Abstract
Incomplete LU (ILU) methods are widely used in petroleum reservoir simulation and many other applications. However high complexity often makes them the hotspot in the whole simulation due to high complexity when problem size is large. ILU’s inherent serial nature also makes them difficult to take full advantage of computing power of multi-core and many-core devices. In this paper, a greedy graph coloring method is applied to the ILU(k) factorization and triangular solution phases. This method increases degree of parallelism and improves load balance. A block-wise storage format is employed in our ILU implementation in order to take advantage of hierarchical memory structures. Moreover, a dual intensive parallel model is proposed to further improve the performance of ILU(k) on GPUs. We test the performance of the proposed parallel ILU(k) with a set of Jacobian systems arising from petroleum reservoir simulation. Numerical results suggest that the proposed parallel ILU(k) method is effective and robust on multi-core and many-core architectures. On an Intel Xeon E5 multi-core CPU, the speedup compared with the serial execution time is \(5.6\times \) and \(5.4\times \) for factorization and triangular solution, respectively; on an Nvidia K40c GPU card, the speedup can reach \(8.6\times \) and \(12.7\times \) for factorization and triangular solution, respectively.
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Acknowledgments
The paper is finished during Li’s visit to the State Key Laboratory of Scientific and Engineering Computing (LSEC), Academy of Mathematics and Systems Science. Li is thankful to the kind support from LSEC. The authors would like to thank Prof. Ludmil Zikatanov from the Pennsylvania State University and Dr. Xiang Li from the Peking University for many helpful discussions on ILU methods.
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Li, Z., Feng, C., Shu, S., Zhang, CS. (2017). Accelerating Reservoir Simulation on Multi-core and Many-Core Architectures with Graph Coloring ILU(k). In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-319-38789-5_31
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DOI: https://doi.org/10.1007/978-3-319-38789-5_31
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