On implementing MPI-IO portably and with high performance
Proceedings of the sixth workshop on I/O in parallel and distributed systems
Efficient Methods for Out-of-Core Sparse Cholesky Factorization
SIAM Journal on Scientific Computing
The Multifrontal Solution of Indefinite Sparse Symmetric Linear
ACM Transactions on Mathematical Software (TOMS)
A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling
SIAM Journal on Matrix Analysis and Applications
The design and implementation of a new out-of-core sparse cholesky factorization method
ACM Transactions on Mathematical Software (TOMS)
Adaptive paging for a multifrontal solver
Proceedings of the 18th annual international conference on Supercomputing
Task Scheduling in an Asynchronous Distributed Memory Multifrontal Solver
SIAM Journal on Matrix Analysis and Applications
Constructing memory-minimizing schedules for multifrontal methods
ACM Transactions on Mathematical Software (TOMS)
Hybrid scheduling for the parallel solution of linear systems
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
GPFS: a shared-disk file system for large computing clusters
FAST'02 Proceedings of the 1st USENIX conference on File and storage technologies
C++ Bindings to External Software Libraries with Examples from BLAS, LAPACK, UMFPACK, and MUMPS
ACM Transactions on Mathematical Software (TOMS)
Analysis of the solution phase of a parallel multifrontal approach
Parallel Computing
Reducing the I/O volume in an out-of-core sparse multifrontal solver
HiPC'07 Proceedings of the 14th international conference on High performance computing
Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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The memory usage of sparse direct solvers can be the bottleneck to solve large-scale problems. This paper describes a first implementation of an out-of-core extension to a parallel multifrontal solver (MUMPS). We show that larger problems can be solved on limited-memory machines with reasonable performance, and we illustrate the behaviour of our parallel out-of-core factorization. Then we use simulations to discuss how our algorithms can be modified to solve much larger problems.