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20172025
most citedKokkos Kernels: Performance Portable Sparse/Dense Linear Algebra and Graph Kernels

26 citations · 68 across the 24 of their papers we have counts for

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7 papers · 1 filter

math.NA2025

ShyLU node: On-node Scalable Solvers and Preconditioners Recent Progresses and Current Performance

Ichitaro Yamazaki, Nathan Ellingwood, Sivasankaran Rajamanickam

ShyLU-node is an open-source software package that implements linear solvers and preconditioners on shared-memory multicore CPUs or on a GPU. It is part of the Trilinos software fr…

math.NA2023

An Experimental Study of Two-Level Schwarz Domain Decomposition Preconditioners on GPUs

Ichitaro Yamazaki, Alexander Heinlein, Sivasankaran Rajamanickam

The generalized Dryja--Smith--Widlund (GDSW) preconditioner is a two-level overlapping Schwarz domain decomposition (DD) preconditioner that couples a classical one-level overlappi…

math.NA2021

Experimental Evaluation of Multiprecision Strategies for GMRES on GPUs

Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki +2

Support for lower precision computation is becoming more common in accelerator hardware due to lower power usage, reduced data movement and increased computational performance. How…

math.NA20213 cited

Two-Stage Gauss--Seidel Preconditioners and Smoothers for Krylov Solvers on a GPU cluster

Luc Berger-Vergiat, Brian Kelley, Sivasankaran Rajamanickam +5

Gauss-Seidel (GS) relaxation is often employed as a preconditioner for a Krylov solver or as a smoother for Algebraic Multigrid (AMG). However, the requisite sparse triangular solv…

math.NA2019

An Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank Approximations

Léopold Cambier, Chao Chen, Erik G Boman +3

We propose a new algorithm for the fast solution of large, sparse, symmetric positive-definite linear systems, spaND -- sparsified Nested Dissection. It is based on nested dissecti…

math.NA2018

Asynchronous One-Level and Two-Level Domain Decomposition Solvers

Christian Glusa, Paritosh Ramanan, Erik G. Boman +2

Parallel implementations of linear iterative solvers generally alternate between phases of data exchange and phases of local computation. Increasingly large problem sizes on more h…