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Template_Metaprogramming

README.md

C++ Template Metaprogramming for CUTLASS 3.x Mastery

Overview

This comprehensive training program is designed to take students from zero experience with C++ template metaprogramming to mastery level, specifically focusing on skills needed to work with CUTLASS 3.x (CUDA Templates for Linear Algebra Subroutines).

Table of Contents

  1. Course Curriculum
  2. Module Descriptions
  3. Prerequisites
  4. Technical Requirements
  5. Learning Path
  6. Assessment Methods
  7. Resources

Course Curriculum

Duration: 8-10 months (20-25 hours/week)

Module Title Duration Topics Covered
1 Foundations of Modern C++ 2-3 weeks Advanced C++ features, RAII, smart pointers, move semantics, function templates, type deduction, SFINAE basics
2 Template Fundamentals 3-4 weeks Class templates, function templates, template parameters, specialization, variadic templates, perfect forwarding
3 Template Metaprogramming Basics 4-5 weeks Compile-time vs runtime, template recursion, type traits, std::enable_if, conditional compilation, dependent expressions
4 Advanced Template Metaprogramming Techniques 4-5 weeks TMP patterns, expression templates, compile-time computations, type lists, higher-order functions, template aliasing
5 CUDA and GPU Programming Fundamentals 2-3 weeks GPU architecture, CUDA model, thread organization, memory hierarchies, coalescing, occupancy
6 Introduction to CUTLASS Architecture 3-4 weeks CUTLASS 3.x overview, GEMM fundamentals, tile-based approach, components, layouts, epilogues
7 CUTLASS Template Patterns and Idioms 4-5 weeks Template conventions, dispatch patterns, specialization strategies, hardware optimizations, math instructions
8 Advanced CUTLASS Customization 4-5 weeks Custom epilogues, non-standard types, tensor operations, performance tuning, debugging
9 Real-world Applications and Case Studies 3-4 weeks Framework integration, quantized operations, sparse ops, mixed precision, production considerations
10 Performance Optimization and Profiling 3-4 weeks Profiling tools, bandwidth optimization, occupancy, asynchronous ops, accuracy validation

Module Descriptions

Module 1: Foundations of Modern C++

Establishes the solid foundation in advanced C++ concepts needed for template metaprogramming:

  • Modern C++ features (auto, constexpr, lambdas)
  • RAII and smart pointers
  • Move semantics and rvalue references
  • Function templates basics
  • Type deduction with auto and decltype
  • SFINAE (Substitution Failure Is Not An Error) basics

Module 2: Template Fundamentals

Master basic template syntax and concepts:

  • Class templates
  • Function templates
  • Template parameters (types, non-types, templates)
  • Template specialization (full and partial)
  • Variadic templates
  • Template argument deduction
  • Perfect forwarding

Module 3: Template Metaprogramming Basics

Understand compile-time computation and type manipulation:

  • Compile-time vs runtime
  • Template recursion
  • Type traits and std::enable_if
  • Conditional compilation with templates
  • Value-dependent and type-dependent expressions
  • Template template parameters
  • Expression SFINAE

Module 4: Advanced Template Metaprogramming Techniques

Master sophisticated TMP techniques:

  • Template metaprogramming patterns:
    • Enable-if pattern
    • Tag dispatching
    • Policy-based design
    • Expression templates
  • Compile-time computations
  • Type lists and operations on them
  • Higher-order template functions
  • Template aliasing and type manipulation
  • Concepts (C++20)

Module 5: CUDA and GPU Programming Fundamentals

Essential GPU programming concepts for CUTLASS:

  • GPU architecture basics
  • CUDA programming model
  • Memory hierarchies (global, shared, registers)
  • Thread organization (blocks, grids, warps)
  • Coalesced memory access
  • Occupancy and performance considerations

Module 6: Introduction to CUTLASS Architecture

Understand CUTLASS design philosophy and basic components:

  • CUTLASS 3.x architecture overview
  • GEMM (General Matrix Multiply) fundamentals
  • Tile-based computation approach
  • CUTLASS components:
    • Threadblock-level operations
    • Warp-level operations
    • Instruction-level operations
  • Layout and stride concepts
  • Epilogues and fusion operations

Module 7: CUTLASS Template Patterns and Idioms

Master CUTLASS-specific template patterns and idioms:

  • CUTLASS template parameter conventions
  • Dispatch patterns in CUTLASS
  • Template specialization strategies in CUTLASS
  • Hardware-specific optimizations
  • CUTLASS math instructions integration
  • Memory access pattern optimization

Module 8: Advanced CUTLASS Customization

Become proficient in extending and customizing CUTLASS:

