Introduction to Accelerated Computing with CUDA C/C++ Training by Tonex
This course is designed to introduce professionals and developers to the world of accelerated computing using NVIDIA’s CUDA (Compute Unified Device Architecture) with C and C++. Through hands-on labs and expert-led discussions, participants will gain foundational knowledge of parallel programming and GPU acceleration. By the end of the course, participants will be equipped with the skills to optimize performance in compute-intensive applications, enabling faster execution of complex algorithms and simulations.
Learning Objectives:
- Understand the core principles of GPU architecture and CUDA programming.
- Learn to write, compile, and execute CUDA C/C++ programs.
- Master techniques for optimizing code performance using parallelization.
- Explore common CUDA libraries and tools for performance analysis and debugging.
- Gain practical experience with memory management in CUDA applications.
- Apply CUDA programming to solve real-world problems in scientific computing and machine learning.
Audience: This course is ideal for software developers, engineers, researchers, and data scientists with a background in C/C++ programming, who are looking to leverage GPU acceleration for high-performance computing applications. Basic knowledge of parallel computing is recommended but not required.
Course Outline:
Introduction to CUDA and GPU Computing
- Overview of GPU architecture
- Differences between CPU and GPU computing
- Introduction to CUDA programming model
- History and evolution of CUDA
- Key applications of GPU acceleration
- CUDA installation and environment setup
CUDA C/C++ Programming Fundamentals
- Writing and compiling CUDA programs
- Host and device memory concepts
- Kernel functions and thread hierarchies
- Memory transfer between CPU and GPU
- Simple parallelization examples
- Debugging CUDA code
Memory Management in CUDA
- Global, shared, and local memory usage
- Understanding memory coalescing
- Register usage and optimization
- Efficient memory allocation and deallocation
- Memory transfer optimization techniques
- Strategies to avoid memory bottlenecks
Performance Optimization Techniques
- Identifying performance bottlenecks
- Using shared memory for optimization
- Load balancing across threads and blocks
- Loop unrolling and memory prefetching
- Avoiding divergence in warp execution
- Profiling and tuning CUDA applications
CUDA Libraries and Tools
- Overview of CUDA libraries (cuBLAS, cuFFT, etc.)
- Using Thrust for parallel programming
- Introduction to CUDA-aware MPI
- Debugging with NVIDIA Nsight tools
- Memory analysis using nvprof
- Leveraging third-party CUDA libraries
Advanced CUDA Programming and Applications
- Multi-GPU programming concepts
- Implementing CUDA streams and events
- CUDA Graphs for asynchronous execution
- Applying CUDA in deep learning frameworks
- Real-world applications in scientific computing
- Scaling applications with GPU clusters
Ready to accelerate your applications and harness the power of GPUs? Enroll in our “Introduction to Accelerated Computing with CUDA C/C++” training today and unlock new potential for high-performance computing in your projects!