Paper Info
Title
Task Scheduling in an Asynchronous Distributed Memory Multifrontal Solver
Abstract
We describe the improvements to the task scheduling for MUMPS, an asynchronous distributed memory direct solver for sparse linear systems. In the new approach, we determine, during the analysis of the matrix, candidate processes for the tasks that will be dynamically scheduled during the subsequent factorization. This approach significantly improves the scalability of the solver in terms of execution time and storage. By comparison with the previous version of MUMPS, we demonstrate the efficiency and the scalability of the new algorithm on up to 512 processors. Our test cases include matrices from regular three-dimensional grids and irregular grids from real-life applications.
Year
DOI
Venue
2004
10.1137/S0895479802419877
SIAM J. Matrix Analysis Applications
Keywords
Field
DocType
sparse linear system,execution time,task scheduling. amsmos subject classications: 65f05,real-life application,65f35,irregular grid,mumps,new algorithm,new approach,memory direct solver,task scheduling,high performance computing,regular three-dimensional grid,memory multifrontal solver,multifrontal gaussian elimination,previous version,distributed memory code,sparse linear systems,65f50.,candidate process,distributed memory,gaussian elimination,dynamic scheduling
Asynchronous communication,Mathematical optimization,Supercomputer,Scheduling (computing),Sparse approximation,Parallel computing,Distributed memory,Theoretical computer science,Test case,Solver,Mathematics,Scalability
Journal
Volume
Issue
ISSN
26
2
0895-4798
Citations 
PageRank 
References 
8
1.64
15
Authors
3
Name
Order
Citations
PageRank
Patrick R. Amestoy144644.24
Iain S. Duff21107148.90
Christof Vömel316817.80