A set of level 3 basic linear algebra subprograms
ACM Transactions on Mathematical Software (TOMS)
Performance and scalability of finite element analysis for distributed parallel computation
Journal of Parallel and Distributed Computing
Applied Numerical Mathematics
Developments and trends in the parallel solution of linear systems
Parallel Computing - Special Anniversary issue
Distributed Schur Complement Techniques for General Sparse Linear Systems
SIAM Journal on Scientific Computing
Iterative solution of linear systems in the 20th century
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
Innovating Computational Methods for Structural Mechanics
Innovating Computational Methods for Structural Mechanics
The Matrix Template Library: Generic Components for High-Performance Scientific Computing
Computing in Science and Engineering
Overview of Iterative Linear System Solver Packages
Overview of Iterative Linear System Solver Packages
LAPACK Working Note 56: Reducing Communication Costs in the Conjugate Gradient Algorithm on Distributed Memory Multiprocessors
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A class of specialised data structures designed for the distributed solution of non-conventional finite element formulations, which are equally effective when used in conjunction with conventional formulations, is presented. We begin by briefly discussing how the non-conventional finite element formulations being developed within the structural analysis group at IST [Freitas JAT, Almeida JPM, Pereira EMBR. Non-conventional formulations for the finite element method. Comput Mech 1999;23(5-6):488-501] lead to systems of equations that appear to be naturally suited for parallel processing, but we also recognise that to take full advantage of the characteristics of these systems - large dimension, non-overlapping block structure and sparsity - it is necessary to use appropriate data structures. The approach presented, which references the logical subdivisions of the system matrices, was designed to fulfil these objectives. Examples of parallel performance and efficiency on an homogeneous distributed platform are presented.