STM-Multifrontal QR: Streaming Task Mapping Multifrontal QR Factorization Empowered by GCN

Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse matrix. In this work, a graph convolutional network (GCN) for adaptively selecting the optimal reordering algorithm is proposed in symbolic an...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:SC21: International Conference for High Performance Computing, Networking, Storage and Analysis s. 01 - 14
Hlavní autoři: Lin, Shengle, Yang, Wangdong, Wang, Haotian, Tsai, Qinyun, Li, Kenli
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 14.11.2021
Témata:
Approximation algorithms
Classification algorithms
Computer architecture
Graph Convolutional Network
High performance computing
Multicore processing
Multifrontal QR Factorization
NUMA Multicore Architecture
Parallel processing
Performance gain
Task Mapping Optimization
Task Stream Processing
ISSN:2167-4337
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Multifrontal QR algorithm, which consists of symbolic analysis and numerical factorization, is a high-performance algorithm for orthogonal factorizing sparse matrix. In this work, a graph convolutional network (GCN) for adaptively selecting the optimal reordering algorithm is proposed in symbolic analysis. Using our GCN adaptive classifier, the average numerical factorization time is reduced by 20.78% compared with the default approach, and the additional memory overhead is approximately 4% higher than that of prior work. Moreover, for numerical factorization, an optimized tasks stream parallel processing strategy is proposed and a more efficient computing task mapping framework for NUMA architecture is adopted in this paper, which called STM-Multifrontal QR factorization. Numerical experiments on the TaiShan Server show average 1.22x performance gains over the original SuiteSparseQR. Nearly 80% of datasets have achieved better performance compared with the MKL sparse QR on Intel Xeon 6248.
ISSN:2167-4337
DOI:10.1145/3458817.3476199