JuliaCon 2020 | Multi-Physics 3-D Inversion on GPU Supercomputers with Julia | Ludovic Räss

We present an iterative and massively scalable 3-D multi-GPU inversion workflow using Julia for coupled multi-physics processes in Earth Sciences. We introduce an adjoint framework for the two-phase flow equations, assess the unknown porosity field reconstruction in 3-D and discuss the performance evaluation.

The adjoint-based multi-physics inversion framework we present enables the development of efficient and massively scalable 3-D multi-GPU solvers with application to optimisation problems.

We use an iterative matrix-free pseudo-transient approach and the finite difference method to solve the forward and the adjoint coupled two-phase flow equations. We achieve efficient calculations of the pointwise gradients of the flow solution with respect to the porosity. We then use the gradients in a gradient descent method to reconstruct the pointwise porosity in 3-D.

We assess the performance of the 3-D memory-bounded solvers using a simple effective memory throughput metric. We finally discuss how the overlap of computations with MPI communications permits us to achieve a close to optimal parallel efficiency. We rely on the ParallelStencil and ImplicitGobalGrid packages for high-performance stencil-based calculations and optimal distributed memory parallelisation.

Co-authors - Georg Reuber, Samuel Omlin Time Stamps:

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[noelaraujomoreira]

Hello. Can you provide any reference (or keyword) of how to the local interactive approach works ?
A toy example showing the an implementation could help me a lot.

[mathiaslouboutin]

Hello. The package `ParallelStencil` does not seem to exist, is it a private package?