Using dual approximation algorithms for scheduling problems theoretical and practical results
Journal of the ACM (JACM)
Task scheduling for parallel sparse Cholesky factorization
International Journal of Parallel Programming
Generalised multiprocessor scheduling using optimal control
SPAA '91 Proceedings of the third annual ACM symposium on Parallel algorithms and architectures
A mapping algorithm for parallel sparse Cholesky factorization
SIAM Journal on Scientific Computing
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling
SIAM Journal on Matrix Analysis and Applications
Generalized Multiprocessor Scheduling and Applications to Matrix Computations
IEEE Transactions on Parallel and Distributed Systems
LAPACK Working Note 95: ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers -- Design Issues and Performance
Hybrid scheduling for the parallel solution of linear systems
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
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We present a new scheduling algorithm for task graphs arising from parallel multifrontal methods for sparse linear systems. This algorithm is based on the theorem proved by Prasanna and Musicus [1] for tree-shaped task graphs, when all tasks exhibit the same degree of parallelism. We propose extended versions of this algorithm to take communication between tasks and memory balancing into account. The efficiency of proposed approach is assessed by a set of experiments on a set of large sparse matrices from several libraries.