On Optimizing Distributed Tucker Decomposition for Dense Tensors
| Autor: | Dheeraj Sreedhar, Douglas J. Joseph, Jee Choi, Xing Liu, Prakash Murali, Venkatesan T. Chakaravarthy, Yogish Sabharwal |
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| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
020203 distributed computing Mathematical optimization Computer science 010103 numerical & computational mathematics 02 engineering and technology 01 natural sciences Matrix decomposition Reduction (complexity) Matrix (mathematics) Computer Science - Distributed Parallel and Cluster Computing Product (mathematics) 0202 electrical engineering electronic engineering information engineering Tensor Distributed Parallel and Cluster Computing (cs.DC) 0101 mathematics Tucker decomposition |
| Zdroj: | IPDPS |
| DOI: | 10.48550/arxiv.1707.05594 |
| Popis: | The Tucker decomposition expresses a given tensor as the product of a small core tensor and a set of factor matrices. Apart from providing data compression, the construction is useful in performing analysis such as principal component analysis (PCA)and finds applications in diverse domains such as signal processing, computer vision and text analytics. Our objective is to develop an efficient distributed implementation for the case of dense tensors. The implementation is based on the HOOI (Higher Order Orthogonal Iterator) procedure, wherein the tensor-times-matrix product forms the core routine. Prior work have proposed heuristics for reducing the computational load and communication volume incurred by the routine. We study the two metrics in a formal and systematic manner, and design strategies that are optimal under the two fundamental metrics. Our experimental evaluation on a large benchmark of tensors shows that the optimal strategies provide significant reduction in load and volume compared to prior heuristics, and provide up to 7x speed-up in the overall running time. Comment: Preliminary version of the paper appears in the proceedings of IPDPS'17 |
| Databáze: | OpenAIRE |
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