Skip to content

Latest commit

 

History

History
659 lines (516 loc) · 22.5 KB

56Days_to_CUDA.md

File metadata and controls

659 lines (516 loc) · 22.5 KB

Personalized Learning Plan for Mastering C++ and CUDA

Duration: 8 weeks (56 days)

Table of Contents

Assumptions

  • Background: Basic programming experience (e.g., Python, Java), but new to C++ and CUDA.
  • Time Commitment: Approximately 2–3 hours per day for learning and practice.
  • Goals: Build a strong foundation in C++ and become proficient in CUDA for GPU parallel programming.

Week 1: Foundations of C++

Day 1: Introduction to C++ ✅

  • Learning Tasks:
    • Understand the evolution and applications of C++.
    • Set up your development environment.
  • Recommended Resources:
    • Book: C++ Primer by Lippman et al., Chapters 1–2.
    • Online: LearnCPP.com - Chapters 0.1 to 0.6.
  • Practical Exercises:
    • Install an IDE (e.g., Visual Studio, CLion) or set up a text editor with GCC/Clang.
    • Write a simple "Hello, World!" program.

Day 2: Basic Syntax and Data Types

  • Learning Tasks:
    • Learn about variables, data types, and basic operators.
    • Understand input/output streams.
  • Recommended Resources:
    • C++ Primer, Chapter 2.
    • LearnCPP.com - Chapters 1.1 to 1.8.
  • Practical Exercises:
    • Write programs using different data types.
    • Practice reading from and writing to the console.

Day 3: Control Flow Statements

  • Learning Tasks:
    • Explore conditional statements (if, switch).
    • Learn about loops (for, while, do-while).
  • Recommended Resources:
    • C++ Primer, Chapter 3 (Sections 3.1–3.4).
    • LearnCPP.com - Chapters 2.1 to 2.6.
  • Practical Exercises:
    • Create a number guessing game.
    • Implement a program to display prime numbers within a range.

Day 4: Functions

  • Learning Tasks:
    • Understand function declaration, definition, and calling.
    • Learn about scope and lifetime of variables.
  • Recommended Resources:
    • C++ Primer, Chapter 6.
    • LearnCPP.com - Chapters 3.1 to 3.5.
  • Practical Exercises:
    • Write functions for mathematical operations (e.g., factorial, Fibonacci).
    • Practice passing parameters by value and by reference.

Day 5: Arrays and Pointers

  • Learning Tasks:
    • Learn how to declare and use arrays.
    • Understand the basics of pointers and pointer arithmetic.
  • Recommended Resources:
    • C++ Primer, Chapter 4 (Sections 4.1–4.4).
    • LearnCPP.com - Chapters 6.1 to 6.8.
  • Practical Exercises:
    • Implement a program to reverse an array.
    • Practice dynamic memory allocation with new and delete.

Day 6: References and Dynamic Memory

  • Learning Tasks:
    • Understand references vs pointers.
    • Dive deeper into dynamic memory management.
  • Recommended Resources:
    • C++ Primer, Chapter 12 (Sections 12.1–12.2).
    • LearnCPP.com - Chapters 6.9 to 6.13.
  • Practical Exercises:
    • Create a simple program using dynamic arrays.
    • Write functions that modify variables using references.

Day 7: Review and Practice

  • Learning Tasks:
    • Review all concepts covered during the week.
  • Practical Exercises:
    • Solve basic problems on platforms like HackerRank or LeetCode using C++.
    • Reflect on areas that need more practice.

Week 2: Object-Oriented Programming in C++

Day 8: Introduction to Classes and Objects

  • Learning Tasks:
    • Understand classes, objects, and encapsulation.
    • Learn how to define and instantiate classes.
  • Recommended Resources:
    • C++ Primer, Chapter 7 (Sections 7.1–7.3).
    • LearnCPP.com - Chapters 8.1 to 8.7.
  • Practical Exercises:
    • Create a Date class with day, month, and year attributes.
    • Write methods to display and modify the date.

Day 9: Constructors and Destructors

  • Learning Tasks:
    • Learn about constructors, destructors, and object lifecycle.
  • Recommended Resources:
    • C++ Primer, Chapter 13 (Sections 13.1–13.3).
    • LearnCPP.com - Chapters 9.1 to 9.5.
  • Practical Exercises:
    • Implement a BankAccount class with constructors for different account types.
    • Ensure proper resource management in your class.

