Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction
- What is GPU programming?
- Why use GPU programming?
- What are the challenges and trade-offs of GPU programming?
- What frameworks exist for GPU programming?
- Selecting the appropriate 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 the 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 the 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 the 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 the 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 the 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 the 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 best practices and tips for GPU programming
- Exploring current and future trends and challenges of GPU programming
Summary and Next Steps
Requirements
- Proficiency in the C/C++ language and understanding of parallel programming concepts
- Foundational knowledge of computer architecture and memory hierarchy
- Experience using command-line tools and code editors
Target Audience
- Developers seeking to learn how to use various frameworks for GPU programming and compare their features, performance, and compatibility
- Developers aiming to write portable and scalable code capable of running across different platforms and devices
- Programmers interested in exploring the trade-offs and challenges associated with GPU programming and optimization
28 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 6400 € + VAT*
Contact us for an exact quote and to hear our latest promotions
