GPU Acceleration with the C++ Standard Library

1. About the Course

“GPU Acceleration with the C++ Standard Library” is an intensive 10-day course designed for developers and engineers seeking to leverage the power of GPU acceleration using C++ and the NVIDIA HPC SDK. As the demand for high-performance computing (HPC) continues to rise, mastering GPU programming is essential for optimizing computationally intensive applications. This course focuses on integrating the C++ Standard Library with GPU acceleration techniques provided by the NVIDIA HPC SDK, enabling participants to develop scalable and efficient applications.

The course is ideal for professionals who already have a background in C++ programming and want to expand their skill set by incorporating GPU acceleration into their applications. Participants will learn how to write GPU-accelerated code using the NVIDIA HPC SDK, optimize their applications for maximum performance, and integrate these optimizations seamlessly with the C++ Standard Library.

2. Learning Objectives

By the end of this course, participants will be able to:

Understand the fundamentals of GPU acceleration and C++: Grasp the core concepts of GPU computing and how C++ can be used to develop high-performance applications.
Utilize the NVIDIA HPC SDK for GPU programming: Write and optimize GPU-accelerated C++ code using the NVIDIA HPC SDK.
Integrate GPU acceleration with the C++ Standard Library: Seamlessly combine GPU programming techniques with the C++ Standard Library to create efficient and maintainable code.
Optimize GPU-accelerated applications: Apply best practices to optimize memory usage, data transfers, and parallel execution in GPU-accelerated applications.
Develop parallel algorithms with C++ and NVIDIA HPC SDK: Design and implement parallel algorithms that leverage GPU resources effectively.
Profile and debug GPU-accelerated C++ applications: Use profiling tools to analyze and optimize the performance of GPU-accelerated applications.

3. Course Prerequisites

This course is designed for developers with a solid foundation in C++ programming. The prerequisites include:

C++ Programming: A strong understanding of C++ programming, including advanced features such as templates, the Standard Template Library (STL), and object-oriented principles.
Basic Knowledge of GPU Computing: Familiarity with GPU computing concepts, parallel processing, and basic CUDA programming is beneficial.
Experience with Linux/Command Line Interface: Proficiency with the Linux operating system and command-line tools, as most HPC development is performed in a Linux environment.
Mathematics: A good grasp of mathematics, particularly linear algebra and matrix operations, which are commonly used in high-performance computing applications.

4. Course Outlines

This course is structured to progressively build your expertise in GPU acceleration using C++ and the NVIDIA HPC SDK. The content is organized as follows:

Introduction to GPU Acceleration and C++: Overview of GPU acceleration, C++ basics, and the NVIDIA HPC SDK.
Setting Up the Development Environment: Installation and configuration of the necessary software tools, including the NVIDIA HPC SDK.
Understanding the C++ Standard Library for HPC: Utilizing the C++ Standard Library in high-performance computing contexts.
CUDA Programming with C++: Writing and optimizing CUDA code in C++ using the NVIDIA HPC SDK.
Optimizing Memory and Data Transfers: Techniques for optimizing memory management and data transfers in GPU-accelerated applications.
Parallel Programming with the C++ Standard Library: Implementing parallel algorithms in C++ using standard library features.
Advanced Optimization Techniques with the NVIDIA HPC SDK: Exploring advanced optimization strategies for GPU-accelerated applications.
Integrating GPU Acceleration with Existing C++ Projects: Best practices for integrating GPU acceleration into existing C++ projects.
Profiling and Debugging GPU-Accelerated Applications: Using profiling tools to optimize and debug GPU-accelerated C++ applications.
Capstone Project: A hands-on project that involves developing and optimizing a GPU-accelerated application using C++ and the NVIDIA HPC SDK.

5. Day-by-Day Breakdown

Day 1: Introduction to GPU Acceleration and C++

Objectives: Understand the basics of GPU acceleration, the role of C++ in HPC, and the capabilities of the NVIDIA HPC SDK.
Topics:
- Overview of GPU acceleration and its benefits
- Introduction to C++ and its relevance in HPC
- Introduction to the NVIDIA HPC SDK
Activities:
- Reading materials on GPU computing and C++ in HPC
- External link: NVIDIA HPC SDK Overview
- Internal link: Regent Studies Advanced C++ Courses

Day 2: Setting Up the Development Environment

Objectives: Set up and configure the development environment, including the installation of the NVIDIA HPC SDK.
Topics:
- Installing the NVIDIA HPC SDK
- Setting up the development environment on Linux
- Configuring tools and compilers for GPU programming
Activities:
- Step-by-step installation and configuration guide
- Verifying the environment setup with sample code execution