Day 10: Inheritance and Polymorphism

  • Learning Tasks:
    • Understand inheritance hierarchies.
    • Learn about polymorphic behavior and virtual functions.
  • Recommended Resources:
    • C++ Primer, Chapter 15 (Sections 15.1–15.3).
    • LearnCPP.com - Chapters 11.1 to 11.8.
  • Practical Exercises:
    • Create a base class Shape and derived classes like Circle, Rectangle.
    • Implement virtual methods for area and perimeter calculations.

Day 11: Operator Overloading

  • Learning Tasks:
    • Learn how to overload operators for custom classes.
  • Recommended Resources:
    • C++ Primer, Chapter 14 (Sections 14.1–14.6).
    • LearnCPP.com - Chapters 10.1 to 10.5.
  • Practical Exercises:
    • Overload the + and - operators for a ComplexNumber class.
    • Implement << and >> operators for easy input/output.

Day 12: Templates

  • Learning Tasks:
    • Understand the concept of templates for generic programming.
  • Recommended Resources:
    • C++ Primer, Chapter 16.
    • LearnCPP.com - Chapters 12.1 to 12.7.
  • Practical Exercises:
    • Write a template function for swapping two variables.
    • Implement a template class for a simple Array.

Day 13: Exception Handling

  • Learning Tasks:
    • Learn how to handle errors using exceptions (try, catch, throw).
  • Recommended Resources:
    • C++ Primer, Chapter 18 (Sections 18.1–18.3).
    • LearnCPP.com - Chapters 13.1 to 13.6.
  • Practical Exercises:
    • Modify previous programs to include exception handling.
    • Create custom exception classes.

Day 14: Review and Mini-Project

  • Learning Tasks:
    • Consolidate all OOP concepts learned.
  • Practical Exercises:
    • Start a mini-project like a simple inventory management system.
    • Utilize classes, inheritance, operator overloading, and exception handling.

Week 3: Advanced C++ Concepts

Day 15: The Standard Template Library (STL) - Containers

  • Learning Tasks:
    • Explore STL containers (vector, list, map, set).
  • Recommended Resources:
    • C++ Primer, Chapter 9.
    • LearnCPP.com - Chapters 17.1 to 17.6.
  • Practical Exercises:
    • Implement a program using std::vector and std::map.
    • Solve problems that require dynamic data storage.

Day 16: STL Algorithms and Iterators

  • Learning Tasks:
    • Learn how to use iterators.
    • Explore common STL algorithms (sort, find, copy).
  • Recommended Resources:
    • C++ Primer, Chapter 10.
    • LearnCPP.com - Chapters 18.1 to 18.7.
  • Practical Exercises:
    • Write a program that reads data into a vector and performs sorting.
    • Use iterators to traverse and manipulate container elements.

Day 17: Smart Pointers

  • Learning Tasks:
    • Understand smart pointers (unique_ptr, shared_ptr, weak_ptr).
  • Recommended Resources:
    • C++ Primer, Chapter 12 (Sections 12.1–12.3).
    • LearnCPP.com - Chapters 6.14 to 6.18.
  • Practical Exercises:
    • Refactor previous dynamic memory code to use smart pointers.
    • Experiment with ownership and lifetime management.

Day 18: Move Semantics and Rvalue References

  • Learning Tasks:
    • Learn about move constructors and move assignment.
  • Recommended Resources:
    • Effective Modern C++ by Scott Meyers, Items 23–25.
    • LearnCPP.com - Chapters 15.1 to 15.6.
  • Practical Exercises:
    • Implement a class with move semantics.
    • Test performance improvements with large data structures.

Day 19: Lambda Expressions and Functional Programming

  • Learning Tasks:
    • Understand lambda expressions and their use cases.
  • Recommended Resources:
    • C++ Primer, Chapter 10 (Section 10.3).
    • LearnCPP.com - Chapters 19.1 to 19.5.
  • Practical Exercises:
    • Use lambdas with STL algorithms.
    • Implement a simple event handler using function objects.

