Using SPITS to Optimize the Cost of High-Performance Geophysics Processing on the Cloud

Okita, Nicholas T.; Coimbra, Tiago A.; Rodamilans, Charles; Tygel, Martin; Borin, Edson

doi:10.3997/2214-4609.201803077

Cited by 9 publications

(1 citation statement)

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“…Notably, spot instances have the same performance as their on-demand counterparts. Therefore, if the software is fault tolerant, it is possible to lower execution prices while keeping the same performance (see Okita et al, 2018). For more information regarding the usage of spot-market in-stances to reduce turnaround prices, the reader can refer to Yi et al (2010), Kumar et al (2017) and Okita and Coimbra (2021a).…”

Section: Cloud Computing Environmentmentioning

confidence: 99%

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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