Proceedings of the fourth workshop on I/O in parallel and distributed systems: part of the federated computing research conference
ScaLAPACK user's guide
Performance Prediction and Analysis of Parallel Out-Of-Core Matrix Factorization
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Virtual Memory Management in Data Parallel Applications
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
POOCLAPACK: Parallel Out-of-Core Linear Algebra Package
POOCLAPACK: Parallel Out-of-Core Linear Algebra Package
On the performance of parallel factorization of out-of-core matrices
Parallel Computing
Efficiently using memory in lattice Boltzmann simulations
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
Out-of-Core and Pipeline Techniques for Wavefront Algorithms
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
A Fine-Grained Pipelined Implementation for Large-Scale Matrix Inversion on FPGA
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
| Hi-index | 0.00 |
This paper presents a parallel out-of-core algorithm to invert huge dense matrices, that is matrices larger than the available physical memory by one or more orders of magnitude. Preliminary performance results are shown for a commodity cluster. An accurate prediction performance model of the algorithm is given. Thanks to the prediction model, optimizations that avoid the overhead of the out-of-core algorithm are derived. Performance of the optimized algorithm using O(N) memory size are similar to the performance of the best known parallel in-core algorithm using O(N2) memory size (where N is the matrix order). There is no memory restriction for inversion of huge matrices!