Petaflop Seismic Simulations in the Public Cloud
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During the last decade cloud services and infrastructure as a service became a popular solution for diverse applications. Additionally, hardware support for virtualization closed performance gaps, compared to on-premises, bare-metal systems. This development is driven by offloaded hypervisors and full CPU virtualization. Today’s cloud service providers, such as Amazon or Google, offer the ability to assemble application-tailored clusters to maximize performance. However, from an interconnect point of view, one has to tackle a 4–5\(\times \) slow-down in terms of bandwidth and 25\(\times \) in terms of latency, compared to latest high-speed and low-latency interconnects. Taking into account the high per-node and accelerator-driven performance of latest supercomputers, we observe that the network-bandwidth performance of recent cloud offerings is within 2\(\times \) of large supercomputers. In order to address these challenges, we present a comprehensive application-centric approach for high-order seismic simulations utilizing the ADER discontinuous Galerkin finite element method, which exhibits excellent communication characteristics. This covers the tuning of the operating system, normally not possible on supercomputers, micro-benchmarking, and finally, the efficient execution of our solver in the public cloud. Due to this performance-oriented end-to-end workflow, we were able to achieve 1.09 PFLOPS on 768 AWS c5.18xlarge instances, offering 27,648 cores with 5 PFLOPS of theoretical computational power. This correlates to an achieved peak efficiency of over 20% and a close-to 90% parallel efficiency in a weak scaling setup. In terms of strong scalability, we were able to strong-scale a science scenario from 2 to 64 instances with 60% parallel efficiency. This work is, to the best of our knowledge, the first of its kind at such a large scale.
KeywordsHigh-order DG Seismic simulations Earthquake simulations Cloud computing Petascale computing
EDGE, EDGEcut and the discussed cloud-related scripts are available under BSD-3 from the linked resources at: http://dial3343.org. We thank David Lenz for his contributions to EDGEcut. We thank the AWS Cloud Credits for Research and Academic Google Cloud program. At AWS we thank Walker Stemple, Linda Hedges, Aaron Bucher, Heather Matson, Randy Ridgley and Pierre-Yves Aquilanti for their patient and very helpful support. This work was supported by the Southern California Earthquake Center through award #18211.
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