swSuperLU: A highly scalable sparse direct solver on Sunway manycore architecture

Sparse LU factorization is essential for scientific and engineering simulations. In this work, we present swSuperLU, a highly scalable sparse direct solver on Sunway manycore architecture based on sparse LU factorization. To improve the parallelism of sparse LU factorization, we introduce the hierar...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 78; no. 9; pp. 11441 - 11463
Main Authors: Tian, Min, Wang, Junjie, Zhang, Zanjun, Du, Wei, Pan, Jingshan, Liu, Tao
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2022
Springer Nature B.V
Subjects:
Compilers
Computer architecture
Computer Science
Factorization
Interpreters
Microprocessors
Parallel processing
Parameters
Processor Architectures
Programming Languages
Solvers
Performance optimization
Parallel scalability
Sparse LU factorization
Sparse direct solver
Sunway manycore architecture
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:Sparse LU factorization is essential for scientific and engineering simulations. In this work, we present swSuperLU, a highly scalable sparse direct solver on Sunway manycore architecture based on sparse LU factorization. To improve the parallelism of sparse LU factorization, we introduce the hierarchical scheme to exploit the hierarchy of Sunway manycore architecture in process-level parallelism between MPEs and thread-level parallelism between the CPE arrays. A task-based hierarchical scheme and a series of highly optimized computation kernels are designed to map processor loads and memory access well to this hierarchy. Moreover, we compared various ordering strategies and several machine-dependent parameter settings to find the most suitable ordering strategies and parameter settings for Sunway manycore architecture. We present performance and scalability experiments of swSuperLU on Newest Generation Sunway Supercomputer and Sunway TaihuLight. swSuperLU achieves 9.02 × speedup on average compared to state-of-the-art packages and strong scalability from 10 thousand cores to million cores.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-04270-w