slimcuda 0.7.4

GPU-accelerated backend for large-scale hologram generation and SLM wavefront synthesis.

SLiM-CUDA

SLiM-CUDA is a GPU-accelerated backend for large-scale hologram generation and wavefront synthesis, designed for high-performance spatial light modulator (SLM) workflows.

This PyPI distribution provides a public, compatibility-first build intended for correctness, reproducibility, and integration. Optimized GPU-specific kernels are available separately for collaborators.

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Features

• CUDA-accelerated weighted Gerchberg–Saxton (WGS)–style solvers
• Designed for large multi-focus hologram synthesis
• CuPy-based runtime integration
• Drop-in upgrade path for optimized kernels (no API changes)

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Installation

pip install slimcuda

This installs:

• PTX kernels compiled for broad GPU compatibility
• Corresponding CUDA source files for transparency and inspection
• Python-side orchestration and utilities

Kernel Architecture & Performance Model

Public PyPI build (default)

The PyPI wheel ships with:

• slimcuda_og.ptx
• Corresponding CUDA source (.cu, .cuh) files

This build prioritizes:

• Broad GPU compatibility
• Reproducibility
• Ease of installation

⚠️ Performance note

The PTX kernels are not performance-optimized for modern GPUs. They exist to ensure correctness and portability.

Optimized builds (collaborators)

Highly optimized, GPU-specific kernels are distributed as fatbin / cubin binaries and are not included in the public wheel.

If an optimized kernel is present locally, SLiM-CUDA will automatically detect and load it.

Benefits:

• Substantially higher throughput
• Reduced launch overhead
• Architecture-specific tuning

If you are a collaborator or have a supported GPU and need optimized kernels, please contact the author.

Runtime Banner

When running with the public PTX kernels, SLiM-CUDA displays a short informational banner indicating that an optimized build exists.

This is informational only and can be disabled:

# Linux / macOS
export SLIMCUDA_BANNER=0

# Windows (PowerShell)
setx SLIMCUDA_BANNER 0

or programmatically via the loader API.

GPU Compatibility

• Public PTX kernels: should run on most CUDA-capable GPUs
• Optimized kernels: GPU- and build-specific

If you have an optimized kernel but encounter issues on your GPU, please contact the author for a tailored build.

License

• Python code: MIT License
• Public CUDA source (PTX / .cu): MIT License
• Optimized CUDA binaries: distributed separately under collaborator-specific terms

Citation

If you use SLiM-CUDA in academic work, please cite the following:

Primary citation (recommended)

SLiM-CUDA was originally developed to support the methodology described in:

Z. Qu et al., Deep-learning-aided multi-focal hologram generation, Optics & Laser Technology, 2025. DOI: 10.1016/j.optlastec.2024.112056

@article{jwangSlimCuda,
  title   = {Deep-learning-aided multi-focal hologram generation},
  author  = {Qu, Z. and others},
  journal = {Optics & Laser Technology},
  year    = {2025},
  doi     = {10.1016/j.optlastec.2024.112056}
}

If your work builds upon or uses the algorithms and concepts enabled by SLiM-CUDA,
please cite this publication.

Software citation

If you prefer to cite the software directly (e.g. for tooling or infrastructure use), you may cite:

SLiM-CUDA: GPU-accelerated hologram generation backend.
https://pypi.org/project/slimcuda/

A formal software citation entry (BibTeX) will be provided in a future release.

Disclaimer

This software is intended for research and advanced technical use.

API stability is maintained, but internal kernel implementations may evolve.