Task-based parallelism in scientific computing¶
- Abstract
The purpose of the course is to learn when a code could benefit from task-based parallelism, and how to apply it. A task-based algorithm comprises of a set of self-contained tasks that have well-defined inputs and outputs. This differs from the common practice of organizing an implementation into subroutines in that a task-based implementation does not call the associated computation kernels directly, instead it is the role of a runtime system to schedule the task to various computational resources, such as CPU cores and GPUs. One of the main benefits of this approach is that the underlying parallelism is exposed automatically as the runtime system gradually traverses the resulting task graph.
- Content
The course mainly focuses on the task-pragmas implemented in the newer incarnations of OpenMP. Other task-based runtime systems, e.g., StarPU, and GPU offloading are briefly discussed.
- Format
The course will be three half-days and comprises of lectures and hands-on sessions. This is an online-only course (Zoom).
- Audience
This HPC2N course is part of the PRACE Training courses. It is open for academics and people who work at industry in PRACE member countries.
- Date and Time
2021-05-{10,11,12}, 9:00-12:00
- Location
Online through Zoom
- Instructors
Mirko Myllykoski (mirkom@cs.umu.se)
- Helpers
Birgitte Brydsö, Pedro Ojeda-May
- Original author
Mirko Myllykoski (spring 2021)
- Recordings
https://www.youtube.com/playlist?list=PL6jMHLEmPVLyVIp67mW1cRj0xbL-6iFMY
- Registration
https://www.hpc2n.umu.se/events/courses/task-based-parallelism-spring-2021
Prerequisites
Basic knowledge of C programming language.
Basic knowledge of parallel programming.
Basic Linux skills.
Basic knowledge of OpenMP is beneficial but not required.
Day 1
Day 3
Miscellanous