ck_tile_tutorial_index.md

CK Tile Comprehensive Tutorial Index

This tutorial series provides an in-depth understanding of the Composable Kernel (CK) Tile programming model, with enhanced Python implementations that include C++ code correspondence, visualizations, and step-by-step explanations.

Overview

The CK Tile programming model is a high-performance abstraction for GPU kernel development that provides:

• Tile-based data distribution across GPU compute units
• Efficient memory access patterns with coalescing and vectorization
• Flexible tensor operations for complex algorithms
• Hardware-optimized implementations for AMD GPUs

Tutorial Structure

1. Tile Distribution Tutorial (tile_distribution_tutorial.py)

Learn how tensor data is distributed across GPU processing elements.

Key Concepts:

• Coordinate systems (X, Y, P, R)
• Thread-to-data mapping
• Warp and block-level distribution
• Space-filling curves and swizzling

Highlights:

• Interactive visualizations of distribution patterns
• C++ code snippets showing actual CK implementations
• Common patterns: GEMM, convolution, reduction
• Performance optimization strategies

Example Usage:

from pytensor.tile_distribution_tutorial import run_interactive_tutorial
run_interactive_tutorial()

2. Tile Window Tutorial (tile_window_tutorial.py)

Master the tile window abstraction for efficient tensor access.

Key Concepts:

• Window views into tensor memory
• Load/store operations
• Memory coalescing
• Boundary handling

Highlights:

• Step-by-step memory access visualization
• Vectorized I/O demonstrations
• Data layout impact on performance
• Complete GEMM example with tile windows

Example Usage:

from pytensor.tile_window_tutorial import run_tile_window_tutorial
run_tile_window_tutorial()

3. Tensor Operations Tutorial (tensor_operations_tutorial.py)

Explore the complete set of tensor operations available in CK.

Key Concepts:

• load_tile / store_tile
• shuffle_tile (inter-thread communication)
• update_tile (element-wise operations)
• sweep_tile (reductions and scans)

Highlights:

• Operation lifecycle visualization
• Fusion strategies for performance
• Real-world examples (GEMM, LayerNorm)
• Performance optimization checklist

Example Usage:

from pytensor.tensor_operations_tutorial import run_tensor_operations_tutorial
run_tensor_operations_tutorial()

Learning Path

Beginner Path

1. Start with tile_distribution_tutorial.py - Section 1 (Core Concepts)
2. Move to tile_window_tutorial.py - Section 1 (Basic Operations)
3. Try simple examples in tensor_operations_tutorial.py - Section 1 (Load/Store)

Intermediate Path

1. Study GEMM distribution patterns in tile_distribution_tutorial.py
2. Understand memory coalescing in tile_window_tutorial.py
3. Learn operation fusion in tensor_operations_tutorial.py

Advanced Path

1. Master hierarchical tiling and swizzling patterns
2. Optimize memory access with vectorization
3. Implement custom kernels using the full operation set

C++ Integration

Each tutorial module includes extensive C++ code snippets that show:

1. Direct CK Library Usage

   // From actual CK headers
   template <typename TileDistribution>
   struct tile_window_with_static_distribution {
       // Implementation details with explanations
   };

2. Kernel Implementation Patterns

   // Complete kernel examples
   template <typename TileShape>
   __global__ void gemm_kernel(...) {
       // Step-by-step implementation
   }

3. Performance Optimizations

   // Vectorized access, shuffle operations, etc.
   using float4 = vector_type<float, 4>::type;

Key Features of Enhanced Tutorials

1. Progressive Complexity

• Start with simple 2D examples
• Build up to complex 3D tensor operations
• Real-world kernel implementations

2. Interactive Visualizations

• Matplotlib-based diagrams
• Thread-to-memory mapping
• Performance comparisons

3. Comprehensive Explanations

• Detailed docstrings
• Step-by-step execution traces
• Common pitfalls and solutions

4. Performance Focus

• Bandwidth utilization analysis
• Optimization checklists
• Hardware-specific considerations

Prerequisites

Software Requirements

• Python 3.8+
• NumPy
• Matplotlib
• (Optional) ROCm SDK for running actual C++ code

Knowledge Requirements

• Basic understanding of GPU architecture
• Familiarity with parallel programming concepts
• C++ knowledge helpful but not required

Getting Started

1. Import the tutorials:

   import sys
   sys.path.append('path/to/pythonck')
   
   from pytensor import tile_distribution_tutorial
   from pytensor import tile_window_tutorial
   from pytensor import tensor_operations_tutorial

2. Run interactive tutorials:

   # Start with tile distribution
   tile_distribution_tutorial.run_interactive_tutorial()

3. Explore specific concepts:

   # Deep dive into memory coalescing
   from pytensor.tile_window_tutorial import MemoryAccessPatterns
   MemoryAccessPatterns.demonstrate_coalescing()

Advanced Topics

Custom Kernel Development

After completing the tutorials, you'll be able to:

• Design efficient tile distributions for your algorithms
• Implement high-performance kernels using CK abstractions
• Optimize memory access patterns
• Debug and profile GPU kernels

Integration with CK Library

The Python tutorials directly correspond to C++ CK usage:

# Python tutorial code
dist = TileDistributionTutorial(...)
window = TileWindowTutorial(...)

# Corresponds to C++ CK code
tile_distribution<...> dist{...};
tile_window<...> window{...};

Contributing

To extend these tutorials:

1. Add New Operations

   • Implement in Python following the existing pattern
   • Include C++ correspondence
   • Add visualizations

2. Create Domain-Specific Examples

   • Machine learning operations
   • Scientific computing kernels
   • Image processing algorithms

3. Improve Visualizations

   • Add animation support
   • 3D visualizations for complex patterns
   • Performance profiling graphs

Resources

CK Documentation

• CK Tile README
• Tile Distribution Encoding
• Example Kernels

Related Tutorials

• GEMM optimization guides
• Tensor operation fusion strategies
• GPU performance analysis

Summary

These enhanced tutorials provide a comprehensive learning experience for the CK Tile programming model by:

1. Bridging Theory and Practice - Python implementations with C++ code
2. Visual Learning - Extensive visualizations and diagrams
3. Hands-on Experience - Interactive examples and exercises
4. Performance Focus - Optimization strategies and best practices

Start your journey with the tile distribution tutorial and progressively build your understanding of high-performance GPU kernel development with CK!

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Note: These tutorials are designed to complement the official CK documentation and provide an accessible learning path for developers new to the CK programming model.