Experiences with Seismic Imaging on GPUs

Morton, Scott A.; Cullison, Thomas; Micikevicius, Paulius

doi:10.3997/2214-4609.201405038

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternately, researchers have examined design space exploration to tailor processor architecture to the needs of RTM in order to improve energy efficiency [13]. Since 2008, researchers in both industry and academia have explored the use of GPUs for seismic processing [1,4,11,17,18]. As canonical wave equations, this report uses mathematical representations of a VTI scheme introduced by [29] and the TTI scheme from [21].…”

Section: Related Workmentioning

confidence: 99%

Optimization of Forward Wave Modeling on Contemporary HPC Architectures

Krueger

Micikevicius

Williams

2012

View full text Add to dashboard Cite

Reverse Time Migration (RTM) is one of the main approaches in the seismic processing industry for imaging the subsurface structure of the Earth. While RTM provides qualitative advantages over its predecessors, it has a high computational cost warranting implementation on HPC architectures. We focus on three progressively more complex kernels extracted from RTM: for isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) media. In this work, we examine performance optimization of forward wave modeling, which describes the computational kernels used in RTM, on emerging multi-and manycore processors and introduce a novel common subexpression elimination optimization for TTI kernels. We compare attained performance and energy efficiency in both the single-node and distributed memory environments in order to satisfy industry's demands for fidelity, performance, and energy efficiency. Moreover, we discuss the interplay between architecture (chip and system) and optimizations (both on-node computation) highlighting the importance of NUMA-aware approaches to MPI communication. Ultimately, our results show we can improve CPU energy efficiency by more than 10× on Magny Cours nodes while acceleration via multiple GPUs can surpass the energy-efficient Intel Sandy Bridge by as much as 3.6×.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimization of Forward Wave Modeling on Contemporary HPC Architectures

Krueger

Micikevicius

Williams

2012

View full text Add to dashboard Cite

show abstract

An efficient scheme for multi-GPU TTI reverse time migration

Meng

et al. 2019

Appl. Geophys.

View full text Add to dashboard Cite

Solving 3D anisotropic elastic wave equations on parallel GPU devices

2013

View full text Add to dashboard Cite

Efficiently modeling seismic datasets in complex 3D anisotropic media by solving the 3D elastic wave equation is an important challenge in computational geophysics. Using a stress-stiffness formulation on a regular grid, we present a 3D finite-difference time-domain (FDTD) solver using a 2 nd-order temporal and 8 thorder spatial accuracy stencil that leverages the massively parallel architecture of graphics processing units (GPUs) to accelerate the computation of key kernels. The relatively small memory of an individual GPU limits the model domain sizes that can be computed on a single device. To circumvent this constraint and move toward modeling industry-sized 3D anisotropic elastic data sets, we parallelize computation across multiple GPU devices by using domain decomposition and, for each time step, employing an inter-device communication protocol to exchange data values falling within interior boundaries of each sub-domain. For two or more GPU devices within a single compute node, we use direct peer-topeer (i.e., GPU-to-GPU) communication, while for networked nodes we employ message-passing interface (MPI) directives to route data over the network. Our 2D GPU-based anisotropic elastic modeling tests achieved a 10× speedup relative to an OpenMP CPU implementation run on an eight-core machine, while our 3D tests using dual GPU devices produced up to a 28× speedup. The performance boost afforded by the GPU architecture allows us to model seismic data for 3D anisotropic elastic models at lower hardware cost and in less time than previously possible.

show abstract

Experiences with Seismic Imaging on GPUs

Cited by 3 publications

References 0 publications

Optimization of Forward Wave Modeling on Contemporary HPC Architectures

Optimization of Forward Wave Modeling on Contemporary HPC Architectures

An efficient scheme for multi-GPU TTI reverse time migration

Solving 3D anisotropic elastic wave equations on parallel GPU devices

Contact Info

Product

Resources

About