Abstract
The full assembly of the stiffness matrix in finite element codes can be prohibitive in terms of memory footprint resulting from storing that enormous matrix. An optimisation and work around, particularly effective for discontinuous Galerkin based approaches, is to construct and solve the small dense linear systems locally within each element and avoid the global assembly entirely. The different independent linear systems can be solved concurrently in a batched manner, however we have found that
the memory subsystem can show destructive behaviour in this paradigm, severely affecting the performance. In this paper we demonstrate the range of performance that can be obtained by allocating the local systems differently, along with evidence to attribute the reasons behind these differences.
the memory subsystem can show destructive behaviour in this paradigm, severely affecting the performance. In this paper we demonstrate the range of performance that can be obtained by allocating the local systems differently, along with evidence to attribute the reasons behind these differences.
Original language | English |
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Title of host publication | 2020 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 8 |
ISBN (Electronic) | 978-0-7381-1066-0 |
ISBN (Print) | 978-0-7381-1067-7 |
DOIs | |
Publication status | Published - 20 Dec 2020 |
Event | 2020 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC) - Virtual event Duration: 11 Nov 2020 → 11 Nov 2020 https://passlab.github.io/mchpc/mchpc2020/ |
Conference
Conference | 2020 IEEE/ACM Workshop on Memory Centric High Performance Computing (MCHPC) |
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Period | 11/11/20 → 11/11/20 |
Internet address |
Keywords
- finite element analysis
- linear systems
- instruction sets
- reseource management
- runtime
- parallel processing
- bandwidth
- Galerkin method
- batched linear algebra
- cahe
- memory allocation