@presentation {rizzuti2021ML4SEISMICdfw,
	title = {A dual formulation of wavefield reconstruction inversion for large-scale seismic inversion},
	year = {2021},
	month = {11},
	abstract = {Many of the seismic inversion techniques currently proposed that focus on robustness with respect to the background model choice are not apt to large-scale 3D applications, and the methods that are computationally feasible for industrial problems, such as full waveform inversion, are notoriously limited by convergence stagnation and require adequate starting models. We propose a novel solution that is both scalable and less sensitive to starting models or inaccurate parameters (such as anisotropy) that are typically kept fixed during inversion. It is based on a dual reformulation of the classical wavefield reconstruction inversion, whose empirical robustness with respect to these issues is well documented in the literature. While the classical version is not suited to 3D, as it leverages expensive frequency-domain solvers for the wave equation, our proposal allows the deployment of state-of-the-art time-domain finite-difference methods, and is potentially mature for industrial-scale problems.},
	keywords = {FWI, ML4SEISMIC, SLIM, wave-equation, WRI},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/rizzuti2021ML4SEISMICdfw/Tue-10-55-Rizzuti.pdf},
	author = {Gabrio Rizzuti and Mathias Louboutin and Rongrong Wang and Felix J. Herrmann}
}
@conference {bisbas2020IPDPStbf,
	title = {Temporal blocking of finite-difference stencil operators with sparse "off-the-grid" sources},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium},
	year = {2021},
	note = {(IPDPS, virtual)},
	month = {5},
	abstract = {Stencil kernels dominate a range of scientific applications including seismic and medical imaging, image processing, and neural networks. Temporal blocking is a performance optimisation that aims to reduce the required memory bandwidth of stencil computations by re-using data from the cache for multiple time steps. It has already been shown to be beneficial for this class of algorithms. However, optimising stencils for practical applications remains challenging. These computations often include sparsely located operators, not aligned with the computational grid ("off-the-grid"). For example, our work is motivated by sources that inject a wavefield and measurements interpolating grid values. The resulting data dependencies make the adoption of temporal blocking much more challenging. We propose a methodology to inspect these data dependencies and reorder the computation, leading to performance gains in stencil codes where temporal blocking has not been applicable. We implement this novel scheme in the Devito domain-specific compiler toolchain. Devito implements a domain-specific language embedded in Python to generate optimised partial differential equation solvers using the finite-difference method from high-level symbolic problem definitions. We evaluate our scheme using isotropic acoustic, anisotropic acoustic and isotropic elastic wave propagators of industrial significance. Performance evaluation, after auto-tuning, shows that this enables substantial performance improvement through temporal blocking, over highly-optimised vectorized spatially-blocked code of up to 1.6x.},
	keywords = {finite-difference, HPC, performance, wave-equation},
	doi = {10.1109/IPDPS49936.2021.00058},
	url = {https://arxiv.org/pdf/2010.10248.pdf},
	author = {George Bisbas and Fabio Luporini and Mathias Louboutin and Rhodri Nelson and Gerard Gorman and Paul H. J. Kelly}
}
@article {louboutin2018dae,
	title = {Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration},
	journal = {Geoscientific Model Development},
	year = {2019},
	note = {(Geoscientific Model Development)},
	abstract = {We introduce Devito, a new domain-specific language for implementing high-performance finite difference partial differential equation solvers. The motivating application is exploration seismology where methods such as Full-Waveform Inversion and Reverse-Time Migration are used to invert terabytes of seismic data to create images of the earth{\textquoteright}s subsurface. Even using modern supercomputers, it can take weeks to process a single seismic survey and create a useful subsurface image. The computational cost is dominated by the numerical solution of wave equations and their corresponding adjoints. Therefore, a great deal of effort is invested in aggressively optimizing the performance of these wave-equation propagators for different computer architectures. Additionally, the actual set of partial differential equations being solved and their numerical discretization is under constant innovation as increasingly realistic representations of the physics are developed, further ratcheting up the cost of practical solvers. By embedding a domain-specific language within Python and making heavy use of SymPy, a symbolic mathematics library, we make it possible to develop finite difference simulators quickly using a syntax that strongly resembles the mathematics. The Devito compiler reads this code and applies a wide range of analysis to generate highly optimized and parallel code. This approach can reduce the development time of a verified and optimized solver from months to days.},
	keywords = {finite-differences, HPC, Modeling, wave-equation},
	doi = {10.5194/gmd-12-1165-2019},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GMD/2019/louboutin2018dae/louboutin2018dae.pdf},
	author = {Mathias Louboutin and Michael Lange and Fabio Luporini and Navjot Kukreja and Philipp A. Witte and Felix J. Herrmann and Paulius Velesko and Gerard J. Gorman}
}
