ROCm for Windows Training Course
ROCm is an open-source platform designed for GPU programming that supports AMD graphics cards, while also maintaining compatibility with CUDA and OpenCL. This platform grants developers direct access to hardware specifics, offering complete control over the parallelization process. However, this level of control demands a solid grasp of device architecture, memory models, execution frameworks, and optimization strategies.
The recent introduction of ROCm for Windows enables users to install and utilize ROCm on the Windows operating system, which remains a dominant choice for both personal and professional use. This adaptation allows users to harness the computational power of AMD GPUs for diverse applications, including artificial intelligence, gaming, graphics processing, and scientific computing.
This instructor-led live training session, available either online or in-person, is tailored for beginner to intermediate-level developers who aim to install and utilize ROCm on Windows to program AMD GPUs and fully exploit their parallel capabilities.
Upon completing this training, participants will be equipped to:
- Establish a development environment comprising the ROCm Platform, an AMD GPU, and Visual Studio Code on Windows.
- Develop a fundamental ROCm application that executes vector addition on the GPU and retrieves results from GPU memory.
- Utilize the ROCm API to query device details, manage device memory allocation and deallocation, transfer data between host and device, launch kernels, and synchronize threads.
- Employ the HIP language to write kernels that run on the GPU and manipulate data.
- Leverage HIP built-in functions, variables, and libraries to carry out common tasks and operations.
- Optimize data transfers and memory access by utilizing ROCm and HIP memory spaces, including global, shared, constant, and local regions.
- Control threads, blocks, and grids that define parallelism through ROCm and HIP execution models.
- Debug and test ROCm and HIP applications using tools like ROCm Debugger and ROCm Profiler.
- Enhance ROCm and HIP programs using optimization techniques such as coalescing, caching, prefetching, and profiling.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
Introduction
- What is ROCm?
- What is HIP?
- Comparison of ROCm, CUDA, and OpenCL
- Overview of ROCm and HIP features and architecture
- Differences between ROCm for Windows and ROCm for Linux
Installation
- Installing ROCm on Windows
- Verifying installation and checking device compatibility
- Updating or uninstalling ROCm on Windows
- Troubleshooting common installation issues
Getting Started
- Creating a new ROCm project using Visual Studio Code on Windows
- Exploring the project structure and files
- Compiling and running the program
- Displaying output using printf and fprintf
ROCm API
- Utilizing the ROCm API in host programs
- Querying device information and capabilities
- Allocating and deallocating device memory
- Copying data between host and device
- Launching kernels and synchronizing threads
- Handling errors and exceptions
HIP Language
- Using the HIP language in device programs
- Writing kernels that execute on the GPU and manipulate data
- Utilizing data types, qualifiers, operators, and expressions
- Using built-in functions, variables, and libraries
ROCm and HIP Memory Model
- Utilizing different memory spaces: global, shared, constant, and local
- Managing different memory objects: pointers, arrays, textures, and surfaces
- Employing various memory access modes: read-only, write-only, read-write, etc.
- Understanding memory consistency models and synchronization mechanisms
ROCm and HIP Execution Model
- Utilizing different execution structures: threads, blocks, and grids
- Using thread functions such as hipThreadIdx_x, hipBlockIdx_x, hipBlockDim_x, etc.
- Employing block functions like __syncthreads and __threadfence_block
- Utilizing grid functions such as hipGridDim_x, hipGridSync, and cooperative groups
Debugging
- Debugging ROCm and HIP programs on Windows
- Using Visual Studio Code debugger to inspect variables, breakpoints, call stacks, etc.
- Utilizing ROCm Debugger for debugging ROCm and HIP programs on AMD devices
- Analyzing ROCm and HIP programs on AMD devices using ROCm Profiler
Optimization
- Optimizing ROCm and HIP programs on Windows
- Applying coalescing techniques to enhance memory throughput
- Using caching and prefetching techniques to reduce memory latency
- Leveraging shared and local memory techniques to optimize memory access and bandwidth
- Utilizing profiling and profiling tools to measure and improve execution time and resource utilization
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.
- Practical experience with command-line tools and code editors.
- Familiarity with the Windows operating system and PowerShell.
Audience
- Developers seeking to learn how to install and use ROCm on Windows to program AMD GPUs and harness their parallelism.
- Developers aiming to write high-performance, scalable code capable of running across various AMD devices.
- Programmers interested in exploring the low-level aspects of GPU programming and optimizing code performance.
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