@article {witte2019ecl,
	title = {Geophysics Bright Spots: Efficient coding of large-scale seismic inversion algorithms},
	journal = {The Leading Edge},
	volume = {38},
	number = {6},
	year = {2019},
	note = {(The Leading Edge)},
	pages = {482-484},
	abstract = {In {\textquotedblleft}A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia,{\textquotedblright} Witte et al. describe new developments in how to code complex geophysical algorithms in a concise way. Subsurface seismic imaging and parameter estimation are among the most computationally challenging problems in the scientific community. Codes for solving seismic inverse problems, such as FWI or least-squares reverse time migration (LS-RTM), need to be highly optimized, but at the same time, facilitate the implementation of complex optimization algorithms. Traditionally, production-level codes in the oil and gas industry were exclusively written in low-level languages, such as C or Fortran, with extensive amounts of manual performance optimizations, thus making code maintenance, debugging, and adoption of new algorithms prohibitively challenging. Witte et al. present a paradigm of software engineering for seismic inverse problems based on symbolic user interfaces and code generation with automated performance optimization. Inspired by recent deep learning frameworks, the Julia Devito inversion framework (JUDI; an open-source software package) combines high-level abstractions for expressing seismic inversion algorithms with a domain-specific language compiler called Devito for solving the underlying wave equations. Devito{\textquoteright}s generated code is compiled just in time and outperforms codes with manual performance optimizations. JUDI utilizes Julia{\textquoteright}s high-level parallelization, making the software easily adaptable to a variety of computing environments such as densely connected HPC clusters or the cloud. The numerical examples (Figure 3) demonstrate the ability to implement a variety of complex algorithms for FWI and LS-RTM in a few lines of Julia code and run it on large-scale 3D models. The paper concludes that abstractions and performance are not mutually exclusive, and use of symbolic user interfaces can facilitate the implementation of new and innovative seismic inversion algorithms.},
	keywords = {bright spots, inversion, Julia, large-scale},
	doi = {10.1190/tle38060482.1},
	url = {https://library.seg.org/doi/10.1190/tle38060482.1},
	author = {Philipp A. Witte and Mathias Louboutin and Navjot Kukreja and Fabio Luporini and Michael Lange and Gerard J. Gorman and Felix J. Herrmann}
}
@article {witte2018alf,
	title = {A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia},
	journal = {Geophysics},
	volume = {84},
	number = {3},
	year = {2019},
	note = {(Geophysics)},
	month = {03},
	pages = {F57-F71},
	abstract = {Writing software packages for seismic inversion is a very challenging task, since problems such as full-waveform inversion or least-squares imaging are both algorithmically and computationally demanding due to the large number of unknown parameters and the fact that we are propagating waves over many wavelengths. Software frameworks therefore need to combine both versatility and performance to provide geophysicists with the means and flexibility to implement complex algorithms that scale to exceedingly large 3D problems. Following these principles, we introduce the Julia Devito Inversion framework, an open-source software package in Julia for large-scale seismic modeling and inversion based on Devito, a domain-specific language compiler for automatic code generation. The framework consists of matrix-free linear operators for implementing seismic inversion algorithms that closely resembles the mathematical notation, a flexible resilient parallelization and an interface to Devito for generating optimized stencil code to solve the underlying wave equations. In comparison to many manually optimized industry codes written in low-level languages, our software is built on the idea of independent layers of abstractions and user interfaces with symbolic operators, making it possible to manage both the complexity of algorithms and performance optimizations, while preserving modularity, which allows for a level of expressiveness needed to formulate a broad range of wave-equation-based inversion problems. Through a series of numerical examples, we demonstrate that this allows users to implement algorithms for waveform inversion and imaging as simple Julia scripts that scale to large-scale 3D problems; thus providing a truly performant research and production framework.},
	keywords = {FWI, inversion, LSRTM, Modeling, software},
	doi = {10.1190/geo2018-0174.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2019/witte2018alf/witte2018alf.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Navjot Kukreja and Fabio Luporini and Michael Lange and Gerard J. Gorman and Felix J. Herrmann}
}
@article {witte2018fwip3,
	title = {Full-Waveform Inversion - Part 3: optimization},
	journal = {The Leading Edge},
	volume = {37},
	number = {2},
	year = {2018},
	note = {(The Leading Edge)},
	month = {1},
	pages = {142-145},
	abstract = {This tutorial is the third part of a full-waveform inversion (FWI) tutorial series with a step-by-step walkthrough of setting up forward and adjoint wave equations and building a basic FWI inversion framework. For discretizing and solving wave equations, we use Devito, a Python-based domain-specific language for automated generation of finite-difference code (Lange et al., 2016). The first two parts of this tutorial (Louboutin et al., 2017, 2018) demonstrated how to solve the acoustic wave equation for modeling seismic shot records and how to compute the gradient of the FWI objective function using the adjoint-state method. With these two key ingredients, we will now build an inversion framework that can be used to minimize the FWI least-squares objective function.},
	keywords = {devito, finite-differences, FWI, inversion, Modeling, tutorial},
	doi = {10.1190/tle37020142.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2018/witte2018fwip3/witte2018fwip3.html},
	author = {Philipp A. Witte and Mathias Louboutin and Keegan Lensink and Michael Lange and Navjot Kukreja and Fabio Luporini and Gerard Gorman and Felix J. Herrmann}
}
@conference {lange2016dtg,
	title = {Devito: Towards a generic finite difference DSL using symbolic python},
	booktitle = {6th Workshop on Python for High-Performance and Scientific Computing},
	year = {2016},
	note = {(PyHPC, Utah)},
	month = {11},
	pages = {67-75},
	abstract = {Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific Python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the Python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic Python expressions. We demonstrate Devito{\textquoteright}s symbolic API and performance advantages over traditional Python acceleration methods before highlighting its use in the scientific context of seismic inversion problems.},
	keywords = {acoustic, finite differences, HPC, inversion, Modelling, python, software optimization},
	doi = {10.1109/PyHPC.2016.9},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg_pres.pdf},
	author = {Michael Lange and Navjot Kukreja and Mathias Louboutin and Fabio Luporini and Felippe Vieira Zacarias and Vincenzo Pandolfo and Paulius Velesko and Paulius Kazakas and Gerard Gorman}
}
