TY - JOUR
T1 - Hierarchical QR factorization algorithms for multi-core clusters
AU - Dongarra, Jack
AU - Faverge, Mathieu
AU - Hérault, Thomas
AU - Jacquelin, Mathias
AU - Langou, Julien
AU - Robert, Yves
PY - 2013
Y1 - 2013
N2 - This paper describes a new QR factorization algorithm which is especially designed for massively parallel platforms combining parallel distributed nodes, where a node is a multi-core processor. These platforms represent the present and the foreseeable future of high-performance computing. Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism). Each tile algorithm is uniquely characterized by its sequence of reduction trees. In the context of a cluster of nodes, in order to minimize the number of inter-processor communications (aka, "communication- avoiding"), it is natural to consider hierarchical trees composed of an "inter-node" tree which acts on top of "intra-node" trees. At the intra-node level, we propose a hierarchical tree made of three levels: (0) "TS level" for cache-friendliness, (1) "low-level" for decoupled highly parallel inter-node reductions, (2) "domino level" to efficiently resolve interactions between local reductions and global reductions. Our hierarchical algorithm and its implementation are flexible and modular, and can accommodate several kernel types, different distribution layouts, and a variety of reduction trees at all levels, both inter-node and intra-node. Numerical experiments on a cluster of multi-core nodes (i) confirm that each of the four levels of our hierarchical tree contributes to build up performance and (ii) build insights on how these levels influence performance and interact within each other. Our implementation of the new algorithm with the DAGUE scheduling tool significantly outperforms currently available QR factorization software for all matrix shapes, thereby bringing a new advance in numerical linear algebra for petascale and exascale platforms.
AB - This paper describes a new QR factorization algorithm which is especially designed for massively parallel platforms combining parallel distributed nodes, where a node is a multi-core processor. These platforms represent the present and the foreseeable future of high-performance computing. Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism). Each tile algorithm is uniquely characterized by its sequence of reduction trees. In the context of a cluster of nodes, in order to minimize the number of inter-processor communications (aka, "communication- avoiding"), it is natural to consider hierarchical trees composed of an "inter-node" tree which acts on top of "intra-node" trees. At the intra-node level, we propose a hierarchical tree made of three levels: (0) "TS level" for cache-friendliness, (1) "low-level" for decoupled highly parallel inter-node reductions, (2) "domino level" to efficiently resolve interactions between local reductions and global reductions. Our hierarchical algorithm and its implementation are flexible and modular, and can accommodate several kernel types, different distribution layouts, and a variety of reduction trees at all levels, both inter-node and intra-node. Numerical experiments on a cluster of multi-core nodes (i) confirm that each of the four levels of our hierarchical tree contributes to build up performance and (ii) build insights on how these levels influence performance and interact within each other. Our implementation of the new algorithm with the DAGUE scheduling tool significantly outperforms currently available QR factorization software for all matrix shapes, thereby bringing a new advance in numerical linear algebra for petascale and exascale platforms.
KW - Cluster
KW - Distributed memory
KW - Hierarchical architecture
KW - Multi-core
KW - Numerical linear algebra
KW - QR factorization
UR - http://www.scopus.com/inward/record.url?scp=84893660628&partnerID=8YFLogxK
U2 - 10.1016/j.parco.2013.01.003
DO - 10.1016/j.parco.2013.01.003
M3 - Article
AN - SCOPUS:84893660628
SN - 0167-8191
VL - 39
SP - 212
EP - 232
JO - Parallel Computing
JF - Parallel Computing
IS - 4-5
ER -