Performance Assessment of Hybrid Parallelism for Large-Scale Reservoir Simulation on Multi- and Many-core Architectures
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Applied Mathematics and Computational Science Program
Extreme Computing Research Center
Online Publication Date2018-12-08
Print Publication Date2018-09
Permanent link to this recordhttp://hdl.handle.net/10754/631262
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AbstractTwo trends are reshaping the landscape of petroleum reservoir simulators, one architecturally and one application driven: an increasing number of cores per node and increasing computational intensity arising from higher fidelity physics at each cell. Implicit algebraic solvers being the dominant kernels, we present hybrid MPI and OpenMP implementations of the linear solver of GigaPOWERS, a full-scale real-world massively parallel simulator for black oil and composition models. We also evaluate the impact of explicit communication and computation overlap by including the halo exchange in the task-dependency graph. We analyze the performance of these modifications across multi- and many-core architectures, i.e., Intel Haswell, Skylake, and Knights Landing, using a variety of synthetic and real-world models. The hybrid approach results in up to 50% reduction of time to solution on a 16 million-cell SPE10-like model on Skylake whereas on a smaller, 1 million-cell, model on Haswell and Knights Landing both implementations achieve very similar performance. In the real-world reservoir simulations, the hybrid parallelism has reduced communication volume, memory consumption, and improved load balancing.
CitationAlOnazi A, Rogowski M, Al-Zawawi A, Keyes D (2018) Performance Assessment of Hybrid Parallelism for Large-Scale Reservoir Simulation on Multi- and Many-core Architectures. 2018 IEEE High Performance extreme Computing Conference (HPEC). Available: http://dx.doi.org/10.1109/HPEC.2018.8547565.
Conference/Event name2018 IEEE High Performance Extreme Computing Conference, HPEC 2018