The WY representation for products of householder matrices
SIAM Journal on Scientific and Statistical Computing - Papers from the Second Conference on Parallel Processing for Scientific Computin
A storage-efficient WY representation for products of householder transformations
SIAM Journal on Scientific and Statistical Computing
Vector and parallel algorithms for Cholesky factorization on IBM 3090
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Matrix computations (3rd ed.)
Solving Linear Algebraic Equations on an MIMD Computer
Journal of the ACM (JACM)
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
New Generalized Data Structures for Matrices Lead to a Variety of High Performance Algorithms
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
LAPACK Working Note 95: ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers -- Design Issues and Performance
Parallel out-of-core computation and updating of the QR factorization
ACM Transactions on Mathematical Software (TOMS)
Supermatrix out-of-order scheduling of matrix operations for SMP and multi-core architectures
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Applying recursion to serial and parallel QR factorization leads to better performance
IBM Journal of Research and Development
The impact of multicore on math software
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Three algorithms for Cholesky factorization on distributed memory using packed storage
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Distributed QR factorization based on randomized algorithms
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Hi-index | 0.00 |
As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a major requirement and introduces the necessity of loose synchronization in the parallel execution of an operation. This paper presents an algorithm for the QR factorization where the operations can be represented as a sequence of small tasks that operate on square blocks of data. These tasks can be dynamically scheduled for execution based on the dependencies among them and on the availability of computational resources. Compared to the standard approach, say with LAPACK, may result in an out of order execution of the tasks which will completely hide the presence of intrinsically sequential tasks in the factorization. Performance comparisons are presented with the LAPACK algorithm for QR factorization where parallelism can only be exploited at the level of the BLAS operations.