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Soft Computing

, Volume 22, Issue 24, pp 8187–8205 | Cite as

Parallel ILU preconditioners in GPU computation

  • Yan Chen
  • Xuhong Tian
  • Hui Liu
  • Zhangxin Chen
  • Bo Yang
  • Wenyuan Liao
  • Peng Zhang
  • Ruijian He
  • Min Yang
Methodologies and Application
  • 157 Downloads

Abstract

Accelerating large-scale linear solvers is always crucial for scientific research and industrial applications. In this regard, preconditioners play a key role in improving the performance of iterative linear solvers. This paper presents a summary and review of our work about the development of parallel ILU preconditioners on GPUs. The mechanisms of ILU(0), ILU(k), ILUT, enhanced ILUT, and block-wise ILU(k) are reviewed and analyzed, which give a clear guidance in the development of iterative linear solvers. ILU(0) is the most commonly used preconditioner, and the nonzero pattern of its matrix is exactly the same as the original matrix to be solved. ILU(k) uses k levels to control the pattern of its preconditioner matrix. ILUT selects entries for its preconditioner matrix by setting thresholds without considering its original matrix pattern. In addition to point-wise ILU preconditioners, a block-wise ILU(k) preconditioner is designed delicately in support of block-wise matrices. In implementation, the RAS (Restricted Additive Schwarz) method is adopted to optimize the parallel structure of a preconditioner matrix. Coupling with the configuration parameters of ILU preconditioners, a complex situation appears in the parallel solution process, so decoupled algorithms are adopted. These algorithms are implemented and tested on NVIDIA GPUs. The experiment results show that a single-GPU implementation can speed up an ILU preconditioner by a factor of 10, compared to traditional CPU implementation. The results also show that the ILU(0) has better speedup than ILU(k) but slower convergence than ILU(k). Level k of ILU(k) and threshold (pt) of ILUT are effective adjustment factors for controlling the equilibrium point between acceleration and convergence for ILU(k) and ILUT, respectively. All these ILU preconditioners are characterized and compared in this work, which shows a clear picture and numerical insights for practitioners in the ILU family.

Keywords

ILU Block-wise matrix Parallel computing GPU Preconditioner 

Notes

Acknowledgements

The support of Department of Chemical and Petroleum Engineering, University of Calgary and Reservoir Simulation Group, is gratefully acknowledged. The research is partly supported by NSERC/AIEE/Foundation CMG and AITF Chairs.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yan Chen
    • 3
  • Xuhong Tian
    • 3
  • Hui Liu
    • 1
  • Zhangxin Chen
    • 1
  • Bo Yang
    • 1
  • Wenyuan Liao
    • 2
  • Peng Zhang
    • 4
  • Ruijian He
    • 1
  • Min Yang
    • 1
  1. 1.Department of Chemical and Petroleum EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Mathematics and StatisticsUniversity of CalgaryCalgaryCanada
  3. 3.College of Mathematics and InformaticsSouth China Agricultural UniversityGuangzhouChina
  4. 4.Biomedical Engineering DepartmentStony Brook UniversityStony BrookUSA

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