ILYA LASHUK is a postdoctoral researcher at the Center for Applied Scientific Computing (CASC) at Lawrence Livermore National Laboratory (LLNL). He earned his Doctorate in Applied Mathematics from the University of Colorado Denver (Department of Mathematical Sciences). He also holds a Master of Science equivalent in Applied Mathematics from the Moscow State University in Russia, where he studied in the department of Computational Mathematics and Cybernetics. After completing his graduate studies, he worked for two years as a postdoctoral researcher at Georgia Institute of Technology before joining LLNL in 2010.
Ilya's research interests include multigrid methods, upscaling, fast multipole methods, and parallel computing.
Ilya's current project at LLNL is "Constrained energy minimization and Multigrid Methods".
PublicationsA. Rahimian, I. Lashuk, S. K.Veerapaneni, A. Chandramowlishwaran, D. Malhotra, L. Moon, R. Sampath, A. Shringarpure, J. Vetter, R.Vuduc, D. Zorin and G. Biros. Petascale direct numerical simulation of blood ﬂow on 200K cores and heterogeneous architectures. ACM/IEEE Conference on Supercomputing, 2010
Ilya Lashuk, Aparna Chandramowlishwaran, Harper Langston, Tuan-Anh Nguyen, Rahul Sampath, Aashay Shringarpure, Richard Vuduc, Lexing Ying, Denis Zorin, and George Biros. A massively parallel adaptive fast-multipole method on heterogeneous architectures. 2009 ACM/IEEE conference on Supercomputing.
Rahul S. Sampath, Santi S. Adavani, Hari Sundar, Ilya Lashuk, and George Biros. Dendro: parallel algorithms for multigrid and amr methods on 2:1 balanced octrees. In SC ’08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pages 1–12, Piscataway, NJ, USA, 2008. IEEE Press.
Ilya Lashuk and Panayot S. Vassilevski. On some versions of the element agglomeration AMGe method. Numer. Linear Algebra Appl., 15(7):595–620, 2008.
A. V. Knyazev, M. E. Argentati, I. Lashuk, and E. E. Ovtchinnikov. Block locally optimal preconditioned eigenvalue xolvers (BLOPEX) in hypre and PETSc. SIAM J. Sci. Comput., 29(5):2224–2239 (electronic), 2007.
Andrew V. Knyazev and Ilya Lashuk. Steepest descent and conjugate gradient methods with variable preconditioning. SIAM J. Matrix Anal. Appl., 29(4):1267–1280, 2007.