  • Custom epilogue operations
  • Non-standard data types support
  • Tensor operations beyond GEMM
  • Performance tuning strategies
  • Debugging template-heavy code
  • Integration with other CUDA libraries

Module 9: Real-world Applications and Case Studies

Apply knowledge to solve real problems similar to production scenarios:

  • Deep learning framework integration
  • Quantized matrix multiplication
  • Sparse operations
  • Mixed precision computations
  • Memory bandwidth optimization
  • Numerical accuracy considerations

Module 10: Performance Optimization and Profiling

Master performance analysis and optimization techniques:

  • GPU profiling tools (Nsight Compute, nvprof)
  • Memory bandwidth utilization
  • Occupancy optimization
  • Register usage optimization
  • Cache efficiency
  • Asynchronous operations

Prerequisites

Required Knowledge:

  • Basic C++ programming knowledge (variables, functions, classes)
  • Understanding of basic mathematical concepts (matrices, vectors)
  • Familiarity with Linux command line

Recommended Knowledge:

  • Basic understanding of computer architecture
  • Experience with any programming language
  • Mathematical background in linear algebra

Technical Requirements

Hardware:

  • NVIDIA GPU with compute capability 6.0 or higher (Pascal architecture or newer)
  • At least 8GB GPU memory recommended
  • Multi-core CPU with at least 8 cores
  • 16GB+ system RAM
  • 50GB+ free disk space

Software:

  • CUDA Toolkit 11.0 or higher
  • C++ compiler supporting C++17 (GCC 7+, Clang 5+, MSVC 2017+)
  • CMake 3.12 or higher
  • Git version control
  • Python 3.6+ (for some examples and tools)
  • Recommended IDE: Visual Studio Code, CLion, or similar with C++ support

Development Environment Setup:

# Install CUDA Toolkit (Ubuntu/Debian example)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.0-1_all.deb
sudo dpkg -i cuda-keyring_1.0-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-0

# Verify installation
nvcc --version
nvidia-smi

# Install additional tools
sudo apt-get install build-essential cmake git python3-dev

Learning Path

Phase 1: Foundation Building (Modules 1-4, Months 1-3)

Focus on mastering C++ template metaprogramming fundamentals:

  • Week 1-6: Modern C++ and template basics
  • Week 7-14: Advanced template metaprogramming
  • Emphasis on hands-on exercises and small projects

Phase 2: GPU Computing (Modules 5-6, Month 4)

Transition to GPU programming and CUTLASS architecture:

  • Week 15-18: CUDA fundamentals
  • Week 19-22: CUTLASS architecture introduction
  • Practical CUDA programming exercises

Phase 3: Advanced CUTLASS (Modules 7-8, Months 5-6)

Deep dive into CUTLASS internals and customization:

  • Week 23-30: Template patterns and idioms
  • Week 31-38: Advanced customization techniques
  • Custom kernel development projects

Phase 4: Real-World Applications (Modules 9-10, Months 7-8)

Apply knowledge to production scenarios:

  • Week 39-44: Real-world applications
  • Week 45-50: Performance optimization and profiling
  • Capstone project development

Assessment Methods

Continuous Assessment:

  • Weekly coding assignments (40% of grade)
  • Peer code reviews (15% of grade)
  • Template debugging challenges (15% of grade)
  • Performance optimization tasks (15% of grade)
  • Participation in discussions (15% of grade)

Milestone Projects:

  • Mid-course project: Implement a basic linear algebra operation using templates (Month 4)
  • Final project: Design and implement a custom high-performance kernel using CUTLASS (Month 8)

Capstone Project:

Students will choose a real-world problem and implement a solution using advanced CUTLASS techniques, demonstrating mastery of template metaprogramming concepts.

Resources

Required Reading:

  • "Effective Modern C++" by Scott Meyers
  • "C++ Templates: The Complete Guide" by David Vandevoorde, Nicolai Josuttis, and Douglas Gregor
  • CUTLASS documentation and examples
  • CUDA programming guides

Supplementary Materials:

  • Video lectures on advanced C++ concepts
  • Interactive coding environments
  • Performance analysis tools tutorials
  • Community forums and discussion groups
  • Sample code repositories

Online Resources:

Getting Started

  1. Set up your development environment following the technical requirements above
  2. Begin with Module 1 - don't skip the foundations even if you have prior C++ experience
  3. Follow the weekly schedule - consistency is key to mastering these concepts
  4. Complete all hands-on exercises - theory alone is insufficient
  5. Join the community - engage with fellow learners and instructors

Support

For questions, issues, or support:

  • Open an issue in the course repository
  • Join the community Discord/Slack channel
  • Attend weekly office hours (schedule TBD)

This curriculum is regularly updated to reflect the latest developments in C++ template metaprogramming and CUTLASS technology.