ROCm for Windows Training Course
ROCm is an open-source platform designed for GPU programming, offering support for AMD GPUs while maintaining compatibility with CUDA and OpenCL. It provides programmers with direct access to hardware specifics, granting full control over the parallelization process. However, this level of control necessitates a solid understanding of device architecture, memory models, execution models, and optimization techniques.
ROCm for Windows is a recent advancement that allows users to install and utilize ROCm on the Windows operating system, which is widely used for both personal and professional purposes. This enables users to harness the power of AMD GPUs for a variety of applications, including artificial intelligence, gaming, graphics, and scientific computing.
This instructor-led, live training (available online or onsite) is tailored for beginner to intermediate-level developers who aim to install and use ROCm on Windows to program AMD GPUs and leverage their parallel processing capabilities.
By the end of this training, participants will be able to:
- Set up a development environment that includes the ROCm Platform, an AMD GPU, and Visual Studio Code on Windows.
- Create a basic ROCm program that performs vector addition on the GPU and retrieves the results from GPU memory.
- Use the ROCm API to query device information, allocate and deallocate device memory, copy data between host and device, launch kernels, and synchronize threads.
- Use the HIP language to write kernels that execute on the GPU and manipulate data.
- Utilize HIP built-in functions, variables, and libraries to perform common tasks and operations.
- Leverage ROCm and HIP memory spaces, such as global, shared, constant, and local, to optimize data transfers and memory accesses.
- Apply ROCm and HIP execution models to control threads, blocks, and grids that define parallelism.
- Debug and test ROCm and HIP programs using tools like the ROCm Debugger and ROCm Profiler.
- Optimize ROCm and HIP programs using techniques such as coalescing, caching, prefetching, and profiling.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and hands-on practice.
- Practical implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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 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 the output using printf and fprintf
ROCm API
- Using the ROCm API in the host program
- 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 the device program
- Writing kernels that execute on the GPU and manipulate data
- Using data types, qualifiers, operators, and expressions
- Utilizing 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.
- Understanding memory consistency models 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 the Visual Studio Code debugger to inspect variables, breakpoints, call stacks, etc.
- Using the ROCm Debugger to debug ROCm and HIP programs on AMD devices
- Using the 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
- Applying caching and prefetching techniques to reduce memory latency
- Using shared memory and local memory techniques to optimize memory accesses and bandwidth
- Using profiling tools to measure and improve execution time and resource utilization
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
- Familiarity with the Windows operating system and PowerShell
Audience
- Developers who wish to learn how to install and use ROCm on Windows to program AMD GPUs and exploit their parallelism
- Developers who aim to write high-performance and scalable code that can run on various AMD devices
- Programmers interested in exploring the low-level aspects of GPU programming and optimizing code performance
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