Parallel Preconditioning with Sparse Approximate Inverses
SIAM Journal on Scientific Computing
Multigrid Method for Maxwell's Equations
SIAM Journal on Numerical Analysis
Developments and trends in the parallel solution of linear systems
Parallel Computing - Special Anniversary issue
High Performance Cluster Computing: Programming and Applications
High Performance Cluster Computing: Programming and Applications
Global and localized parallel preconditioning techniques for large scale solid Earth simulations
Future Generation Computer Systems - Selected papers from CCGRID 2002
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Fine-Grain Numerical Computations in Dynamic SMP Clusters with Communication on the Fly
PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering
NoC Synthesis Flow for Customized Domain Specific Multiprocessor Systems-on-Chip
IEEE Transactions on Parallel and Distributed Systems
The use of configurable computing for computational kernels in scientific simulations
Future Generation Computer Systems
Parallel iterative solvers for sparse linear systems in circuit simulation
Future Generation Computer Systems
Self-adapting numerical software and automatic tuning of heuristics
ICCS'03 Proceedings of the 2003 international conference on Computational science
Self-adapting numerical software and automatic tuning of heuristics
ICCS'03 Proceedings of the 2003 international conference on Computational science
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The paper presents a comparative analysis of parallel implementation of the preconditioned conjugate gradient method with the symmetric-successive over-relaxation preconditioner. Two parallel implementations of the matrix solver are compared. The first one is a message-passing version executed on a cluster of workstations. The other one is an efficient version simulated on a novel architecture of dynamically reconfigurable shared memory clusters with a new paradigm of inter-processor communication called communication on the fly. The presented example shows high suitability of the proposed architecture for fine grain numerical computations. It can be very useful in the simulation of physical phenomena described as numerical problems suitable for fine grain parallel execution.