Day 3: Understanding the C++ Standard Library for HPC

Objectives: Learn how to effectively utilize the C++ Standard Library in high-performance computing contexts.
Topics:
- Overview of the C++ Standard Library
- Using C++ STL containers in HPC applications
- Best practices for C++ programming in HPC
Activities:
- Writing sample C++ programs that utilize STL in an HPC context

Day 4: CUDA Programming with C++

Objectives: Write and optimize CUDA code in C++ using the NVIDIA HPC SDK.
Topics:
- Basics of CUDA programming with C++
- Writing CUDA kernels in C++
- Compiling and running CUDA code with the NVIDIA HPC SDK
Activities:
- Hands-on exercise: Writing and executing a simple CUDA program in C++

Day 5: Optimizing Memory and Data Transfers

Objectives: Learn techniques to optimize memory management and data transfers in GPU-accelerated applications.
Topics:
- Memory management in CUDA
- Optimizing data transfers between host and device
- Best practices for efficient memory usage in HPC
Activities:
- Implement and analyze memory optimization techniques in CUDA programs

Day 6: Parallel Programming with the C++ Standard Library

Objectives: Implement parallel algorithms in C++ using standard library features.
Topics:
- Introduction to parallel algorithms in C++
- Using C++ STL and parallel algorithms for GPU computing
- Combining CUDA with C++ parallel algorithms
Activities:
- Write and execute parallel algorithms in C++ that utilize GPU acceleration

Day 7: Advanced Optimization Techniques with the NVIDIA HPC SDK

Objectives: Explore advanced strategies for optimizing GPU-accelerated applications.
Topics:
- Advanced CUDA optimization strategies
- Performance tuning with NVIDIA tools
- Optimizing multi-node applications with MPI
Activities:
- Apply advanced optimization techniques to a GPU-accelerated application and analyze the results

Day 8: Integrating GPU Acceleration with Existing C++ Projects

Objectives: Learn best practices for integrating GPU acceleration into existing C++ projects.
Topics:
- Integrating CUDA code with existing C++ projects
- Managing build systems and dependencies
- Ensuring compatibility and performance in integrated projects
Activities:
- Integrate CUDA code into a sample C++ project and analyze the performance

Day 9: Profiling and Debugging GPU-Accelerated Applications

Objectives: Use profiling tools to optimize and debug GPU-accelerated C++ applications.
Topics:
- Profiling GPU-accelerated applications with Nsight Systems and Compute
- Debugging CUDA code in C++
- Identifying and resolving performance bottlenecks
Activities:
- Profile and debug a GPU-accelerated C++ application using NVIDIA tools

Day 10: Capstone Project

Objectives: Apply all the knowledge gained throughout the course to develop and optimize a GPU-accelerated application using C++ and the NVIDIA HPC SDK.
Topics:
- Project planning and design
- Coding, testing, and optimizing the application
- Presenting the project and discussing the results
Activities:
- Work on a real-world project that involves developing and optimizing a GPU-accelerated application using C++ and the NVIDIA HPC SDK

6. Learning Outcomes

By the end of “GPU Acceleration with the C++ Standard Library,” participants will be able to:

Develop GPU-accelerated applications using C++: Confidently write and optimize C++ code for GPU acceleration using the NVIDIA HPC SDK.
Optimize memory management and data transfers: Apply techniques to optimize memory usage and data transfers in GPU-accelerated applications.
Implement parallel algorithms with C++: Design and implement parallel algorithms using the C++ Standard Library and NVIDIA HPC SDK.
Integrate GPU acceleration with existing C++ projects: Seamlessly incorporate GPU programming into existing C++ projects, ensuring compatibility and enhanced performance.
Use profiling tools for optimization: Effectively profile and debug GPU-accelerated applications using NVIDIA’s profiling tools.
Complete a real-world GPU acceleration project: Demonstrate your ability to develop and optimize a complete GPU-accelerated application through a capstone project.

Participants will finish the course with a strong understanding of GPU-accelerated computing, practical experience in integrating C++ with the NVIDIA HPC SDK, and the ability to optimize and profile applications for high-performance computing. This course is an essential step for anyone looking to specialize in GPU programming and high-performance computing with C++.

This course outline is designed to be engaging, informative, and structured to ensure that participants gain the necessary skills to excel in GPU-accelerated computing with C++ and the NVIDIA HPC SDK. Whether you are aiming to enhance your current projects or expand your skill set, this course provides the tools and knowledge to help you succeed.