Multigrid is widely used as an efficient solver for sparse linear systems arising from the discretization of elliptic boundary value problems. Linear relaxation methods like Gauss-Seidel and Red-Black Gauss-Seidel form the principal computational component of multigrid, and thus affect its efficiency. In the context of multigrid, these iterative solvers are executed for a small number of iterations (2–8).We exploit this property of the algorithm to develop a cache-efficient multigrid, by focusing on improving the memory behavior of the linear relaxation methods. The efficiency in our cache-efficient linear relaxation algorithm comes from two sources: reducing the number of data cache and TLB misses, and reducing the number of memory references by keeping values register-resident. Experiments on five modern computing platforms show a performance improvement of 1.15–2.7 times over a standard implementation of Full Multigrid V-Cycle.
CITATION STYLE
Sellappa, S., & Chatterjee, S. (2001). Cache-efficient multigrid algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2073, pp. 107–116). Springer Verlag. https://doi.org/10.1007/3-540-45545-0_20
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