Day 20: Concurrency and Multithreading

  • Learning Tasks:
    • Explore the basics of multithreading in C++.
  • Recommended Resources:
    • C++ Concurrency in Action by Anthony Williams, Chapters 2–3.
    • LearnCPP.com - Chapters 21.1 to 21.7.
  • Practical Exercises:
    • Write a program that executes tasks in parallel using std::thread.
    • Practice thread synchronization using mutexes.

Day 21: Review and Advanced Practice

  • Learning Tasks:
    • Revisit challenging concepts.
  • Practical Exercises:
    • Solve advanced problems on coding platforms.
    • Enhance your mini-project with advanced features.

Week 4: Modern C++ and Best Practices

Day 22: Modern C++ Features (C++11/C++14/C++17/C++20)

  • Learning Tasks:
    • Learn about auto, constexpr, decltype, range-based loops, structured bindings.
  • Recommended Resources:
    • Effective Modern C++ by Scott Meyers.
    • LearnCPP.com - Chapters 23.1 to 23.8.
  • Practical Exercises:
    • Update previous code to utilize modern features.
    • Write programs that demonstrate new language capabilities.

Day 23: Best Practices and Coding Standards

  • Learning Tasks:
    • Understand the importance of clean code and maintainability.
  • Recommended Resources:
    • Clean Code by Robert C. Martin.
    • Articles on C++ Core Guidelines.
  • Practical Exercises:
    • Refactor code to improve readability and performance.
    • Implement naming conventions and documentation.

Day 24: Design Patterns in C++

  • Learning Tasks:
    • Study common design patterns (Singleton, Observer, Factory).
  • Recommended Resources:
    • Design Patterns by Gamma et al.
    • LearnCPP.com - Chapters 24.1 to 24.5.
  • Practical Exercises:
    • Implement the Observer pattern in a messaging system.
    • Use the Factory pattern for object creation.

Day 25: Debugging and Testing

  • Learning Tasks:
    • Learn how to debug effectively.
    • Understand unit testing frameworks (e.g., Google Test).
  • Recommended Resources:
    • Documentation on GDB or Visual Studio Debugger.
    • Google Test documentation.
  • Practical Exercises:
    • Debug a complex program with multiple bugs.
    • Write unit tests for your classes and functions.

Day 26: Build Systems and Version Control

  • Learning Tasks:
    • Learn about build tools (Make, CMake).
    • Understand version control with Git.
  • Recommended Resources:
    • Official CMake documentation.
    • Git tutorials on Git-SCM.com.
  • Practical Exercises:
    • Set up a project with CMake.
    • Practice Git workflows: branching, merging, and pull requests.

Day 27: Final Project Planning

  • Learning Tasks:
    • Plan a comprehensive project that incorporates all learned concepts.
  • Practical Exercises:
    • Define the scope, requirements, and milestones of your project.
    • Set up the project repository and environment.

Day 28: Project Development

  • Learning Tasks:
    • Begin coding your final project.
  • Practical Exercises:
    • Implement core functionalities.
    • Apply best practices and modern C++ features.

Week 5: Introduction to CUDA Programming

Day 29: Understanding GPU Architecture and Parallel Computing

  • Learning Tasks:
    • Learn the basics of GPU hardware and parallel computing concepts.
  • Recommended Resources:
    • CUDA Programming: A Developer's Guide to Parallel Computing with GPUs by B. Kirk and W. Hwu, Chapter 1.
    • NVIDIA's CUDA C++ Programming Guide.
  • Practical Exercises:
    • Read about the differences between CPU and GPU architectures.
    • Familiarize yourself with parallel programming terminology.

Day 30: Setting Up the CUDA Development Environment

  • Learning Tasks:
    • Install the CUDA Toolkit and necessary drivers.
    • Configure your IDE for CUDA development.
  • Recommended Resources:
  • Practical Exercises:
    • Verify the installation by compiling and running sample CUDA programs.

Day 31: CUDA Programming Basics

  • Learning Tasks:
    • Understand the CUDA programming model.
    • Learn about kernels, threads, blocks, and grids.
  • Recommended Resources:
    • CUDA Programming, Chapters 2–3.
    • NVIDIA's CUDA C++ Programming Guide (Sections on kernels and execution configuration).
  • Practical Exercises:
    • Write a CUDA program for vector addition.
    • Experiment with different grid and block dimensions.

