Data distributions for sparse matrix vector multiplication
Parallel Computing
The design of a new frontal code for solving sparse, unsymmetric systems
ACM Transactions on Mathematical Software (TOMS)
Run-time compilation for parallel sparse matrix computations
ICS '96 Proceedings of the 10th international conference on Supercomputing
Solving large nonsymmetric sparse linear systems using MCSPARSE
Parallel Computing
Sparse code generation for imperfectly nested loops with dependences
ICS '97 Proceedings of the 11th international conference on Supercomputing
Vienna-Fortran/HPF Extensions for Sparse and Irregular Problems and Their Compilation
IEEE Transactions on Parallel and Distributed Systems
Supporting Irregular Distributions Using Data-Parallel Languages
IEEE Parallel & Distributed Technology: Systems & Technology
Sparse LU factorization on the CRAY T3D
HPCN Europe '95 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
HPCN Europe '95 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Exploiting locality on parallel irregular problem computations
PDP '95 Proceedings of the 3rd Euromicro Workshop on Parallel and Distributed Processing
A Supernodal Approach to Sparse Partial Pivoting
A Supernodal Approach to Sparse Partial Pivoting
| Hi-index | 0.00 |
There is a class of sparse matrix computations, such as direct solvers of systems of linear equations, that change the fill-in (nonzero entries) of the coefficient matrix, and involve row and column operations (pivoting). This paper addresses the problem of the parallelization of these sparse computations from the point of view of the parallel language and the compiler. Dynamic data structures for sparse matrix storage are analyzed, permitting to efficiently deal with fill-in and pivoting issues. Any of the data representations considered enforces the handling of indirections for data accesses, pointer referencing and dynamic data creation. All of these elements go beyond current data-parallel compilation technology. We propose a small set of new extensions to HPF-2 to parallelize these codes, supporting part of the new capabilities on a runtime library. This approach has been evaluated on a Cray T3E, implementing, in particular, the sparse LU factorization.