The multifrontal method and paging in sparse Cholesky factorization
ACM Transactions on Mathematical Software (TOMS)
The role of elimination trees in sparse factorization
SIAM Journal on Matrix Analysis and Applications
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)
Compiler-based I/O prefetching for out-of-core applications
ACM Transactions on Computer Systems (TOCS)
Virtual Memory Management in Data Parallel Applications
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Tools for the Development of Application Specific Virtual Memory
Tools for the Development of Application Specific Virtual Memory
Impact of reordering on the memory of a multifrontal solver
Parallel Computing - Parallel matrix algorithms and applications (PMAA '02)
On the performance of parallel factorization of out-of-core matrices
Parallel Computing
Improving performance by embedding HPC applications in lightweight Xen domains
Proceedings of the 2nd workshop on System-level virtualization for high performance computing
A preliminary out-of-core extension of a parallel multifrontal solver
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
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In this paper, we present a new way to improve performance of the factorization of large sparse linear systems which cannot fit in memory. Instead of rewriting a large part of the code to implement an out-of-core algorithm with explicit I/O, we modify the paging mechanisms in such a way that I/O are transparent. This approach will be helpful to study the key points for getting performance with large problems on under sized memory machines with an explicit out-of-core scheme. The modification is done thanks to the MMUM&MMUSSEL software tool which allows the management of the paging activity at the application level. We designed a first paging polic that is well adapted for the parallel multifrontal solver MUMPS We present here a study and we give our preliminary results.