Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Dynamic task fusion for a block-structured finite volume solver over a dynamically adaptive mesh with local time stepping

Li, Baojiu and Schulz, Holger and Weinzierl, Tobias and Han, Zhang (2022) 'Dynamic task fusion for a block-structured finite volume solver over a dynamically adaptive mesh with local time stepping.', in High Performance Computing 37th International Conference, ISC High Performance 2022, Hamburg, Germany, May 29 – June 2, 2022, Proceedings. , pp. 153-173. Lecture Notes in Computer Science., 13289

Abstract

Load balancing of generic wave equation solvers over dynamically adaptive meshes with local time stepping is dicult, as the load changes with every time step. Task-based programming promises to mitigate the load balancing problem. We study a Finite Volume code over dynamically adaptive block-structured meshes for two astrophysics simulations, where the patches (blocks) dene tasks. They are classied into urgent and low priority tasks. Urgent tasks are algorithmically latencysensitive. They are processed directly as part of our bulk-synchronous mesh traversals. Non-urgent tasks are held back in an additional task queue on top of the task runtime system. If they lack global side-eects, i.e. do not alter the global solver state, we can generate optimised compute kernels for these tasks. Furthermore, we propose to use the additional queue to merge tasks without side-eects into task assemblies, and to balance out imbalanced bulk synchronous processing phases.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 29 May 2023.
(AM) Accepted Manuscript
File format - PDF
(714Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-031-07312-0_8
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-031-07312-0_8
Date accepted:29 March 2022
Date deposited:18 August 2022
Date of first online publication:29 May 2022
Date first made open access:14 September 2022

Save or Share this output

Export:
Export
Look up in GoogleScholar