@article{30be8e85a9df49e9a44e22c3d91d28b6,
title = "A parallel hierarchical blocked adaptive cross approximation algorithm",
abstract = "This article presents a low-rank decomposition algorithm based on subsampling of matrix entries. The proposed algorithm first computes rank-revealing decompositions of submatrices with a blocked adaptive cross approximation (BACA) algorithm, and then applies a hierarchical merge operation via truncated singular value decompositions (H-BACA). The proposed algorithm significantly improves the convergence of the baseline ACA algorithm and achieves reduced computational complexity compared to the traditional decompositions such as rank-revealing QR. Numerical results demonstrate the efficiency, accuracy, and parallel scalability of the proposed algorithm.",
keywords = "Adaptive cross approximation, multilevel algorithms, parallelization, rank-revealing decomposition, singular value decomposition",
author = "Yang Liu and Wissam Sid-Lakhdar and Elizaveta Rebrova and Pieter Ghysels and Li, {Xiaoye Sherry}",
note = "Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US Department of Energy Office of Science and the National Nuclear Security Administration, and in part by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program through the FASTMath Institute under contract no. DE-AC02-05CH11231 at Lawrence Berkeley National Laboratory. Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US Department of Energy Office of Science and the National Nuclear Security Administration, and in part by the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program through the FASTMath Institute under contract no. DE-AC02-05CH11231 at Lawrence Berkeley National Laboratory. Funding Information: This research used resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under contract no. DE-AC02-05CH11231. Publisher Copyright: {\textcopyright} The Author(s) 2020.",
year = "2020",
month = jul,
day = "1",
doi = "10.1177/1094342020918305",
language = "English (US)",
volume = "34",
pages = "394--408",
journal = "International Journal of High Performance Computing Applications",
issn = "1094-3420",
publisher = "SAGE Publications Inc.",
number = "4",
}