Abstract
The Controlled Source Electromagnetic (CSEM) combined with seismic surveys has been used to explore new oil and gas reservoirs. The MARE2DEM application generates as mesh that represents a resistivity model of the seafloor underground. From a set of electromagnetic readings, the application runs a data inversion (using Maxwell’s equations) along many steps to converge to a resistivity model that more closely matches the measured data. This data inversion procedure is very compute-bound because of the large amount of arithmetic operations involved. As consequence, the MARE2DEM application divides the workload into smaller work grains, called refinement groups due to the usage of Adaptive Mesh Refinement (AMR). These groups are processed independently in a parallel fashion by a set of workers. It is known that parallel processing suffers from delays and resource underutilization if the load remains imbalanced. In this article, we propose an analysis of the performance and imbalance of the MARE2DEM through source code inspection and trace analysis. The novelty of our investigation consists in the usage of runtime parameters to more profoundly understand and characterize the refinement groups’ execution time and variability. Our results show that the execution time of the refinement groups is strongly impacted by both the number of processed nodes present on the input mesh and the measured data associated to each refinement group.
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Notes
- 1.
MARE2DEM repository: https://mare2dem.bitbucket.io/.
- 2.
UFRGS-PCAD cluster: http://gppd-hpc.inf.ufrgs.br/.
- 3.
otf2csv: https://github.com/schnorr/otf2utils.
- 4.
References
Cogo Miletto, M., Leandro Nesi, L., Mello Schnorr, L., Legrand, A.: Performance analysis of task-based multi-frontal sparse linear solvers: structure matters. Future Gener. Comput. Syst. 135, 409–425 (2022). https://doi.org/10.1016/j.future.2022.05.013
Cooper, R., MacGregor, L.: CSEM: back from the brink (2020)
Correa, J.L., Menezes, P.T.L.: Marlim R3D (MR3D) - the full azimuth CSEM dataset (2018). https://doi.org/10.5281/zenodo.1256787
Correa, J.L., Menezes, P.T.L.: Marlim R3D: a realistic model for controlled-source electromagnetic simulations phase 2: the controlled-source electromagnetic data set. Geophysics 84(5), E293–E299 (2019). https://doi.org/10.1190/geo2018-0452.1
Dagostini, J.I., da Silva, H.C.P., Pinto, V.G., Velho, R.M., Gastal, E.S.L., Schnorr, L.M.: Improving workload balance of a marine CSEM inversion application. In: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 704–713 (2021). https://doi.org/10.1109/IPDPSW52791.2021.00107
Isaacs, K.E., et al.: State of the art of performance visualization. In: Borgo, R., Maciejewski, R., Viola, I. (eds.) EuroVis - STARs. The Eurographics Association (2014). https://doi.org/10.2312/eurovisstar.20141177
Key, K.: MARE2DEM: a 2-D inversion code for controlled-source electromagnetic and magnetotelluric data. Geophys. J. Int. 207(1), 571–588 (2016). https://doi.org/10.1093/gji/ggw290
Key, K., Ovall, J.: A parallel goal-oriented adaptive finite element method for 2.5-d electromagnetic modelling. Geophys. J. Int. 186(1), 137–154 (2011). https://doi.org/10.1111/j.1365-246X.2011.05025.x
Knüpfer, A., et al.: Score-P: a joint performance measurement run-time infrastructure for periscope, scalasca, tau, and vampir. In: Brunst, H., Müller, M.S., Nagel, W.E., Resch, M.M. (eds.) Tools for High Performance Computing 2011, pp. 79–91. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31476-6_7
Nascimento, T.M., Menezes, P.T.L., Braga, I.L.: High-resolution acoustic impedance inversion to characterize turbidites at Marlim field, Campos basin, Brazil. Interpretation 2(3), T143–T153 (2014). https://doi.org/10.1190/INT-2013-0137.1
Veroneze Solórzano, A.L., Leandro Nesi, L., Mello Schnorr, L.: Using visualization of performance data to investigate load imbalance of a geophysics parallel application. In: Practice and Experience in Advanced Research Computing, pp. 518–521. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3311790.3400844
Zhdanov, M.S.: Geophysical Electromagnetic Theory and Methods. Elsevier, Oxford (2009)
Acknowledgements
FAPERGS (16/354-8, 16/488-9), the CNPq (447311/2014-0), the CAPES (Brafitec 182/15, Cofecub 899/18), and Petrobras (2018/00263-5). This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001. Experiments have been executed in INF/UFRGS’s High-Performance Computational Resources, http://gppd-hpc.inf.ufrgs.br.
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da Silva Alves, B., Gaspary, L.P., Schnorr, L.M. (2022). Towards Parameter-Based Profiling for MARE2DEM Performance Modeling. In: Navaux, P., Barrios H., C.J., Osthoff, C., Guerrero, G. (eds) High Performance Computing. CARLA 2022. Communications in Computer and Information Science, vol 1660. Springer, Cham. https://doi.org/10.1007/978-3-031-23821-5_5
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