On Advanced Monte Carlo Methods for Linear Algebra on Advanced Accelerator Architectures

Autor:	Lebedev, Anton, Alexandrov, Vassil
Rok vydání:	2024
Předmět:	Mathematics - Numerical Analysis Computer Science - Distributed Parallel and Cluster Computing G.1.3 G.3
Zdroj:	2018 IEEE/ACM 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (scalA), Dallas, TX, USA, 2018, pp. 81-90
Druh dokumentu:	Working Paper
DOI:	10.1109/ScalA.2018.00014
Popis:	In this paper we present computational experiments with the Markov Chain Monte Carlo Matrix Inversion ($(\text{MC})^2\text{MI}$) on several accelerator architectures and investigate their impact on performance and scalability of the method. The method is used as a preconditioner and for solving the corresponding system of linear equations iterative methods, such as generalized minimal residuals (GMRES) or bi-conjugate gradient (stabilized) (BICGstab), are used. Numerical experiments are carried out to highlight the benefits and deficiencies of both approaches and to assess their overall usefulness in light of scalability of the method. Comment: 10 pages, 8 figures, 2 pages IEEE artifact information, accepted to the 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.03095 Zobrazit plný text záznamu View this record from Arxiv