JISE


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Journal of Information Science and Engineering, Vol. 34 No. 6, pp. 1383-1403


Cache-Aware Out-of-Core Tensor Decomposition on GPUs


YU-TING TSAI, WEI-JHIH WANG AND TZU-YUAN KAO
Department of Computer Science and Engineering
Yuan Ze University
Taoyuan, 320 Taiwan
E-mail: hieicis91@hotmail.com; {plok00125; s815l7za}@gmail.com


For compressing large-scale multidimensional datasets, out-of-core tensor decomposition often consumes a lot of time. This article particularly presents a method based on two key ideas to improve its performance. First, cache-aware static scheduling schemes are employed to reduce the total number of disk accesses. Second, we take advantage of the massively parallel computing power and large memory size of modern GPUs to accelerate linear algebra operations of tensor decomposition. Our experiments demonstrate that the proposed method can achieve speedups of 11~16 over a naive implementation and 2.5~5.3 over previous work [43] for practical data-driven rendering applications.


Keywords: data-driven photorealistic rendering, multidimensional data analysis, tensor decomposition, out-of-core computation, general-purpose GPU computing

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