gpuicalcc

GPU-accelerated locally centered contrast functions for FastICA. PyTorch extension of icalcc. Same API, drop-in replacement with CUDA acceleration for bounded and polynomial LCC contrasts.

from gpuicalcc import GPUICALCC
ica = GPUICALCC(n_components=4, K='ltanh', device='cuda', random_state=0)
S_hat = ica.fit_transform(X)

Installation

pip install gpuicalcc

Requires PyTorch with CUDA. See pytorch.org for installation instructions.

Supported K Values

Bounded contrasts ('ltanh', 'lexp') achieve 40–48× speedup across all dataset sizes due to the O(NB) pairwise computation being embarrassingly parallel. Polynomial contrasts benefit from GPU acceleration at N ≥ 500,000. Classical contrasts fall back to the CPU implementation in icalcc.

Usage

from gpuicalcc import GPUICALCC

# Bounded LCC-tanh (recommended for heavy-tailed or skewed sources)
ica = GPUICALCC(n_components=4, K='ltanh', device='cuda', random_state=0)
S_hat = ica.fit_transform(X)

# Bounded LCC-tanh with memory limit
ica = GPUICALCC(n_components=4, K='ltanh', device='cuda',
                batch_size=500, gpu_mem_limit=8, random_state=0)
S_hat = ica.fit_transform(X)

# Polynomial LCC order 8 (near-Gaussian sources)
ica = GPUICALCC(n_components=4, K=8, device='cuda', random_state=0)
S_hat = ica.fit_transform(X)

# Falls back to CPU automatically if CUDA unavailable
ica = GPUICALCC(n_components=4, K='ltanh', device='cuda', random_state=0)

Parameters

Benchmark

Bounded contrasts (ltanh, lexp) achieve 40–48× speedup across all dataset sizes. Polynomial contrasts (K=4,6,8) benefit from GPU acceleration at N ≥ 500,000, reaching up to 2.4× at K=8. All runs use batch_size=500. Full benchmark table: Experiment code.

Requirements

• Python ≥ 3.9
• numpy ≥ 1.24
• scikit-learn ≥ 1.3
• icalcc ≥ 0.1.0
• torch ≥ 2.0

See Also

• icalcc — CPU version
• Experiment code

Citation

If you use this package, please cite both the software paper and the underlying LCC kernel paper:

@article{saito2026icalcc,
  author  = {Saito, Tetsuya},
  title   = {{ICALCC}: Locally Centered Contrast Functions for
             {FastICA} with {GPU} Acceleration},
  journal = {TechRxiv},
  year    = {2026}
}

@article{saito2026lcc,
  author  = {Saito, Tetsuya},
  title   = {Locally Centered Cyclic Kernels for Higher-Order
             Independent Component Analysis},
  journal = {TechRxiv},
  year    = {2026}
}

License

CC BY 4.0