Elastic 3D Wavefield Simulation on budget GPUs using the GLSL shading language
Emanuel Trabes, Silvana Spagnotto, Orlando Alvarez Pontoriero, Julio, Daniel Dondo Gazzano, Carlos Federico Sosa Paez

TL;DR
This paper presents a method for elastic 3D wavefield simulation using GLSL on low-end GPUs, offering a cost-effective alternative to high-end hardware for seismic applications.
Contribution
It introduces a GLSL-based finite difference simulation approach that runs efficiently on low-end GPUs, simplifying implementation and reducing hardware costs.
Findings
Outperforms multicore CPU implementations in speed
Achieves accurate seismic simulations in reasonable time
Runs on any modern GPU with simplified programming
Abstract
Forward wavefield simulation is an important step in Full Waveform Inversion systems. Fast simulations are instrumental to get inversion result in reasonable time frames. Most of research and software aims towards utilizing costly computer clusters composed of multiple CPUs and numerous high end GPUs to shorten the forward simulation time. Using this type of hardware has some disadvantages as: high cost, complex programming models and unavailability of resources. In this work, we present a finite difference elastic 3D wavefield forward simulation that takes advantage of any modern low end GPU, by using the GLSL shading language.Some of the advantages of using GLSL are: runs in any modern GPU, has a simplified computing and memory model and provides state of art performance thanks to its very well optimized vendor developed drivers. We show that our GLSL implementation easily outperforms…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSeismic Imaging and Inversion Techniques · Geological Modeling and Analysis · Distributed and Parallel Computing Systems
