CUDA Code Generation
====================

.. note::

   CUDA support is **planned for a future release**. This page documents
   the intended API and capabilities.

Overview
--------

GPU acceleration via CUDA will enable massive parallelization for:

- Large N-body simulations (thousands of bodies)
- High-resolution SPH fluids (millions of particles)
- Parameter sweeps and ensemble simulations
- Real-time interactive simulations

Planned Features
----------------

**Particle-based systems**:

- Parallel force computation
- Spatial hashing on GPU
- Shared memory optimizations

**N-body gravity**:

- Barnes-Hut tree on GPU
- O(N log N) instead of O(N²)

**SPH fluids**:

- All-pairs neighbor search
- Compact neighbor lists
- Pressure solve on GPU

Intended API
------------

The planned API will mirror C++ code generation:

.. code-block:: python

   from mechanics_dsl import PhysicsCompiler
   
   compiler = PhysicsCompiler()
   compiler.compile_dsl(n_body_source)
   
   # Generate CUDA code
   compiler.compile_to_cuda("n_body.cu")
   
   # Compile to executable (requires nvcc)
   compiler.compile_to_cuda("n_body.cu", compile_binary=True)

Expected Performance
--------------------

Preliminary benchmarks (estimated):

.. list-table::
   :header-rows: 1

   * - System
     - CPU (C++)
     - GPU (CUDA)
     - Speedup
   * - N-body (1000)
     - 5 s
     - 0.1 s
     - 50x
   * - N-body (10000)
     - 500 s
     - 2 s
     - 250x
   * - SPH (100k particles)
     - 600 s
     - 10 s
     - 60x

Requirements
------------

When available, CUDA generation will require:

- NVIDIA GPU (Compute Capability 5.0+)
- CUDA Toolkit 11.0+
- cuBLAS (optional, for linear algebra)

Contributing
------------

Interested in helping implement CUDA support? See :doc:`../contributing`.

Key areas needing work:

1. CUDA kernel templates
2. Memory management (host/device transfers)
3. Spatial data structures on GPU
4. Testing infrastructure