TY - GEN
T1 - Implementing linear algebra routines on multi-core processors with pipelining and a look ahead
AU - Kurzak, Jakub
AU - Dongarra, Jack
PY - 2007
Y1 - 2007
N2 - Linear algebra algorithms commonly encapsulate parallelism in Basic Linear Algebra Subroutines (BLAS). This solution relies on the fork-join model of parallel execution, which may result in suboptimal performance on current and future generations of multi-core processors. To overcome the shortcomings of this approach a pipelined model of parallel execution is presented, and the idea of look ahead is utilized in order to suppress the negative effects of sequential formulation of the algorithms. Application to one-sided matrix factorizations, LU, Cholesky and QR, is described. Shared memory implementation using POSIX threads is presented.
AB - Linear algebra algorithms commonly encapsulate parallelism in Basic Linear Algebra Subroutines (BLAS). This solution relies on the fork-join model of parallel execution, which may result in suboptimal performance on current and future generations of multi-core processors. To overcome the shortcomings of this approach a pipelined model of parallel execution is presented, and the idea of look ahead is utilized in order to suppress the negative effects of sequential formulation of the algorithms. Application to one-sided matrix factorizations, LU, Cholesky and QR, is described. Shared memory implementation using POSIX threads is presented.
UR - http://www.scopus.com/inward/record.url?scp=38049005629&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-75755-9_18
DO - 10.1007/978-3-540-75755-9_18
M3 - Conference contribution
AN - SCOPUS:38049005629
SN - 9783540757542
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 156
BT - Applied Parallel Computing
PB - Springer Verlag
T2 - 8th International Workshop on Applied Parallel Computing, PARA 2006
Y2 - 18 June 2007 through 21 June 2007
ER -