HotCSE Seminar
Computational Science & Engineering
Wednesday November 7, 12pm-1pm, Petit 102A

Domain-specific abstractions for large-scale geophysical inverse-problems

Mathias Louboutin and Philipp Witte
Advisor: Prof. Felix J. Herrmann


Seismic inverse problems for subsurface imaging and parameter estimation are a computationally challenging class of inverse problems and involve solving large numbers of wave equations on big computational domains. Furthermore, seismic inverse problems are challenging from the mathematical point of view as well, as they are often non-linear and ill-posed. Software frameworks for seismic inverse problems therefore need to provide both high-level abstractions for facilitating the implementation of optimization algorithms and solvers for partial differential equations (PDEs), but they also need to scale to problem sizes with over one billion unknown parameters. In this talk, we introduce two high-level abstractions that address these issues; Devito is a domain-specific language and compiler for finite differences embedded in Python and designed for symbolic implementations of PDEs on a high abstraction level. During runtime, the Devito compiler automatically translates these high-level expressions into optimized C code, using a range of performance optimization techniques such as vectorization, loop blocking, elimination of common sub-expressions and OpenMP/MPI parallelism. The second half of the talk introduces an abstract linear algebra framework called JUDI, which provides data containers and matrix-free linear operators built around Devito, for symbolic implementations of optimization algorithms for seismic inversion. The talk will give an overview of these two frameworks and demonstrate through a series of examples from seismic imaging and non-linear parameter estimation, how the mathematical and computational complexity of these problems can be addressed through the power of abstractions.


Mathias Louboutin is a Ph.D student at the School for Computational Science and Engineering and a member of the Seismic Laboratory for Imaging and Modeling (SLIM), under the supervision of Felix J. Herrmann. He obtained his M.Sc. in Mathematics in France and started a Ph.D. in Applied Geophysics at the University of British Columbia (UBC), but transferred to Georgia Tech in January 2018. His main interests are numerical methods for wave propagation, high performance computing and applications to computational inverse problems such exploration seismology or medical imaging.
Philipp Witte is a Ph.D student at the School for Computational Science and Engineering and also a member of the Seismic Laboratory for Imaging and Modeling. His research interest lie in developing software and algorithms for large-scale seismic inverse problems and in machine learning. Philipp holds a B.Sc. and an M.Sc. in Geophysics from the University of Hamburg, Germany. From September 2014 to August 2018 he was a Ph.D. student at the department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia in Vancouver and he transferred to Georgia Tech in August 2018.