `spectral_gaps_fast.cu` β€” bulk spectral gap verification
- `spectral_gaps_primes.cu` β€” prime-indexed gaps
- `certify_rho_cuda.cu` β€” arb ball arithmetic certification
- `compute_Q0.cu` / `Q0_frolenkov_kan.cu` β€” effective constant extraction
- `count_representations.cu` β€” CF representation counting
- `dolgopyat_exact.cu` / `dolgopyat_profile.cu` β€” Dolgopyat estimate profiling
- `exponential_sum.cu` β€” exponential sum bounds
- `extract_eigenfunction.cu` β€” transfer operator eigenfunction extraction
- `flat_spectral_gap.cu` β€” uniform spectral gap verification
- `matrix_enum.cu` / `matrix_enum_multipass.cu` β€” SL(2,Z) matrix enumeration
- `minor_arc_primes.cu` / `minor_arc_profile.cu` β€” minor arc estimates
- `verify_all_gaps_fp64.cu` / `verify_gaps_interval.cu` / `verify_gaps_v2.cu` β€” gap verification suite
- `compute_c1_rigorous.cu` β€” rigorous constant computation
**Cayley diameters** (`zaremba-cayley-diameter/`) β€” BFS on Cayley graphs of SL(2,Z/pZ):
- `cayley_diameter.cu` / `cayley_gpu.cu` β€” full BFS diameter computation
**Transitivity** (`zaremba-transitivity/`) β€” algebraic verification:
- `check_transitivity.cu` β€” Dickson classification check
### Ramsey R(5,5) (7 kernels)
`ramsey-r55/` β€” search for 2-colorings of complete graphs with no monochromatic K5:
- `ramsey_gpu.cu` β€” base simulated annealing kernel
- `ramsey_incremental.cu` / `ramsey_incremental_v2.cu` β€” incremental K5 counter
- `ramsey_extend.cu` / `ramsey_extend_all.cu` β€” exhaustive extension checking (4.4T extensions of K42 to K43)
- `ramsey_fullcount.cu` β€” complete clique enumeration
- `ramsey_search.cu` / `ramsey_global.cu` / `ramsey_verified.cu` β€” search variants
### Class Numbers (4 kernels)
`class-numbers/` β€” class numbers of real quadratic fields via BSGS:
- `class_numbers_v2.cu` β€” production kernel (10^9 to 10^12 range)
- `class_number_rqf.cu` β€” real quadratic field specialization
- `class_number_fast.cu` β€” optimized inner loop
- `sieve_gpu.cu` β€” GPU prime sieve
### Kronecker Coefficients (3 kernels)
`kronecker-coefficients/` β€” character tables and Kronecker triple computation:
- `kronecker_gpu.cu` β€” full character table (S20: 3.7s, S30: 7.4 min, S40: 9.5 hr)
- `kronecker_fast.cu` β€” optimized triple-sum
- `kronecker_compute.cu` β€” targeted triple computation
### Ramanujan Machine (2 kernels)
`ramanujan-machine/` β€” automated discovery of continued fraction formulas:
- `ramanujan_gpu.cu` β€” v1 kernel (equal-degree polynomials, exhausted)
- `ramanujan_v2.cu` β€” v2 kernel (asymmetric-degree, where new discoveries live)
### Prime Convergents (2 kernels)
`prime-convergents/` β€” prime statistics of CF convergents:
- `prime_convergents.cu` β€” v1 (uint64, depth ~38)
- `prime_convergents_v2.cu` β€” v2 (uint128, depth ~75, 128-bit Miller-Rabin)
### Erdos-Straus Conjecture (1 kernel)
`erdos-straus/` β€” solution counting for 4/p = 1/x + 1/y + 1/z:
- `erdos_straus.cu` β€” per-prime f(p) enumeration, tested to 10^9
### Spectral Computations (4 kernels)
`hausdorff-spectrum/` β€” Hausdorff dimension via transfer operator + Chebyshev collocation:
- `hausdorff_spectrum.cu` β€” all 2^20 - 1 subsets of {1,...,20}
`lyapunov-spectrum/` β€” Lyapunov exponents of CF digit sets:
- `lyapunov_spectrum.cu` β€” full spectrum computation
`minkowski-spectrum/` β€” Minkowski question-mark function:
- `minkowski_spectrum.cu` β€” singularity spectrum
`flint-hills/` β€” Flint Hills series partial sums:
- `flint_hills.cu` β€” high-precision partial sum to 10B terms
## Results
All computation results are open:
- **Website**: [bigcompute.science](https://bigcompute.science)
- **Datasets**: [huggingface.co/cahlen](https://huggingface.co/cahlen)
- **Source code**: [github.com/cahlen/idontknow](https://github.com/cahlen/idontknow)
- **MCP server**: [mcp.bigcompute.science](https://mcp.bigcompute.science)
## License
MIT
## Citation
```bibtex
@misc{humphreys2026bigcompute,
author = {Humphreys, Cahlen},
title = {bigcompute.science: GPU-Accelerated Computational Mathematics},
year = {2026},
url = {https://bigcompute.science}
}
```
*Human-AI collaborative research (Cahlen Humphreys + Claude). All code and data open for verification.*