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Highly Asynchronous and Scalable Algorithms for Distributed-Memory Adaptive Mesh Refinement at Extreme Scales
| Content Provider | Semantic Scholar |
|---|---|
| Author | Langer, Akhil Kalé, Laxmikant V. |
| Copyright Year | 2013 |
| Abstract | In this paper, we present our developments of a novel approach for distributed memory Adaptive Mesh Refinement (AMR). Our approach is highly asynchronous and fully distributed that makes it suitable for extreme-level scaling. It takes negligible memory to store mesh structure as compared to the traditional approaches which are not scalable. It accomplishes adaptive mesh restructuring in just 1 global collective call as compared to O(d) calls in the traditional approaches. A new distributed load balancer has been developed that led to an improvement in performance by 18%. We present our scaling results on up to 131, 072 cores of BG/Q supercomputer. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://charm.cs.illinois.edu/newPapers/13-58/shortpaper.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
