Gauss-Seidel is an iterative computation used for solving sets of simulataneous linear equations, $Au=f$. When these unknowns are associated with nodes in an irregular mesh, then the Gauss-Seidel computation structure is related to the mesh structure. We use this structure to subdivide the computation at runtime using a technique called {\em sparse tiling}. The rescheduled computation exhibits better data locality and therefore improved performance. This paper gives a complete proof that a serial schedule based on sparse tiling generates results equivalent to those that a standard Gauss-Seidel computation produces.Pre-2018 CSE ID: CS2001-069
Abstract—Many scientific applications are organized in a data parallel way: as sequences of parallel...
Publication rights licensed to ACM. Sparse tiling is a technique to fuse loops that access common da...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
Gauss-Seidel is an iterative computation used for solving sets of simulataneous linear equations, $A...
Finite Element problems are often solved using multigrid techniques. The most time consuming part of...
Abstract: In order to optimize data locality, communication and synchronization overhead, this pape...
Gauss-Seidel is a popular multigrid smoother as it is provably optimal on structured grids and exhib...
In geometry processing, numerical optimization methods often involve solving sparse linear systems o...
Analysis of real life problems often results in linear systems of equations for which solutions are ...
Gauss Seidel algorithm for solving iteratively system of equations is usually categorised as an intr...
Today most real life applications require processing large amounts of data (i.e. ”Big Data”). The pa...
The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...
SIGLETIB Hannover: RN 8680(37) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische Informa...
In this paper, we suggest a generalized Gauss-Seidel approach to sparse linear and nonlinear least-s...
The mathematical models of many practical problems lead to systems of linear algebraic equations wh...
Abstract—Many scientific applications are organized in a data parallel way: as sequences of parallel...
Publication rights licensed to ACM. Sparse tiling is a technique to fuse loops that access common da...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
Gauss-Seidel is an iterative computation used for solving sets of simulataneous linear equations, $A...
Finite Element problems are often solved using multigrid techniques. The most time consuming part of...
Abstract: In order to optimize data locality, communication and synchronization overhead, this pape...
Gauss-Seidel is a popular multigrid smoother as it is provably optimal on structured grids and exhib...
In geometry processing, numerical optimization methods often involve solving sparse linear systems o...
Analysis of real life problems often results in linear systems of equations for which solutions are ...
Gauss Seidel algorithm for solving iteratively system of equations is usually categorised as an intr...
Today most real life applications require processing large amounts of data (i.e. ”Big Data”). The pa...
The problem of finding sparse solutions to underdetermined systems of linear equations is very commo...
SIGLETIB Hannover: RN 8680(37) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische Informa...
In this paper, we suggest a generalized Gauss-Seidel approach to sparse linear and nonlinear least-s...
The mathematical models of many practical problems lead to systems of linear algebraic equations wh...
Abstract—Many scientific applications are organized in a data parallel way: as sequences of parallel...
Publication rights licensed to ACM. Sparse tiling is a technique to fuse loops that access common da...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...