Randomized Singular Value Decomposition (SVD) is gaining attention in finding structure in scientific data. However, processing large-scale data is not easy due to the limited capacity of GPU memory. To deal with this issue, we propose RLAGPU, an out-of-core process method accelerating large-scale randomized SVD on GPU. The contribution of our method is as follows: l Out-of-core implementation that overcomes the GPU memory capacity limit. l High-performance. In-core and out-of-core routines switched automatically according to data size and available GPU memory. We found that our proposed method outperforms the existing cuBLAS-XT by a margin up to 50%
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