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Fourth EAGE Workshop on High Performance Computing for Upstream 2019
- Conference date: October 7-9, 2019
- Location: Dubai, UAE
- Published: 07 October 2019
21 - 23 of 23 results
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GPU Implementation of Line Solve Power Series Preconditioner used in Reservoir Simulation
Authors O. Hajjar, A. Alturki and M. BaddoumaSummaryThe objective of the work presented in is to share our experience in a Proof-of-Concept (PoC) that was conducted to assess the feasibility of offloading heavy computation modules of numerical reservoir simulation on GPUs
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Accelerating seismic parameter estimation with Adaptive Differential Evolution (JADE) and graphics processing units (GPUs)
Authors J. Ribeiro, N.T. Okita, T.A. Coimbra and M. TygelSummaryThrough the last decades, multiparametric traveltime has been used as an efficient technique for inversion and seismic imaging. Despite its efficiency, the required multiparameter estimations present itself as a challenge, since additional computational costs must be added. To mitigate this problem meta-heuristics, such as Adaptive Differential Evolution (JADE), can be applied. Despite the fast convergence of JADE, the runtime execution of parameter estimation can be suboptimal in many cases, mainly when the intrinsic embarrassingly parallel characteristic of the problem is not fully explored. In this paper, we propose a parallel implementation of parameter estimation with JADE in both GPU (through CUDA) and CPU (through OpenMP). We resort to cloud computing, specifically Amazon Web Service, to validate our implementation; executing our code on five different instances types. Experimental results show a considerable speedup obtained using GPU instances instead of CPU ones. Furthermore, qualitative results reveal similarity between both implementations. Thus, without any loss of quality, we are able to obtain an immense gain in execution time and a reduction of cloud computing costs.
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High-Performance Computing Applications Transition to the Cloud in Upstream
Authors S. Kayum and M. RogowskiSummaryThis paper seeks to focus on the O&G industry and the transition of their HPC applications to cloud platforms effectively by providing two assessment methods for HPC applications in the cloud. A new assessment tool is used to aid in assessing HPC applications’ feasibility and advantage of use in the cloud and is used in assessing the migration of reservoir simulation activities to the cloud.
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