22,215 research outputs found

    Performance Analysis and Optimization of a Hybrid Distributed Reverse Time Migration Application

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    To fully exploit emerging processor architectures, programs will need to employ threaded parallelism within a node and message passing across nodes. Today, MPI+OpenMP is the preferred programming model for this task. However, tuning MPI+OpenMP programs for clusters is difficult. Performance tools can help users identify bottlenecks and uncover opportunities for improvement. Applications to analyze seismic data employ scalable parallel systems to produce timely results. This thesis describes our experiences of applying performance tools to gain insight into an MPI+OpenMP code that performs Reverse Time Migration (RTM) to analyze seismic data and also assess the capabilities of available tools for analyzing the performance of a sophisticated application that employ both message-passing and threaded parallelism. The tools provided us with insights into the effectiveness of the domain decomposition strategy, the use of threaded parallelism, and functional unit utilization in individual cores. By applying insights obtained from Rice University's HPCToolkit and hardware performance counters, we were able to improve the performance of the RTM code by roughly 30 percent

    Cloud migration of legacy applications

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    Enhancing Energy Production with Exascale HPC Methods

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    High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft
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