PSPASES (Parallel SPArse Symmetric dirEct Solver) is a high performance, scalable, parallel, MPI-based library, intended for solving linear systems of equations involving sparse symmetric positive definite matrices. The library provides various interfaces to solve the system using four phases of direct method of solution : compute fill-reducing ordering, perform symbolic factorization, compute numerical factorization, and solve triangular systems of equations. The library efficiently implements the scalable parallel algorithms developed by the authors, to compute each of the phases [GKK , JGKK , GGJKK , KK].
A user's manual is supplied with the distribution. It explains in detail, the formats of input and output parameters and the calling sequences for various functions provided. Here is a copy of the PSPASES user's manual [PostScript | PDF].
Read the Install Instructions first after downloading and uncompressing the library. It has instructions for building and testing the library. Read Usage Notes which may help you to get most out of PSPASES. It answers some FAQs and explains various matrix formats accepted by the command line interfaces provided. The format specifications for two of the supported formats can be found in these files : Harwell-Boeing format and Rutherford-Boeing format .
PSPASES uses the standard BLAS, LAPACK, and MPI library calls for its functionality. Tuned versions of these libraries are recommended to get good performance out of PSPASES. Public domain vanilla implementations of BLAS and LAPACK routines are available on the web. A public domain implementation of MPI standard is available at MPICH site .
Also, a faster version of PSPASES, with enhanced functionality, is available
for the IBM SP and RS6000 systems, as WSMP. It can solve symmetric positive
definite as well as indefinite systems. For more information, please visit
Designed and developed the algorithm for parallel numerical factorization phase. Helped in designing the triangular solution and symbolic factorization phases.
Designed and developed algorithms for parallel symbolic factorization and parallel triangular solution phases, and integrated all the four phases of the solver to make it portable and useable in its current format.
Designed and developed parallel algorithm for computing fill-reducing ordering.
Helped in designing algorithms for all the four phases of the solver.
Supported the development of solver.
Please contact Mahesh Joshi
for any usage related questions or comments as well as to report
any abnormal behavior which could be possibly related to
bugs that are not discovered so far.
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