Performance Modeling of MARE2DEM's Adaptive Mesh Refinement for Makespan Estimation

  • Bruno Da Silva Alves UFRGS
  • Lucas Mello Schnorr UFRGS


Adaptive Mesh Refinement (AMR) is a widely known technique to adapt the accuracy of a solution in critical areas of the problem domain instead of using regular or irregular but static meshes. The MARE2DEM is a parallel application that employs the AMR technique to model 2D electromagnetics in oil and gas exploration. The modeling consists in iteratively applying a data inversion based on a set of measurements collected and registered by a survey on an area of interest. The parallelism of the MARE2DEM works by dividing the workload into a set of refinement groups that represent overlapping areas of the problem domain. Each refinement group can be computed independently of the others by a set of workers, carrying out the AMR in the meshes when necessary. The shape and compute performance of the refinement group depend directly of a set of user-defined parameters. In this article, we provide a method to estimate the MARE2DEM performance for all possible values that can be used in the influencing parameters of the application for a given case study. Our relatively cheap method enables the geologist to configure MARE2DEM correctly and extract the best performance for a given cluster configuration. We detail how the method works and evaluate its effectiveness with success, pinpointing the best values for the creating refinement groups using a real case study from the Marlim field on the coast of Rio de Janeiro, Brazil. Although we demonstrate our evaluation with this scenario, our method works for any input of MARE2DEM.
Palavras-chave: Marine CSEM, Performance Modeling, MARE2DEM, Adaptive Mesh Refinement
ALVES, Bruno Da Silva; SCHNORR, Lucas Mello. Performance Modeling of MARE2DEM's Adaptive Mesh Refinement for Makespan Estimation. In: INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD), 35. , 2023, Porto Alegre/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 119-128.