OpenMP is a directives-based approach to exploit the parallelism available in a model simulation or data analysis by using multiple cores (CPU or accelerator) that share the same memory space. This one day introduction will illustrate “shared memory” (whether your multicore laptop or a node on a supercomputer), recap parallel programming principles before participants work through a series of hands-on exercises to learn how to write their own parallel code and better understand what is possible (and some common pitfalls to avoid). Slides will also cover options for using OpenMP for GPUs.
Related Training
Also see our main training page
- introduction to thinking parallel
- getting started with FORTRAN
- designing and writing efficient programs
- the art of compilation (and how to use compilers to tell you what they're not doing but could be with your hints)
- introduction to MPI for multi-node processing (and for Xeon Phi)
- using profilers to improve the efficiency of your parallel code
- introduction to measuring the energy consumed by your model simulation for #greenerCompute
- batch schedules and running "high throughput" eg Monte Carlo
- Using MS Azure for high end compute
- use of directives for programming GPUs and Xeon Phi