# CUDA-Rust-WASM Architecture Design
## Overview
This document outlines the architecture for translating CUDA code to Rust with WebGPU/WASM support. The system provides a runtime environment that maps CUDA concepts to Rust equivalents while maintaining performance and compatibility.
## System Architecture
```
┌─────────────────────────────────────────────────────────────────┐
│ CUDA Source Code │
└─────────────────────────────────────┬───────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ CUDA Parser & AST │
│ • PTX/CUDA C++ parsing │
│ • Kernel extraction │
│ • Memory layout analysis │
└─────────────────────────────────────┬───────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Transpiler Core │
│ ┌─────────────┐ ┌─────────────┐ ┌────────────────┐ │
│ │ Kernel │ │ Memory │ │ Runtime │ │
│ │ Translator │ │ Analyzer │ │ Generator │ │
│ └─────────────┘ └─────────────┘ └────────────────┘ │
└─────────────────────────────────────┬───────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ CUDA-Rust Runtime │
│ ┌─────────────┐ ┌─────────────┐ ┌────────────────┐ │
│ │ Memory │ │ Kernel │ │ Device │ │
│ │ Management │ │ Execution │ │ Abstraction │ │
│ └─────────────┘ └─────────────┘ └────────────────┘ │
└─────────────────────────────────────┬───────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Backend Targets │
│ ┌─────────────┐ ┌─────────────┐ ┌────────────────┐ │
│ │ Native │ │ WebGPU │ │ WASM │ │
│ │ GPU │ │ Backend │ │ Runtime │ │
│ └─────────────┘ └─────────────┘ └────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
```
## Core Components
### 1. Parser Module (`parser/`)
- **Purpose**: Parse CUDA source code and generate AST
- **Components**:
- `cuda_parser.rs`: CUDA C++ syntax parser
- `ptx_parser.rs`: PTX assembly parser
- `ast.rs`: Abstract syntax tree definitions
- `kernel_extractor.rs`: Extract kernel functions
### 2. Transpiler Module (`transpiler/`)
- **Purpose**: Convert CUDA AST to Rust code
- **Components**:
- `kernel_translator.rs`: Translate CUDA kernels to Rust
- `memory_mapper.rs`: Map CUDA memory operations
- `type_converter.rs`: Convert CUDA types to Rust
- `builtin_functions.rs`: Map CUDA built-ins
### 3. Runtime Module (`runtime/`)
- **Purpose**: Provide CUDA-compatible runtime in Rust
- **Components**:
- `device.rs`: Device management and abstraction
- `memory.rs`: Memory allocation and transfers
- `kernel.rs`: Kernel launch and execution
- `stream.rs`: Asynchronous execution streams
- `event.rs`: Synchronization primitives
### 4. Memory Management (`memory/`)
- **Purpose**: Handle CUDA memory patterns in Rust
- **Components**:
- `device_memory.rs`: GPU memory allocation
- `host_memory.rs`: CPU memory management
- `unified_memory.rs`: Unified memory abstraction
- `memory_pool.rs`: Memory pooling for performance
### 5. Kernel Execution (`kernel/`)
- **Purpose**: Execute translated kernels
- **Components**:
- `grid.rs`: Grid and block dimensions
- `thread.rs`: Thread indexing and synchronization
- `shared_memory.rs`: Shared memory management
- `warp.rs`: Warp-level primitives
### 6. Backend Abstraction (`backend/`)
- **Purpose**: Abstract different execution backends
- **Components**:
- `backend_trait.rs`: Common backend interface
- `native_gpu.rs`: Native GPU execution (CUDA/ROCm)
- `webgpu.rs`: WebGPU backend implementation
- `wasm_runtime.rs`: WASM execution environment
## CUDA to Rust Mapping
### Type Mappings
```rust
// CUDA Types → Rust Types
float → f32
double → f64
int → i32
long long → i64
char → i8
short → i16
dim3 → struct Dim3 { x: u32, y: u32, z: u32 }
float4 → struct Float4 { x: f32, y: f32, z: f32, w: f32 }
```
### Memory Operations
```rust
// CUDA → Rust Runtime
cudaMalloc() → device::allocate()
cudaMemcpy() → memory::copy()
cudaFree() → device::free()
__shared__ → SharedMemory<T>
__device__ → #[device_function]
__global__ → #[kernel]
```
### Kernel Execution
```rust
// CUDA kernel launch
kernel<<<grid, block>>>(args);
// Rust equivalent
runtime::launch_kernel(
kernel_fn,
Grid::new(grid),
Block::new(block),
args
);
```
### Thread Indexing
```rust
// CUDA → Rust
threadIdx.x → thread::index().x
blockIdx.x → block::index().x
blockDim.x → block::dim().x
gridDim.x → grid::dim().x
```
## WebGPU Integration Strategy
### 1. Shader Translation
- Convert CUDA kernels to WGSL (WebGPU Shading Language)
