cahlen/zaremba-conjecture-data
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How to use cahlen/zaremba-transfer-operator-cuda with Kernels:
# !pip install kernels
from kernels import get_kernel
kernel = get_kernel("cahlen/zaremba-transfer-operator-cuda")Computes spectral gaps of the transfer operator for Zaremba generators {1,...,5} using implicit Kronecker product + power iteration.
Note: The
kernelsPython binding for this repo is a stub only. The underlying CUDA kernel uses cuBLAS, pthreads, and multi-GPU orchestration that has not yet been refactored into a single torch-callable function. For now, use the standalone compiled binary (see below). Fullget_kernel()support is planned.
nvcc -O3 -arch=sm_120 -o transfer_operator transfer_operator/transfer_operator.cu -lcublas -lcusolver -lm -lpthread
./transfer_operator [N] [phase] [max_m] [min_m]
# N: Chebyshev polynomial order (default 40)
# phase: 1=Hausdorff only, 2=congruence gaps only, 3=both (default 3)
# max_m: maximum modulus (default 100)
# min_m: minimum modulus (default 2)
All computation results are open:
@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 Not peer-reviewed.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.