Day 32: Memory Management in CUDA

  • Learning Tasks:
    • Learn about different memory types: global, shared, constant, and local.
  • Recommended Resources:
    • CUDA Programming, Chapter 5.
    • NVIDIA's documentation on CUDA memory types.
  • Practical Exercises:
    • Implement matrix multiplication using shared memory.
    • Analyze the performance impact of memory optimizations.

Day 33: Synchronization and Communication

  • Learning Tasks:
    • Understand thread synchronization and barriers.
  • Recommended Resources:
    • CUDA Programming, Chapter 8.
    • NVIDIA's documentation on synchronization primitives.
  • Practical Exercises:
    • Implement a parallel reduction algorithm.
    • Use __syncthreads() to coordinate threads.

Day 34: CUDA Streams and Events

  • Learning Tasks:
    • Learn how to use streams for concurrent execution.
    • Understand events for timing and synchronization.
  • Recommended Resources:
    • CUDA Programming, Chapter 11.
    • NVIDIA's documentation on streams and events.
  • Practical Exercises:
    • Overlap data transfer and kernel execution using streams.
    • Measure execution times using CUDA events.

Day 35: Review and Practice

  • Learning Tasks:
    • Revisit challenging concepts from the week.
  • Practical Exercises:
    • Optimize previous CUDA programs.
    • Solve practice problems from NVIDIA's CUDA samples.

Week 6: Advanced CUDA Programming

Day 36: Advanced Memory Management

  • Learning Tasks:
    • Dive into unified memory and memory prefetching.
  • Recommended Resources:
    • NVIDIA's documentation on Unified Memory.
  • Practical Exercises:
    • Modify programs to use unified memory.
    • Test the impact on performance and ease of development.

Day 37: CUDA Libraries and Thrust

  • Learning Tasks:
    • Explore CUDA libraries (cuBLAS, cuFFT) and the Thrust library.
  • Recommended Resources:
  • Practical Exercises:
    • Use Thrust to implement parallel sorting.
    • Perform complex matrix operations using cuBLAS.

Day 38: Debugging and Profiling CUDA Applications

  • Learning Tasks:
    • Learn about CUDA debugging tools (cuda-gdb, Nsight).
    • Understand profiling with NVIDIA Visual Profiler.
  • Recommended Resources:
    • NVIDIA's debugging and profiling guides.
  • Practical Exercises:
    • Debug a CUDA program with race conditions.
    • Profile your applications to identify bottlenecks.

Day 39: Multi-GPU Programming

  • Learning Tasks:
    • Learn how to scale applications across multiple GPUs.
  • Recommended Resources:
    • NVIDIA's documentation on multi-GPU programming.
  • Practical Exercises:
    • Modify applications to distribute workloads across GPUs.
    • Handle data synchronization between devices.

Day 40: Optimization Techniques

  • Learning Tasks:
    • Understand occupancy, latency hiding, and memory coalescing.
  • Recommended Resources:
    • CUDA Programming, Chapters 6 and 9.
    • NVIDIA's CUDA Optimization Guide.
  • Practical Exercises:
    • Optimize kernel launches for maximum occupancy.
    • Implement memory access patterns that improve coalescing.

Day 41: Asynchronous Programming

  • Learning Tasks:
    • Learn about asynchronous data transfers and compute.
  • Recommended Resources:
    • NVIDIA's documentation on asynchronous execution.
  • Practical Exercises:
    • Implement a pipeline that overlaps data transfer and computation.
    • Use page-locked host memory for faster transfers.

Day 42: Review and Mini-Project

  • Learning Tasks:
    • Apply advanced concepts to a mini-project.
  • Practical Exercises:
    • Start a project like an image processing application.
    • Focus on applying optimization and advanced programming techniques.

Week 7: CUDA Optimization and Real-World Applications

Day 43: Performance Optimization Strategies

  • Learning Tasks:
    • Learn about performance metrics and optimization strategies.
  • Recommended Resources:
    • NVIDIA's CUDA Optimization Guide (Sections on performance metrics).
  • Practical Exercises:
    • Analyze and optimize your mini-project for performance.

