Tuesday, July 18, 2017

RLAGPU: High-performance Out-of-Core Randomized Singular Value Decomposition on GPU - implementation -

When the GPU cannot handle your randomized SVD:. 

RLAGPU: High-performance Out-of-Core Randomized Singular Value Decomposition on GPU by Yuechao Lu, Fumihiko Ino, Yasuyuki Matsushita and Kenichi Hagihara

Randomized Singular Value Decomposition (SVD)[1] 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%

An implementation is here: https://github.com/luyuechao/

Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments: