Parallel Reservoir Simulation with OpenACC and Domain Decomposition

Kang, Zhi‐Hui; Deng, Ze; Han, Wei; Zhang, Dongmei

doi:10.3390/a11120213

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…As multi-core and many-core architectures become more and more popular, parallel computing for petroleum reservoir simulation is now a subject of great interest. In recent years, there has been some work on parallel algorithms for reservoir problems; see [33][34][35][36][37][38][39][40][41][42][43][44][45] and references therein. For example, Feng et al [35] designed an OpenMP parallel algorithm with high efficiency and low memory cost for standard interpolation and coarse grid operator of AMG, under the framework of Fast Auxiliary Space Preconditioning (FASP) [46].…”

mentioning

confidence: 99%

Parallel Multi-Stage Preconditioners with Adaptive Setup for the Black Oil Model

Zhao,

Feng,

Zhang

et al. 2022

Preprint

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The black oil model is widely used to describe multiphase porous media flow in the petroleum industry. The fully implicit method features strong stability and weak constraints on time step-sizes; hence, commonly used in the current mainstream commercial reservoir simulators. In this paper, a CPRtype preconditioner with an adaptive "setup phase" is developed to improve parallel efficiency of petroleum reservoir simulation. Furthermore, we propose a multi-color Gauss-Seidel (GS) algorithm for algebraic multigrid method based on the coefficient matrix of strong connections. Numerical experiments show that the proposed preconditioner can improve the parallel performance for both OpenMP and CUDA implements. Moreover, the proposed algorithm yields good parallel speedup as well as same convergence behavior as the corresponding single-threaded algorithm. In particular, for a three-phase benchmark problem, the parallel speedup of the OpenMP version is over 6.5 with 16 threads and the CUDA version reaches more than 9.5.

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mentioning

confidence: 99%

Parallel Multi-Stage Preconditioners with Adaptive Setup for the Black Oil Model

Zhao,

Feng,

Zhang

et al. 2022

Preprint

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“…Their experimental results have shown that their hybrid CPU-GPU solution outperforms the CPU-only and GPU-only implementations. Kang et al [11] proposed a parallel algorithm implemented with OpenACC to use a GPU to parallelize the reservoir simulations. The experimental results show that the proposed approach outperform the CPU-based one while preserving the small programming effort for porting the algorithm to a parallel execution on a GPU.…”

Section: Remote Sensing and Dta Algorithms Implementation On A Gpumentioning

confidence: 99%

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

Stojanović

2019

IJGI

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Watershed analysis, as a fundamental component of digital terrain analysis, is based on the Digital Elevation Model (DEM), which is a grid (raster) model of the Earth surface and topography. Watershed analysis consists of computationally and data intensive computing algorithms that need to be implemented by leveraging parallel and high-performance computing methods and techniques. In this paper, the Multiple Flow Direction (MFD) algorithm for watershed analysis is implemented and evaluated on multi-core Central Processing Units (CPU) and many-core Graphics Processing Units (GPU), which provides significant improvements in performance and energy usage. The implementation is based on NVIDIA CUDA (Compute Unified Device Architecture) implementation for GPU, as well as on OpenACC (Open ACCelerators), a parallel programming model, and a standard for parallel computing. Both phases of the MFD algorithm (i) iterative DEM preprocessing and (ii) iterative MFD algorithm, are parallelized and run over multi-core CPU and GPU. The evaluation of the proposed solutions is performed with respect to the execution time, energy consumption, and programming effort for algorithm parallelization for different sizes of input data. An experimental evaluation has shown not only the advantage of using OpenACC programming over CUDA programming in implementing the watershed analysis on a GPU in terms of performance, energy consumption, and programming effort, but also significant benefits in implementing it on the multi-core CPU.

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“…Due to its high float-point performance and memory bandwidth [21], it has great potential in petroleum reservoir simulation. In recent years, research on GPU parallel algorithms has been developed in [22][23][24][25][26][27][28] and the references therein. For example, Chen et al [23,24] developed a hybrid sparse matrix storage format and the corresponding sparse matrix-vector multiplication (SpMV) kernel.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Chen et al [23,24] developed a hybrid sparse matrix storage format and the corresponding sparse matrix-vector multiplication (SpMV) kernel. Kang et al [25] developed a parallel nonlinear solver based on Ope-nACC [29] using the domain decomposition method to achieve load balancing. Finally, Manea et al [26] studied a parallel algebraic multiscale solver on GPU architectures to improve the solution efficiency of the pressure equation.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Stage Preconditioner on GPUs for Compositional Reservoir Simulation

Zhao¹,

Zhang²,

Feng³

et al. 2022

Preprint

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The compositional model is often used to describe multicomponent multiphase porous media flows in the petroleum industry. The fully implicit method with strong stability and weak constraints on timestep sizes is commonly used in the mainstream commercial reservoir simulators. In this paper, we develop an efficient multi-stage preconditioner for the fully implicit compositional flow simulation. The method employs an adaptive setup phase to improve the parallel efficiency on GPUs. Furthermore, a multi-color Gauss-Seidel algorithm based on the adjacency matrix is applied in the algebraic multigrid methods for the pressure part. Numerical results demonstrate that the proposed algorithm achieves good parallel speedup while yields the same convergence behavior as the corresponding sequential version.

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Parallel Reservoir Simulation with OpenACC and Domain Decomposition

Cited by 4 publications

References 21 publications

Parallel Multi-Stage Preconditioners with Adaptive Setup for the Black Oil Model

Parallel Multi-Stage Preconditioners with Adaptive Setup for the Black Oil Model

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

An Improved Multi-Stage Preconditioner on GPUs for Compositional Reservoir Simulation

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