cuda-rust-wasm 0.1.6

# CUDA-Rust-WASM Implementation Status

## ✅ Completed Core APIs

### 1. Error Handling System (`src/error.rs`)
- Comprehensive error types for all subsystems
- Result type alias for convenient error handling
- Helper macros for creating specific error types
- Full test coverage

### 2. Device Management (`src/runtime/device.rs`)
- Device abstraction supporting multiple backends (Native, WebGPU, CPU)
- Device properties querying
- Automatic backend detection based on target architecture
- Device enumeration and selection

### 3. Memory Allocation Primitives

#### Device Memory (`src/memory/device_memory.rs`)
- `DevicePtr` - Raw device memory management
- `DeviceBuffer<T>` - Type-safe device memory buffer
- Backend-specific allocation strategies
- Host-to-device and device-to-host memory transfers
- Memory safety with proper cleanup in Drop implementations

#### Host Memory (`src/memory/host_memory.rs`)
- `HostBuffer<T>` - Page-locked host memory for efficient transfers
- Slice-based access patterns
- Index trait implementations for convenient access
- Memory copy operations with bounds checking

### 4. Kernel Launch Mechanism (`src/runtime/kernel.rs`)
- `KernelFunction` trait for defining kernels
- `ThreadContext` for accessing thread/block indices
- `LaunchConfig` for specifying grid/block dimensions
- CPU backend executor (sequential execution for testing)
- `kernel_function!` macro for easy kernel definition

### 5. Runtime Infrastructure
- Grid and Block dimension types (`Dim3`)
- Stream abstraction for asynchronous operations
- Runtime context managing device and default stream
- Thread and block index access helpers

### 6. Example Implementation (`examples/vector_add.rs`)
- Complete vector addition example demonstrating:
  - Memory allocation on host and device
  - Data transfer between host and device
  - Kernel definition using the macro
  - Kernel launch with proper configuration
  - Result verification
  - Device property querying

### 7. Build Configuration (`build.rs`)
- WASM target detection and configuration
- CUDA backend detection (when available)
- Optimization flags for release builds
- Native bindings generation support

### 8. Module Organization
- Prelude module for convenient imports
- Proper module exports and re-exports
- Macro availability throughout the crate

## 🚧 TODO / Future Enhancements

### Backend Implementations
1. **Native CUDA Backend**
   - Real CUDA memory allocation (cudaMalloc)
   - CUDA kernel launching
   - CUDA stream management
   - CUDA event synchronization

2. **WebGPU Backend**
   - WebGPU buffer creation
   - Compute pipeline setup
   - Shader compilation from kernels
   - WebGPU command encoding

3. **Optimizations**
   - Parallel CPU execution using Rayon
   - Memory pooling for allocation reuse
   - Kernel caching and JIT compilation
   - Auto-tuning for optimal block sizes

### Additional Features
1. **Advanced Memory**
   - Unified memory support
   - Memory pools for efficient allocation
   - Texture memory support
   - Constant memory

2. **Kernel Features**
   - Shared memory support
   - Warp primitives (shuffle, vote)
   - Atomic operations
   - Dynamic parallelism

3. **Developer Experience**
   - Procedural macros for kernel attributes
   - Better error messages
   - Performance profiling tools
   - Debug visualization

## Usage Example

```rust
use cuda_rust_wasm::prelude::*;
use cuda_rust_wasm::kernel_function;

// Define a kernel
kernel_function!(MyKernel, (&mut [f32], &[f32]), |(output, input), ctx| {
    let tid = ctx.global_thread_id();
    if tid < input.len() {
        output[tid] = input[tid] * 2.0;
    }
});

fn main() -> Result<()> {
    // Initialize runtime
    let runtime = Runtime::new()?;
    let device = runtime.device();
    
    // Allocate memory
    let mut d_input = DeviceBuffer::new(1024, device.clone())?;
    let mut d_output = DeviceBuffer::new(1024, device.clone())?;
    
    // Launch kernel
    let config = LaunchConfig::new(
        Grid::new(4),
        Block::new(256)
    );
    
    launch_kernel(MyKernel, config, (&mut d_output, &d_input))?;
    
    Ok(())
}
```

## Testing

To test the implementation:

1. Install Rust: `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh`
2. Run tests: `cargo test`
3. Run example: `cargo run --example vector_add`
4. Build for WASM: `cargo build --target wasm32-unknown-unknown`

The implementation provides a solid foundation for CUDA-to-Rust transpilation with support for multiple backends and WASM compilation.