<span class="var-sub_title">Phase Asynchronous AMR Execution for Productive and Performant Astrophysical Flows</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Phase Asynchronous AMR Execution for Productive and Performant Astrophysical Flows


Authors: Muhammad Nufail Farooqi (Koc University), Tan Nguyen (Lawrence Berkeley National Laboratory), Weiqun Zhang (Lawrence Berkeley National Laboratory), Ann S. Almgren (Lawrence Berkeley National Laboratory), John Shalf (Lawrence Berkeley National Laboratory), Didem Unat (Koc University)

Abstract: Adaptive Mesh Refinement (AMR) is an approach to solving PDEs that reduces the computational and memory requirements at the expense of increased communication. Although adopting asynchronous execution can overcome communication issues, manually restructuring an AMR application to realize asynchrony is extremely complicated and hinders readability and long-term maintainability. To balance performance against productivity, we design a user-friendly API and adopt phase asynchronous execution model where all subgrids at an AMR level can be computed asynchronously.

We apply the phase asynchrony to transform a real-world AMR application, CASTRO, which solves multicomponent compressible hydrodynamic equations for astrophysical flows. We evaluate the performance and programming effort required to use our carefully designed API and execution model for transitioning large legacy codes from synchronous to asynchronous execution up to 278,528 Intel-KNL cores. CASTRO is about 100K lines of code but less than 0.2% code changes are required to achieve significant performance improvement.



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