WGPU Backend

The WGPU backend provides cross-platform GPU compute acceleration using the WebGPU standard. It supports Metal, Vulkan, DirectX 12, and OpenGL backends, making it an excellent choice for portable high-performance computing.

Features

• Cross-Platform: Works on Windows, macOS, Linux, iOS, Android, and Web
• Multiple APIs: Supports Vulkan, Metal, DX12, DX11, OpenGL ES, and WebGL
• Compute Shaders: Uses WGSL (WebGPU Shading Language) for parallel operations
• Memory Efficient: GPU buffer management with automatic cleanup
• Future-Proof: Based on the emerging WebGPU standard

Installation

Enable the WGPU backend with the feature flag:

[dependencies]
tensor_frame = { version = "0.0.3-alpha", features = ["wgpu"] }

Additional Dependencies:

• No platform-specific GPU drivers required
• Uses system graphics drivers (Metal, Vulkan, DirectX, OpenGL)

System Requirements

Minimum Requirements

• GPU: Any GPU with compute shader support
• Driver: Up-to-date graphics drivers
• Memory: Sufficient GPU memory for tensor data

Supported Platforms

Implementation Details

Storage

WGPU tensors use GPU buffers for data storage:

pub struct WgpuStorage {
    pub buffer: Arc<wgpu::Buffer>,    // GPU buffer handle
}

Buffer Properties:

• Location: GPU memory (VRAM)
• Layout: Contiguous, row-major layout
• Usage: Storage buffers with compute shader access
• Synchronization: Automatic via command queue

Compute Shaders

Operations are implemented as WGSL compute shaders loaded from external files in src/shaders/:

• add.wgsl - Element-wise addition
• sub.wgsl - Element-wise subtraction
• mul.wgsl - Element-wise multiplication
• div.wgsl - Element-wise division with IEEE 754 compliance

// Example: Element-wise addition shader (add.wgsl)
@group(0) @binding(0) var<storage, read> input_a: array<f32>;
@group(0) @binding(1) var<storage, read> input_b: array<f32>;
@group(0) @binding(2) var<storage, read_write> output: array<f32>;

@compute @workgroup_size(64)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
    let index = global_id.x;
    if (index >= arrayLength(&input_a)) {
        return;
    }
    output[index] = input_a[index] + input_b[index];
}

Parallelization

• Workgroups: Operations dispatched in parallel workgroups
• Thread Count: Automatically sized based on tensor dimensions
• GPU Utilization: Optimized for high occupancy on modern GPUs

Performance Characteristics

Strengths

• Massive Parallelism: Thousands of parallel threads
• High Throughput: Excellent for large tensor operations
• Memory Bandwidth: High GPU memory bandwidth utilization
• Compute Density: Specialized compute units for arithmetic operations

Limitations

• Latency: GPU command submission and synchronization overhead
• Memory Transfer: CPU-GPU data transfers can be expensive
• Limited Precision: Currently only supports f32 operations
• Shader Compilation: First-use compilation overhead

Performance Guidelines

Optimal Use Cases

// Large tensor operations (> 10K elements)
let large = Tensor::zeros(vec![2048, 2048])?;
let result = (large_a * large_b) + large_c;

// Repeated operations on same-sized tensors
for batch in batches {
    let output = model.forward(batch)?;  // Shader programs cached
}

// Element-wise operations with complex expressions
let result = ((a * b) + c).sqrt();  // Single GPU kernel

Suboptimal Use Cases

// Very small tensors
let small = Tensor::ones(vec![10, 10])?;  // GPU overhead dominates

// Frequent CPU-GPU transfers  
let gpu_tensor = cpu_tensor.to_backend(BackendType::Wgpu)?;
let back_to_cpu = gpu_tensor.to_vec()?;  // Expensive transfers

// Scalar operations
let sum = tensor.sum(None)?;  // Result copied back to CPU

Memory Management

GPU Memory Allocation

WGPU automatically manages GPU memory:

let tensor = Tensor::zeros(vec![2048, 2048])?;  // Allocates ~16MB GPU memory

Memory Pool: WGPU uses internal memory pools for efficient allocation
Garbage Collection: Buffers automatically freed when last reference dropped
Fragmentation: Large allocations may fail even with sufficient total memory

Memory Transfer Patterns

// Efficient: Create on GPU
let gpu_tensor = Tensor::zeros(vec![1000, 1000])?
    .to_backend(BackendType::Wgpu)?;

// Inefficient: Frequent transfers
let result = cpu_data.to_backend(BackendType::Wgpu)?
    .sum(None)?
    .to_backend(BackendType::Cpu)?;  // Multiple transfers

