Abstract:Efficient solutions of large-scale, ill-conditioned and indefinite algebraic equations are ubiquitously needed in numerous computational fields, including multiphysics simulations, machine learning, and data science. Because of their robustness and accuracy, direct solvers are crucial components in building a scalable solver toolchain. In this chapter, we will review recent advances of sparse direct solvers along two axes: 1) reducing communication and latency costs in both task- and data-parallel settings, and 2) reducing computational complexity via low-rank and other compression techniques such as hierarchical matrix algebra. In addition to algorithmic principles, we also illustrate the key parallelization challenges and best practices to deliver high speed and reliability on modern heterogeneous parallel machines.
| Subjects: | Mathematical Software (cs.MS); Distributed, Parallel, and Cluster Computing (cs.DC); Numerical Analysis (math.NA) |
| Cite as: | arXiv:2602.14289 [cs.MS] |
| (or arXiv:2602.14289v2 [cs.MS] for this version) | |
| https://doi.org/10.48550/arXiv.2602.14289 arXiv-issued DOI via DataCite |
From: Yang Liu [view email]
[v1]
Sun, 15 Feb 2026 19:40:14 UTC (3,725 KB)
[v2]
Fri, 22 May 2026 15:57:25 UTC (13,323 KB)
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