# Advanced Topics

This section covers advanced TorchFX features for experienced users, including GPU acceleration, PyTorch ecosystem integration, multi-channel processing, and performance optimization.

## Who Should Read This

These guides are for users who:

- Want to maximize performance with GPU acceleration
- Need to integrate TorchFX with existing PyTorch codebases
- Process multi-channel or surround sound audio
- Require detailed performance tuning and optimization

## Prerequisites

Before diving into advanced topics, ensure you're familiar with:

- {doc}`../core-concepts/index` - Core TorchFX architecture
- {doc}`../tutorials/index` - Basic usage patterns
- PyTorch fundamentals - {class}`torch.nn.Module`, device management, tensors

## Topics Overview

```{toctree}
:maxdepth: 2

gpu-acceleration
pytorch-integration
multi-channel
performance
```

### {doc}`gpu-acceleration`

Learn how to leverage CUDA-enabled GPUs for accelerated audio processing. Covers device management, data transfer strategies, and when GPU acceleration provides the greatest benefits.

**Key concepts**: Device transfer, automatic propagation, performance considerations

### {doc}`pytorch-integration`

Discover how TorchFX integrates seamlessly with PyTorch's neural network ecosystem. Learn to combine TorchFX with {class}`torch.nn.Sequential`, custom modules, torchaudio transforms, and more.

**Key concepts**: Module composition, gradient flow, library mixing

### {doc}`multi-channel`

Master multi-channel audio processing patterns for stereo, surround sound, and custom channel configurations. Understand tensor shape conventions and per-channel vs. cross-channel processing.

**Key concepts**: Tensor shapes, channel strategies, nn.ModuleList patterns

### {doc}`performance`

Understand TorchFX's performance characteristics through comprehensive benchmarks. Learn optimization strategies for filters, effects, and processing chains.

**Key concepts**: Benchmarking methodology, GPU vs CPU performance, optimization guidelines

## Quick Navigation

### By Use Case

**I want to process audio faster**
→ Start with {doc}`gpu-acceleration`, then review {doc}`performance`

**I'm building a PyTorch neural network with audio**
→ Read {doc}`pytorch-integration` for seamless integration patterns

**I'm working with stereo or surround sound**
→ Check {doc}`multi-channel` for tensor conventions and processing patterns

**I need to optimize my audio pipeline**
→ See {doc}`performance` for benchmarks and optimization guidelines

### By Level

**Intermediate**: {doc}`multi-channel`, {doc}`pytorch-integration`
**Advanced**: {doc}`gpu-acceleration`, {doc}`performance`

## Additional Resources

- {doc}`../core-concepts/index` - Foundational concepts
- {doc}`../tutorials/index` - Practical examples
- {doc}`../developer/index` - Contributing and development

## External References

- [PyTorch Documentation](https://pytorch.org/docs/stable/index.html) - Official PyTorch docs
- [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/) - NVIDIA CUDA documentation
- [torchaudio Documentation](https://pytorch.org/audio/stable/index.html) - Audio processing in PyTorch
