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Course Outline
Introduction
- What is GPU programming?
- Why use GPU programming?
- What are the challenges and trade-offs of GPU programming?
- What are the frameworks for GPU programming?
- Choosing the right framework for your application
OpenCL
- What is OpenCL?
- What are the advantages and disadvantages of OpenCL?
- Setting up the development environment for OpenCL
- Creating a basic OpenCL program that performs vector addition
- Using OpenCL API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads
- Using OpenCL C language to write kernels that execute on the device and manipulate data
- Using OpenCL built-in functions, variables, and libraries to perform common tasks and operations
- Using OpenCL memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses
- Using OpenCL execution model to control the work-items, work-groups, and ND-ranges that define the parallelism
- Debugging and testing OpenCL programs using tools such as CodeXL
- Optimizing OpenCL programs using techniques such as coalescing, caching, prefetching, and profiling
CUDA
- What is CUDA?
- What are the advantages and disadvantages of CUDA?
- Setting up the development environment for CUDA
- Creating a basic CUDA program that performs vector addition
- Using CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads
- Using CUDA C/C++ language to write kernels that execute on the device and manipulate data
- Using CUDA built-in functions, variables, and libraries to perform common tasks and operations
- Using CUDA memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses
- Using CUDA execution model to control the threads, blocks, and grids that define the parallelism
- Debugging and testing CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight
- Optimizing CUDA programs using techniques such as coalescing, caching, prefetching, and profiling
ROCm
- What is ROCm?
- What are the advantages and disadvantages of ROCm?
- Setting up the development environment for ROCm
- Creating a basic ROCm program that performs vector addition
- Using ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads
- Using ROCm C/C++ language to write kernels that execute on the device and manipulate data
- Using ROCm built-in functions, variables, and libraries to perform common tasks and operations
- Using ROCm memory spaces, such as global, local, constant, and private, to optimize data transfers and memory accesses
- Using ROCm execution model to control the threads, blocks, and grids that define the parallelism
- Debugging and testing ROCm programs using tools such as ROCm Debugger and ROCm Profiler
- Optimizing ROCm programs using techniques such as coalescing, caching, prefetching, and profiling
Comparison
- Comparing the features, performance, and compatibility of OpenCL, CUDA, and ROCm
- Evaluating GPU programs using benchmarks and metrics
- Learning the best practices and tips for GPU programming
- Exploring the current and future trends and challenges of GPU programming
Summary and Next Steps
Requirements
- An understanding of C/C++ language and parallel programming concepts
- Basic knowledge of computer architecture and memory hierarchy
- Experience with command-line tools and code editors
Audience
- Developers who wish to learn how to use different frameworks for GPU programming and compare their features, performance, and compatibility
- Developers who wish to write portable and scalable code that can run on different platforms and devices
- Programmers who wish to explore the trade-offs and challenges of GPU programming and optimization
28 Hours
Testimonials (1)
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.
