Full-Wavefield Inversion: An Extreme-Scale PDE-Constrained Optimization Problem

Lacasse, Martin‐D.; White, Laurent; Denli, Huseyin; Qiu, Lirong

doi:10.1007/978-1-4939-8636-1_6

Cited by 3 publications

(2 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Full-wavefield inversion [11,22] is the state-of-the-art method to estimate material properties of the earth's subsurface. The estimated material properties can then enable multiple opportunities, such as finding regions that contain hydrocarbons (e.g., in oil and gas exploration), identifying safe zones for carbon sequestration, as well as finding appropriate locations for storing hydrogen, which is a promising source of low-carbon energy.…”

Section: Background 21 Full-wavefield Inversionmentioning

confidence: 99%

“…Wave simulations have become one of the important topics in science and engineering, with applications in seismic hazard mitigation [34], medical imaging [13,26], deriving the composition and features of the Earth's subsurface [19,22,29], oceanography [8], or military [16]. We investigate the performance of discontinuous Galerkin (DG) discretizations, when used with the Gauss-Lobatto-Legendre (GLL) integration scheme, on straight-faced hexahedral elements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gaps

Hanindhito

Gourounas

Fathi

et al. 2022

Proceedings of the 36th ACM International Conference on Supercomputing

View full text Add to dashboard Cite

Large-scale simulations of wave-type equations have many industrial applications, such as in oil and gas exploration. Realistic simulations, which involve a vast amount of data, are often performed on multiple nodes of an HPC cluster. Using GPUs for these simulations is attractive due to considerable parallelizability of the algorithms. Many industry-relevant simulations have characteristics in their physics or geometry that can be exploited to improve computational efficiency. Furthermore, the choice of simulation algorithm impacts computational efficiency significantly.In this work, we exploit these features to significantly improve performance for a class of problems. Specifically, we use the discontinuous Galerkin (DG) finite element method, along with the Gauss-Lobatto-Legendre (GLL) integration scheme on hexahedral elements with straight faces, which then greatly reduces the number of BLAS operations, and simplify the computations to Level-1 BLAS operations, reducing the turn around time for wave simulation. However, attaining peak performance of GPUs is often not possible in these codes that exacerbate bottlenecks caused by data movement, even when modern GPUs enjoying the latest highbandwidth memory are being used.We have developed GAPS, an efficient and scalable, GPUaccelerated PDE solver for Wave Simulation, by using hardwareand data-movement-aware algorithms. While significant speed-up over CPUs can be achieved, data movement still limits GPU performance. We present several optimization strategies, including kernel fusion, Look-Up-Table-based neighbor search, improved shared memory utilization, and SM-occupancy-aware register allocation. They improve performance up to 84.15x over CPU implementations and 1.84x over base GPU implementations on average. We then extend GAPS to support multi-GPUs on multi-node HPC clusters for large-scale wave simulations, and perform additional optimizations to reduce communication overhead. We also investigate the performance of several MPI libraries in order to fully overlap communication and computation. We are able to reduce the communication overhead by 70%, and achieve weak-scaling over 128 GPUs.

show abstract

Section: Background 21 Full-wavefield Inversionmentioning

confidence: 99%