|Maxim Naumov, Ph.D.
Sr. Research Scientist
Maxim Naumov joined NVIDIA Research in January 2015. His interests include parallel algorithms, numerical linear algebra, optimization and graphs. He contributes to Data Analytics nvGRAPH library. He has led the development of the AmgX library, which provides distributed Algebraic Multigrid, Krylov and Relaxation-based schemes. He has also worked on the cuBLAS, cuSPARSE and cuSOLVER(RF) libraries that are part of the CUDA Toolkit. In the past, Maxim held different positions at the NVIDIA Corporation CUDA Platform and Emerging Applications teams, the Intel Corporation Microprocessor Technology Lab and Computational Software Lab. He was also awarded 2008-09 Intel Foundation Ph.D. Fellowship during his graduate studies. He received his Ph.D. in Computer Science (with specialization in Computational Science and Engineering) in 2009 and his B.Sc. in Computer Science and Mathematics in 2003, all from Purdue University - West Lafayette.
|Research Interests: |
Parallel Algorithms, Numerical Linear Algebra, Optimization, Graphs (Google Scholar Publications)
S-Step and Communication-Avoiding Iterative Methods|
Parallel Spectral Graph Partitioning
AmgX: A Library for GPU Accelerated Algebraic Multigrid and Preconditioned Iterative Methods
Parallel Graph Coloring with Applications to the Incomplete-LU Factorization on the GPU
Preconditioned Block-Iterative Methods on GPUs
Incomplete-LU and Cholesky Factorization in the Preconditioned Iterative Methods on the GPU
Parallel Solution of Sparse Triangular Linear Systems in the Preconditioned Iterative Methods on the GPU