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Course Outline
Introduction
- What is ROCm?
- What is HIP?
- ROCm vs CUDA vs OpenCL
- Overview of ROCm and HIP features and architecture
- Setting up the Development Environment
Getting Started
- Creating a new ROCm project using Visual Studio Code
- Exploring the project structure and files
- Compiling and running the program
- Displaying the output using printf and fprintf
ROCm API
- Understanding the role of ROCm API in the host program
- Using ROCm API to query device information and capabilities
- Using ROCm API to allocate and deallocate device memory
- Using ROCm API to copy data between host and device
- Using ROCm API to launch kernels and synchronize threads
- Using ROCm API to handle errors and exceptions
HIP Language
- Understanding the role of HIP language in the device program
- Using HIP language to write kernels that execute on the GPU and manipulate data
- Using HIP data types, qualifiers, operators, and expressions
- Using HIP built-in functions, variables, and libraries to perform common tasks and operations
ROCm and HIP Memory Model
- Understanding the difference between host and device memory models
- Using ROCm and HIP memory spaces, such as global, shared, constant, and local
- Using ROCm and HIP memory objects, such as pointers, arrays, textures, and surfaces
- Using ROCm and HIP memory access modes, such as read-only, write-only, read-write, etc.
- Using ROCm and HIP memory consistency model and synchronization mechanisms
ROCm and HIP Execution Model
- Understanding the difference between host and device execution models
- Using ROCm and HIP threads, blocks, and grids to define the parallelism
- Using ROCm and HIP thread functions, such as hipThreadIdx_x, hipBlockIdx_x, hipBlockDim_x, etc.
- Using ROCm and HIP block functions, such as __syncthreads, __threadfence_block, etc.
- Using ROCm and HIP grid functions, such as hipGridDim_x, hipGridSync, cooperative groups, etc.
Debugging
- Understanding the common errors and bugs in ROCm and HIP programs
- 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
- Understanding the factors that affect the performance of ROCm and HIP programs
- Using ROCm and HIP coalescing techniques to improve memory throughput
- Using ROCm and HIP caching and prefetching techniques to reduce memory latency
- Using ROCm and HIP shared memory and local memory techniques to optimize memory accesses and bandwidth
- Using ROCm and HIP profiling and profiling tools to measure and improve the 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
Audience
- Developers who wish to learn how to use ROCm and HIP to program AMD GPUs and exploit their parallelism
- Developers who wish to write high-performance and scalable code that can run on different AMD devices
- Programmers who wish to explore the low-level aspects of GPU programming and optimize their code performance
28 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £7600 online delivery, based on a group of 2 delegates, £2400 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
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Testimonials (2)
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.
Jenny - Andheo
Course - GPU Programming with CUDA and Python
Trainers energy and humor.