Memory Debugging

Monitor GPU memory usage:

// Check GPU memory limits
let limits = device.limits();
println!("Max buffer size: {} MB", limits.max_buffer_size / (1024*1024));

// Handle out-of-memory errors
match Tensor::zeros(vec![16384, 16384]) {
    Ok(tensor) => println!("Allocated 1GB GPU tensor"),
    Err(TensorError::BackendError(msg)) if msg.contains("memory") => {
        eprintln!("GPU out of memory, trying smaller size");
    }
    Err(e) => eprintln!("Other error: {}", e),
}

Debugging and Profiling

Shader Debugging

WGPU provides validation and debugging features:

// Enable validation (debug builds)
let instance = wgpu::Instance::new(&wgpu::InstanceDescriptor {
    backends: wgpu::Backends::all(),
    flags: wgpu::InstanceFlags::DEBUG | wgpu::InstanceFlags::VALIDATION,
    ..Default::default()
});

Performance Profiling

Use GPU profiling tools:

Windows (DirectX):

• PIX for Windows
• RenderDoc
• Visual Studio Graphics Diagnostics

macOS (Metal):

• Xcode Instruments (GPU Timeline)
• Metal System Trace

Linux (Vulkan):

• RenderDoc
• Vulkan Tools

Custom Timing

use std::time::Instant;

let start = Instant::now();
let result = gpu_tensor_a + gpu_tensor_b;
// Note: GPU operations are asynchronous!
let _data = result.to_vec()?;  // Synchronization point
println!("GPU operation took: {:?}", start.elapsed());

Error Handling

WGPU backend errors can occur at multiple levels:

Device Creation Errors

match WgpuBackend::new() {
    Ok(backend) => println!("WGPU backend ready"),
    Err(TensorError::BackendError(msg)) => {
        eprintln!("WGPU initialization failed: {}", msg);
        // Fallback to CPU backend
    }
}

Runtime Errors

// Out of GPU memory
let result = Tensor::zeros(vec![100000, 100000]); // May fail

// Shader compilation errors (rare)
let result = custom_operation(tensor);  // May fail for invalid shaders

// Device lost (driver reset, etc.)
let result = tensor.sum(None);  // May fail if device is lost

Common Error Scenarios:

• Device Not Found: No compatible GPU available
• Out of Memory: GPU memory exhausted
• Driver Issues: Outdated or buggy graphics drivers
• Unsupported Operations: Feature not implemented in WGPU backend

Platform-Specific Notes

Windows

• DirectX 12: Best performance and feature support
• Vulkan: Good alternative if DX12 not available
• DirectX 11: Fallback with limited compute support

macOS

• Metal: Excellent native support and performance
• MoltenVK: Vulkan compatibility layer (not recommended for production)

Linux

• Vulkan: Primary choice with best performance
• OpenGL: Fallback with limited compute features
• Graphics Drivers: Ensure latest Mesa/NVIDIA/AMD drivers

Mobile (iOS/Android)

• iOS: Metal provides excellent mobile GPU performance
• Android: Vulkan on newer devices, OpenGL ES fallback
• Power Management: Be aware of thermal throttling

Web (Experimental)

• WebGPU: Emerging standard with excellent performance potential
• WebGL2: Fallback with compute shader emulation
• Browser Support: Chrome/Edge (flag), Firefox (experimental)

Optimization Tips

Workgroup Size Tuning

// Optimal workgroup sizes depend on GPU architecture
// Current default: 64 threads per workgroup
// Nvidia: 32 (warp size) or 64
// AMD: 64 (wavefront size)  
// Intel: 32 or 64
// Mobile: 16 or 32

Batch Operations

// Efficient: Batch similar operations
let results: Vec<Tensor> = inputs
    .iter()
    .map(|input| model.forward(input))
    .collect()?;

// Inefficient: Individual operations
for input in inputs {
    let result = model.forward(input)?;
    let cpu_result = result.to_vec()?;  // Forces synchronization
}

Memory Layout Optimization

// Ensure tensor shapes are GPU-friendly
let aligned_size = (size + 63) & !63;  // Align to 64-element boundaries
let tensor = Tensor::zeros(vec![aligned_size, aligned_size])?;

Future Developments

The WGPU backend is actively developed with planned improvements:

• Reduction Operations: Sum, mean, and other reductions on GPU
• Advanced Operations: GPU-optimized tensor operations
• Mixed Precision: f16 and bf16 data type support
• Async Operations: Fully asynchronous GPU command queues
• WebGPU Stability: Production-ready web deployment