- Map CUDA compute patterns to WebGPU compute shaders
- Handle workgroup sizes and shared memory
### 2. Memory Model
- Map CUDA global memory to WebGPU storage buffers
- Convert shared memory to workgroup memory
- Implement texture memory using WebGPU textures
### 3. Execution Model
- Convert CUDA grid/block to WebGPU workgroups
- Map thread synchronization primitives
- Handle compute pipeline creation
## Module Structure
```
cuda-rust-wasm/
├── Cargo.toml
├── src/
│ ├── lib.rs # Main library entry
│ ├── parser/ # CUDA parsing
│ ├── transpiler/ # Code translation
│ ├── runtime/ # CUDA runtime in Rust
│ ├── memory/ # Memory management
│ ├── kernel/ # Kernel execution
│ ├── backend/ # Backend abstraction
│ ├── utils/ # Utilities
│ └── error.rs # Error handling
├── tests/
│ ├── parser_tests.rs
│ ├── transpiler_tests.rs
│ ├── runtime_tests.rs
│ └── integration_tests.rs
├── examples/
│ ├── vector_add.rs # Simple vector addition
│ ├── matrix_mult.rs # Matrix multiplication
│ └── reduction.rs # Parallel reduction
└── docs/
├── api.md # API documentation
├── cuda_mapping.md # CUDA to Rust mappings
└── examples.md # Usage examples
```
## Design Decisions
### 1. Zero-Copy Where Possible
- Use Rust's ownership system to avoid unnecessary copies
- Leverage move semantics for efficient memory transfers
- Implement copy-on-write for shared data
### 2. Type Safety
- Use Rust's type system to catch errors at compile time
- Strongly typed kernel parameters
- Safe abstractions for pointer arithmetic
### 3. Async by Default
- All GPU operations return futures
- Support for async/await patterns
- Non-blocking memory transfers
### 4. Modular Architecture
- Each component is independent and testable
- Clear interfaces between modules
- Support for custom backends
### 5. Progressive Enhancement
- Start with basic kernel support
- Incrementally add CUDA features
- Maintain backward compatibility
## Error Handling Strategy
```rust
use thiserror::Error;
#[derive(Error, Debug)]
pub enum CudaRustError {
#[error("Parser error: {0}")]
ParseError(String),
#[error("Translation error: {0}")]
TranslationError(String),
#[error("Runtime error: {0}")]
RuntimeError(String),
#[error("Memory error: {0}")]
MemoryError(String),
#[error("Backend error: {0}")]
BackendError(String),
}
pub type Result<T> = std::result::Result<T, CudaRustError>;
```
## Performance Considerations
### 1. Memory Layout
- Maintain CUDA memory alignment requirements
- Use repr(C) for compatibility
- Optimize for cache-friendly access patterns
### 2. Kernel Fusion
- Detect and merge compatible kernels
- Reduce kernel launch overhead
- Optimize memory bandwidth usage
### 3. Compile-Time Optimization
- Use const generics for fixed-size operations
- Inline critical functions
- Leverage LLVM optimizations
### 4. Runtime Optimization
- Dynamic kernel selection based on hardware
- Adaptive work distribution
- Memory pooling and reuse
## Future Extensions
### Phase 1: Core Functionality
- Basic kernel translation
- Memory operations
- Simple synchronization
### Phase 2: Advanced Features
- Texture memory support
- Cooperative groups
- Dynamic parallelism
### Phase 3: Optimization
- Auto-tuning capabilities
- Kernel fusion
- Memory coalescing optimization
### Phase 4: Ecosystem Integration
- PyTorch/TensorFlow integration
- ONNX runtime support
- Distributed computing support
## Testing Strategy
### Unit Tests
- Test each module independently
- Mock external dependencies
- Cover edge cases
### Integration Tests
- Test complete CUDA → Rust pipeline
- Verify correctness against CUDA results
- Performance benchmarks
### Example Tests
```rust
#[test]
fn test_vector_add_translation() {
let cuda_code = r#"
__global__ void vectorAdd(float* a, float* b, float* c, int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
c[i] = a[i] + b[i];
}
}
"#;
let rust_code = transpile(cuda_code)?;
assert!(rust_code.contains("#[kernel]"));
assert!(rust_code.contains("thread::index()"));
}
```
## Conclusion
This architecture provides a solid foundation for translating CUDA code to Rust while maintaining performance and adding safety guarantees. The modular design allows for incremental development and easy extension of functionality.