"""
Advanced GPU Integration Techniques for PyTorch

This module provides cross-backend GPU integration techniques that leverage
advanced hardware features and low-level GPU programming concepts for
maximum performance in PyTorch neural networks.

Advanced GPU integration requires understanding hardware capabilities:
- CUDA programming model: Threads, blocks, grids, and memory hierarchy
- Tensor Cores: Specialized hardware for mixed-precision matrix operations
- Memory coalescing: Optimizing global memory access patterns
- Occupancy optimization: Maximizing GPU utilization through resource management
- Multi-GPU techniques: Scaling across multiple GPUs with optimal communication

 ADVANCED TECHNIQUES:
- custom_kernels: CUDA kernel integration and backend-aware generation
- tensor_cores: Specialized Tensor Core utilization patterns
- memory_optimization: Advanced memory hierarchy management
- multi_gpu_patterns: Efficient multi-GPU computation strategies
- profiling_tools: Advanced profiling and performance measurement

 PRACTICAL VALUE:
These techniques enable the highest levels of cross-backend GPU performance,
providing the tools to achieve state-of-the-art efficiency in production
deep learning systems.
"""

# Custom kernel integration
from .custom_kernels import (
    CUDAKernelBuilder,
    CustomKernelWrapper,
    TritonKernelOptimizer,
    optimize_with_custom_kernels,
    validate_kernel_correctness,
)

# Note: Additional modules will be implemented in future updates
# The following imports are placeholders for educational framework structure

__all__ = [
    # Custom kernels (implemented)
    "CustomKernelWrapper",
    "TritonKernelOptimizer",
    "CUDAKernelBuilder",
    "optimize_with_custom_kernels",
    "validate_kernel_correctness",

    # Note: Additional modules will be available in future updates
    # This framework provides the foundation for advanced GPU integration techniques
]
