ROCm for Windows Training Course
ROCm is an open-source platform designed for GPU programming that supports AMD GPUs and ensures compatibility with both CUDA and OpenCL. This platform provides developers with direct access to hardware details, granting full control over the parallelization process. However, effective utilization demands a solid grasp of device architecture, memory models, execution models, and various optimization strategies.
ROCm for Windows represents a significant advancement, enabling users to install and operate ROCm on the Windows operating system, a widely adopted choice for both personal and professional use. This capability allows users to harness the power of AMD GPUs for diverse applications, including artificial intelligence, gaming, graphics processing, and scientific computing.
This instructor-led live training, available either online or onsite, targets beginner to intermediate developers who intend to install and utilize ROCm on Windows to program AMD GPUs and leverage their parallel processing capabilities.
Upon completion of this training, participants will be capable of:
- Establishing a development environment comprising the ROCm Platform, an AMD GPU, and Visual Studio Code on Windows.
- Developing a fundamental ROCm program that executes vector addition on the GPU and retrieves results from GPU memory.
- Utilizing the ROCm API to query device information, allocate and deallocate device memory, transfer data between host and device, launch kernels, and synchronize threads.
- Employing the HIP language to write kernels that run on the GPU and manipulate data.
- Using HIP built-in functions, variables, and libraries to carry out common tasks and operations.
- Leveraging ROCm and HIP memory spaces—such as global, shared, constant, and local—to optimize data transfers and memory access.
- Applying ROCm and HIP execution models to manage the threads, blocks, and grids that define parallelism.
- Debugging and testing ROCm and HIP programs using tools like ROCm Debugger and ROCm Profiler.
- Optimizing ROCm and HIP programs through techniques such as coalescing, caching, prefetching, and profiling.
Course Format
- Interactive lecture and discussion.
- Extensive exercises and practice 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?
- ROCm vs CUDA vs OpenCL
- Overview of ROCm and HIP features and architecture
- ROCm for Windows vs ROCm for Linux
Installation
- Installing ROCm on Windows
- Verifying the installation and check the device compatibility
- Updating or uninstall 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 run the program
- Displaying the output using printf and fprintf
ROCm API
- Using ROCm API in the host program
- Querying device information and capabilities
- Allocating and deallocate device memory
- Copying data between host and device
- Launching kernels and synchronize threads
- Handling errors and exceptions
HIP Language
- Using HIP language in the device program
- Writing kernels that execute on the GPU and manipulate data
- Using data types, qualifiers, operators, and expressions
- Using built-in functions, variables, and libraries
ROCm and HIP Memory Model
- Using different memory spaces, such as global, shared, constant, and local
- Using different memory objects, such as pointers, arrays, textures, and surfaces
- Using different memory access modes, such as read-only, write-only, read-write, etc.
- Using memory consistency model and synchronization mechanisms
ROCm and HIP Execution Model
- Using different execution models, such as threads, blocks, and grids
- Using thread functions, such as hipThreadIdx_x, hipBlockIdx_x, hipBlockDim_x, etc.
- Using block functions, such as __syncthreads, __threadfence_block, etc.
- Using grid functions, such as hipGridDim_x, hipGridSync, cooperative groups, etc.
Debugging
- Debugging ROCm and HIP programs on Windows
- Using Visual Studio Code debugger to inspect variables, breakpoints, call stack, etc.
- Using ROCm Debugger to debug ROCm and HIP programs on AMD devices
- Using ROCm Profiler to analyze ROCm and HIP programs on AMD devices
Optimization
- Optimizing ROCm and HIP programs on Windows
- Using coalescing techniques to improve memory throughput
- Using caching and prefetching techniques to reduce memory latency
- Using shared memory and local memory techniques to optimize memory accesses and bandwidth
- Using profiling and profiling tools to measure and improve the execution time and resource utilization
Summary and Next Steps
Requirements
- A firm understanding of the C/C++ language and parallel programming concepts
- Basic knowledge of computer architecture and memory hierarchy
- 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 exploit their parallelism
- Developers aiming to write high-performance, scalable code compatible with various AMD devices
- Programmers interested in exploring the low-level aspects of GPU programming and optimizing code performance
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