
@article{superlu_v8,
author = {Li, Xiaoye S. and Lin, Paul and Liu, Yang and Sao, Piyush},
title = {Newly Released Capabilities in the Distributed-Memory SuperLU Sparse Direct Solver},
year = {2023},
issue_date = {March 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {49},
number = {1},
issn = {0098-3500},
url = {https://doi.org/10.1145/3577197},
doi = {10.1145/3577197},
abstract = {We present the new features available in the recent release of SuperLU_DIST, Version 8.1.1. SuperLU_DIST is a distributed-memory parallel sparse direct solver. The new features include (1) a 3D communication-avoiding algorithm framework that trades off inter-process communication for selective memory duplication, (2) multi-GPU support for both NVIDIA GPUs and AMD GPUs, and (3) mixed-precision routines that perform single-precision LU factorization and double-precision iterative refinement. Apart from the algorithm improvements, we also modernized the software build system to use CMake and Spack package installation tools to simplify the installation procedure. Throughout the article, we describe in detail the pertinent performance-sensitive parameters associated with each new algorithmic feature, show how they are exposed to the users, and give general guidance of how to set these parameters. We illustrate that the solver’s performance both in time and memory can be greatly improved after systematic tuning of the parameters, depending on the input sparse matrix and underlying hardware.},
journal = {ACM Trans. Math. Softw.},
month = mar,
articleno = {10},
numpages = {20},
keywords = {mixed precision, GPU, communication-avoiding, Sparse direct solver}
}