Finite element assembly strategies on multi‐core and many‐core architectures

Citations Over TimeTop 1% of 2012 papers

Abstract

SUMMARY We demonstrate that radically differing implementations of finite element methods (FEMs) are needed on multi‐core (CPU) and many‐core (GPU) architectures, if their respective performance potential is to be realised. Our numerical investigations using a finite element advection–diffusion solver show that increased performance on each architecture can only be achieved by committing to specific and diverse algorithmic choices that cut across the high‐level structure of the implementation. Making these commitments to achieve high performance for a single architecture leads to a loss of performance portability. Data structures that include redundant data but enable coalesced memory accesses are faster on many‐core architectures, whereas redundancy‐free data structures that are accessed indirectly are faster on multi‐core architectures. The Addto algorithm for global assembly is optimal on multi‐core architectures, whereas the Local Matrix Approach is optimal on many‐core architectures despite requiring more computation than the Addto algorithm. These results demonstrate the value in making the correct choice of algorithm and data structure when implementing FEMs, spectral element methods and low‐order discontinuous Galerkin methods on modern high‐performance architectures. Copyright © 2012 John Wiley & Sons, Ltd.

Related Papers

• → Tools and Environments for Multicore and Many-Core Architectures(2009)13 cited
• → Load balancing strategy for multicore systems(2015)4 cited
• → 2017 IEEE 11th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)(2017)4 cited
• → Proceedings of the 11th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures / 9th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms(2020)2 cited
• → Proceedings of the 10th and 8th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and Design Tools and Architectures for Multicore Embedded Computing Platforms(2019)