2020-07-29 –, Green Track
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
Computational Scientist and responsible for Julia Computing at the Swiss National Supercomputing Centre (CSCS), ETH Zurich
Why to perform serial tasks while nature is mostly parallel? I am geoscientist with strong interest in high-performance computing (HPC), two-phase flow and ice dynamics. GPUs and supercomputers permit to investigate previously unsolvable problems at unprecedented resolutions.