Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F231

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 489–492 (2017)

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Abstract. The aim of this paper is to investigate the impact of thread affinity on computing performance for matrix factorization on shared memory multicore systems with hierarchical memory. We consider two parallel block matrix factorizations (LU and WZ) and employ thread affinity to improve their performance. We study decomposition without pivoting and we compare differences between various affinity strategies for diagonally dominant matrices. Our results show that the choice of thread affinity has the measurable impact on the performance of the matrice factorizations.

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