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:

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):

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 ../contributing.

Key areas needing work:

CUDA kernel templates
Memory management (host/device transfers)
Spatial data structures on GPU
Testing infrastructure