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Course Outline
Introduction
- What is GPU programming?
- Why is GPU programming used?
- What are the challenges and trade-offs of GPU programming?
- What frameworks and tools are available for GPU programming?
- Choosing the right framework and tool 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 the OpenCL API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronise threads
- Using the 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 optimise data transfers and memory accesses
- Using the OpenCL execution model to control work-items, work-groups, and ND-ranges that define parallelism
- Debugging and testing OpenCL programs using tools such as CodeXL
- Optimising 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 the CUDA API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronise threads
- Using the 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 optimise data transfers and memory accesses
- Using the CUDA execution model to control threads, blocks, and grids that define parallelism
- Debugging and testing CUDA programs using tools such as CUDA-GDB, CUDA-MEMCHECK, and NVIDIA Nsight
- Optimising 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 the ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronise threads
- Using the 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 optimise data transfers and memory accesses
- Using the ROCm execution model to control threads, blocks, and grids that define parallelism
- Debugging and testing ROCm programs using tools such as the ROCm Debugger and ROCm Profiler
- Optimising ROCm programs using techniques such as coalescing, caching, prefetching, and profiling
HIP
- What is HIP?
- What are the advantages and disadvantages of HIP?
- Setting up the development environment for HIP
- Creating a basic HIP program that performs vector addition
- Using the HIP language to write kernels that execute on the device and manipulate data
- Using HIP built-in functions, variables, and libraries to perform common tasks and operations
- Using HIP memory spaces, such as global, shared, constant, and local, to optimise data transfers and memory accesses
- Using the HIP execution model to control threads, blocks, and grids that define parallelism
- Debugging and testing HIP programs using tools such as the ROCm Debugger and ROCm Profiler
- Optimising HIP programs using techniques such as coalescing, caching, prefetching, and profiling
Comparison
- Comparing the features, performance, and compatibility of OpenCL, CUDA, ROCm, and HIP
- Evaluating GPU programs using benchmarks and metrics
- Learning best practices and tips for GPU programming
- Exploring current and future trends and challenges in GPU programming
Summary and Next Steps
Requirements
- A solid understanding of the C/C++ language and parallel programming concepts.
- Foundational knowledge of computer architecture and memory hierarchy.
- Experience with command-line tools and code editors.
Audience
- Developers eager to learn the basics of GPU programming and the key frameworks and tools for developing GPU applications.
- Developers aiming to write portable and scalable code capable of running across different platforms and devices.
- Programmers interested in exploring the benefits and challenges of GPU programming and optimization.
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
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