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Course Outline
Introduction
- What is OpenACC?
- OpenACC versus OpenCL, CUDA, and SYCL
- Overview of OpenACC features and architecture
- Setting up the development environment
Getting Started
- Creating an OpenACC project in Visual Studio Code
- Exploring project structure and files
- Compiling and running the program
- Displaying output using printf and fprintf
OpenACC Directives and Clauses
- Understanding OpenACC directives and clauses
- Using parallel directives to create parallel regions
- Using kernels directives for compiler-managed parallelism
- Using loop directives to parallelise loops
- Managing data movement with data directives
- Synchronising data with update directives
- Improving data reuse with cache directives
- Creating device functions with routine directives
- Synchronising events with wait directives
OpenACC API
- Understanding the role of the OpenACC API
- Querying device information and capabilities
- Setting device number and type
- Handling errors and exceptions
- Creating and synchronising events
OpenACC Libraries and Interoperability
- Understanding OpenACC libraries and interoperability
- Using math, random, and complex libraries
- Integrating with other models (CUDA, OpenMP, MPI)
- Integrating with GPU libraries (cuBLAS, cuFFT)
OpenACC Tools
- Understanding OpenACC tools in development
- Profiling and debugging OpenACC programs
- Performance analysis using PGI Compiler, NVIDIA Nsight Systems, and Allinea Forge
Optimisation
- Factors affecting OpenACC program performance
- Optimising data locality and reducing transfers
- Optimising loop parallelism and fusion
- Optimising kernel parallelism and fusion
- Optimising vectorisation and auto-tuning
Summary and Next Steps
Requirements
- A solid understanding of C/C++ or Fortran languages and parallel programming concepts.
- Basic knowledge of computer architecture and memory hierarchy.
- Experience with command-line tools and code editors.
Audience
- Developers wishing to learn how to use OpenACC to program heterogeneous devices and exploit their parallelism.
- Developers aiming to write portable and scalable code that operates across different platforms and devices.
- Programmers interested in exploring the high-level aspects of heterogeneous programming and optimising code productivity.
28 Hours