Day 44: Handling Large Datasets

  • Learning Tasks:
    • Understand strategies for processing data larger than GPU memory.
  • Recommended Resources:
    • Articles on data partitioning and streaming.
  • Practical Exercises:
    • Implement tiled computation for large datasets.
    • Use streams to process data in chunks.

Day 45: Integrating CUDA with Other Libraries

  • Learning Tasks:
    • Learn how to integrate CUDA with libraries like OpenCV.
  • Recommended Resources:
    • Documentation on CUDA interoperability.
  • Practical Exercises:
    • Use CUDA to accelerate computer vision tasks.
    • Implement filters or transformations on images.

Day 46: Heterogeneous Computing

  • Learning Tasks:
    • Explore combining CPU and GPU tasks effectively.
  • Recommended Resources:
    • Articles on heterogeneous computing patterns.
  • Practical Exercises:
    • Partition tasks between CPU and GPU for optimal performance.

Day 47: Machine Learning Applications

  • Learning Tasks:
    • Understand how CUDA is used in machine learning frameworks.
  • Recommended Resources:
    • Documentation on cuDNN and TensorRT.
  • Practical Exercises:
    • Use CUDA to implement basic neural network operations.
    • Accelerate a machine learning algorithm.

Day 48: Final Project Development

  • Learning Tasks:
    • Continue developing your final CUDA project.
  • Practical Exercises:
    • Integrate all learned concepts.
    • Focus on code quality and documentation.

Day 49: Testing and Optimization

  • Learning Tasks:
    • Test and optimize your final project.
  • Practical Exercises:
    • Perform comprehensive testing.
    • Profile and fine-tune performance.

Day 50: Project Presentation and Reflection

  • Learning Tasks:
    • Prepare to present your project.
  • Practical Exercises:
    • Create a presentation highlighting your project's features and optimizations.
    • Reflect on your learning journey and identify areas for future growth.

Week 8: Consolidation and Future Learning Paths

Day 51: Code Review and Feedback

  • Learning Tasks:
    • Conduct a thorough code review of your projects.
  • Practical Exercises:
    • Seek feedback from peers or mentors.
    • Implement improvements based on feedback.

Day 52: Exploring Advanced Topics

  • Learning Tasks:
    • Look into advanced C++ or CUDA topics like template metaprogramming or real-time ray tracing.
  • Recommended Resources:
    • Advanced articles and research papers.
  • Practical Exercises:
    • Implement a small prototype using an advanced concept.

Day 53: Contributing to Open Source

  • Learning Tasks:
    • Learn how to contribute to open-source C++ or CUDA projects.
  • Practical Exercises:
    • Find a project on GitHub that interests you.
    • Submit a pull request or fix an issue.

Day 54: Building a Portfolio

  • Learning Tasks:
    • Compile your projects and code samples.
  • Practical Exercises:
    • Create a GitHub repository showcasing your work.
    • Write a blog post or article about your learning experience.

Day 55: Planning for Certification or Advanced Studies

  • Learning Tasks:
    • Explore certifications or advanced courses.
  • Recommended Resources:
    • NVIDIA's Developer Program.
    • Advanced C++ courses or specializations.
  • Practical Exercises:
    • Enroll in a course or plan for certification exams.

Day 56: Final Reflection and Next Steps

  • Learning Tasks:
    • Reflect on your accomplishments.
    • Set goals for continued learning and skill development.
  • Practical Exercises:
    • Create a learning roadmap for the next 6 months.
    • Identify areas of specialization or interest.

Additional Resources

  • Books:
    • Effective Modern C++ by Scott Meyers
    • Professional CUDA C Programming by John Cheng et al.
  • Online Courses:
  • Websites:

Final Tips

  • Practice Regularly: Consistency is key to mastering programming languages.
  • Join Communities: Participate in forums like Stack Overflow, NVIDIA Developer Forums, and Reddit's r/cpp and r/cuda.
  • Build Projects: Apply your knowledge by building real-world applications.
  • Stay Updated: Keep abreast of the latest developments in C++ and CUDA technologies.

Good luck on your journey to mastering C++ and CUDA! Remember, the key is to build a strong foundation and continuously challenge yourself with new projects and concepts.