12 citations · 13 across the 3 of their papers we have counts for
7 papers
Automatic Differentiation for Adjoint Stencil Loops
Jan Hückelheim, Navjot Kukreja, Sri Hari Krishna Narayanan +3
Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil…
Towards automatically building starting models for full-waveform inversion using global optimization methods: A PSO approach via DEAP + Devito
Oscar F. Mojica, Navjot Kukreja
In this work, we illustrate an example of estimating the macro-model of velocities in the subsurface through the use of global optimization methods (GOMs). The optimization problem…
Training on the Edge: The why and the how
Navjot Kukreja, Alena Shilova, Olivier Beaumont +4
Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distrib…
Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration
Mathias Louboutin, Michael Lange, Fabio Luporini +5
We introduce Devito, a new domain-specific language for implementing high-performance finite difference partial differential equation solvers. The motivating application is explora…
Architecture and performance of Devito, a system for automated stencil computation
Fabio Luporini, Michael Lange, Mathias Louboutin +7
Stencil computations are a key part of many high-performance computing applications, such as image processing, convolutional neural networks, and finite-difference solvers for part…
Backpropagation for long sequences: beyond memory constraints with constant overheads
Navjot Kukreja, Jan Hückelheim, Gerard J. Gorman
Naive backpropagation through time has a memory footprint that grows linearly in the sequence length, due to the need to store each state of the forward propagation. This is a prob…