@inproceedings{9835226,
  title = {A Scalable Parallel Partition Tridiagonal Solver for Many-Core and Low B/F Processors},
  author = {Mitsuda, Tatsuya and Ono, Kenji},
  booktitle = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
  year = {2022},
  month = {May},
  volume = {},
  number = {},
  pages = {860-869},
  doi = {10.1109/IPDPSW55747.2022.00142},
  abstract = {Tridiagonal systems are among the most fundamental computations in science, engineering, and mathematics, and one solver used in such systems is Tree Partitioning Reduction (TPR), which is a divide-and-conquer method that solves large-scale linear equations by dividing them and then computing the parts in parallel within different local memory threads. Herein, we propose an improved TPR algorithm that has a parallel cyclic reduction flavor, with which we reduced the number of algorithm steps by approximately half while simultaneously increasing arithmetic intensity and cache reusability. A performance evaluation conducted on an Intel Skylake-SP microprocessor showed a high hit ratio for the L1 cache and that our solver was as much as 31 times faster on 32 threads for 262144 equations. In the case of a Nvidia Tesla P100 GPU, our method processed 10 MRow/s more than TPR and cuSPARSE.},
  peer_reviewed = {true},
  issn = {},
  keywords = {Performance evaluation;Microprocessors;Scalability;Instruction sets;Memory management;Graphics processing units;Approximation algorithms;Tridiagonal system;GPU;Parallel computing;Multi/many-core},
}