Reverse Time Migration on a GPU Cluster Using the Seismic Data Parallelism Strategy

Obregon, Ivan; Salamanca, William; Ramirez, Ana B.

doi:10.1190/sbgf2017-069

Cited by 1 publication

(3 citation statements)

References 6 publications

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“…Not only that, but the bandwidth between a central processing unit (CPU) and GPU can become a bottleneck, slowing down the whole execution. Recently, the usage of such devices accelerated the execution of techniques such as regularization, wave modelling, reverse time migration (RTM), and full waveform inversion (FWI) significantly compared to CPU implementations (see Michéa and Komatitsch, 2010; Obregon et al ., 2017; Wang et al ., 2019; Okita et al ., 2021).…”

Section: High‐performance Computing Techniquesmentioning

confidence: 99%

“…Their results are limited to a two-dimensional model and a single consumergrade accelerator, and we expand it using considerably larger three-dimensional models and more powerful accelerators targeted for the HPC usage. Finally, regarding scalability, Obregon et al (2017) also investigated strategies to parallelize an RTM algorithm through independent shots.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, regarding scalability, Obregon et al . (2017) also investigated strategies to parallelize an RTM algorithm through independent shots.…”

Section: Introductionmentioning

confidence: 99%

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High‐performance computing strategies for seismic‐imaging software on the cluster and cloud‐computing environments

Okita

Camargo

Ribeiro

et al. 2021

Geophysical Prospecting

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Modern subsurface imaging techniques allow obtaining high‐quality images but with high computational costs. Nonetheless, depending on the amount of data, their execution is limited by memory in the current generation's hardware. However, with the advancement of new hardware and cloud‐based solutions, these problems are mitigated but still with the risk of work loss and instability. To mitigate the execution problems in memory‐limited and fail‐prone environments, we propose two high‐performance computing techniques. The first is based on independent checkpointing alongside a fault‐tolerant framework to store an execution state and recover from that state in case of failures. Besides, for memory‐limited graphics processing units, we present a technique to reduce the amount of memory requirement that we call the hybrid strategy. The experiments showed that the independent checkpointing alongside the fault‐tolerant framework is able to mitigate the performance penalty of node failures, with the independent checkpointing technique being more relevant when multiple nodes are terminated. Furthermore, the hybrid strategy technique has shown the possibility of execution of larger models that could make the graphics processing unit run out of memory otherwise. Finally, our implementation is scalable, allowing a significant improvement in performance when adding new nodes. In conclusion, our techniques can be used to deliver fast, high‐fidelity subsurface imaging in unstable and memory‐limited environments, such as the cloud.

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