Proceedings of the fourth workshop on I/O in parallel and distributed systems: part of the federated computing research conference
Key concepts for parallel out-of-core LU factorization
Parallel Computing - Special double issue on environment and tools for parallel scientific computing
ScaLAPACK user's guide
Virtual Memory Management in Data Parallel Applications
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Optimization of the ScaLAPACK LU Factorization Routine Using Communication/Computation Overlap
Euro-Par '96 Proceedings of the Second International Euro-Par Conference on Parallel Processing-Volume II
LAPACK Working Note 95: ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers -- Design Issues and Performance
POOCLAPACK: Parallel Out-of-Core Linear Algebra Package
POOCLAPACK: Parallel Out-of-Core Linear Algebra Package
Parallel Out-of-Core Matrix Inversion
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Mixed Parallel Implementations of Strassen and Winograd Matrix Multiplication Algorithms
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
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In this paper, we present an analytical performance model of the parallel left-right looking out-of-core LU factorization algorithm. We show the accuracy of the performance prediction for a prototype implementation in the ScaLAPACK library. We will show that with a correct distribution of the matrix and with an overlapof IO by computation, we obtain performances similar to those of the in-core algorithm. To get such performances, the size of the physical main memory only need to be proportional to the product of the matrix order (not the matrix size) by the ratio of the IO bandwidth and the computation rate: There is no need of large main memory for the factorization of huge matrix!