Aluno: Victor Eduardo Martinez Abaunza
Orientador: Prof. Dr. Philippe Olivier Alexandre Navaux
Title: Performance Optimization of Geophysics Stencils on HPC Architectures
Linha de Pesquisa: Computação de Alto Desempenho e Sistemas Distribuídos
Local: Prédio 43412 – Sala 215 (sala de videoconferência), Instituto de Informática
– Prof. Dr. Nicolas Bruno Maillard (UFRGS)
– Prof. Dr. Laércio Lima Pilla (UFSC)
– Prof. Dr. Marco Antonio Zanata Alves (UFPR – por videoconferência)
Presidente da Banca: Prof. Dr. Philippe Olivier Alexandre Navaux
Abstract: Wave modeling is a crucial tool in geophysics (for efficient strong motion analysis, risk mitigation and oil & gas exploration). Due to its simplicity and numerical efficiency, the finite-difference method is one of the standard techniques implemented to solve the wave propagation equations. This kind of applications is known as stencils because they consist in a pattern that replicates the same computation on a multi-dimensional domain. High Performance Computing is required to solve this class of problems, as a consequence of a large number of grid points involved in three-dimensional simulations of the underground. The performance optimization of stencil computations is a challenge and strongly depends on the underlying architecture. In this context, this work was directed toward a twofold aim. Firstly, we have led our research on multicore architectures and we have analyzed the standard OpenMP implementation of numerical kernels from the 3D heat transfer model (a 7-point Jacobi stencil) and the Ondes3D code (a full-fledged application developed by the French Geological Survey). We have considered two well-known implementations (naïve, and space blocking) to find correlations between parameters from the input configuration at runtime and the computing performance; thus, we have proposed a Machine Learning-based approach to evaluate, to predict, and to improve the performance of these stencil models on the underlying architecture. We have also used an acoustic wave propagation model provided by the Petrobras company and we have predicted the performance with high accuracy on multicore architectures. Secondly, we have oriented our research on heterogeneous architectures, we have analyzed the standard implementation for seismic wave propagation model in CUDA, to find which factors affect the performance; then, we have proposed a task-based implementation to improve the performance, according to the runtime configuration set (scheduling algorithm, size, and number of tasks), and we have compared the performance obtained with the classical CPU or GPU only versions with the results obtained on heterogeneous architectures.
Keywords: HPC. Machine Learning. Multicore. Heterogeneous Architectures. Stencil Computations. Performance Simulation. Performance improvement.