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The Gordon Bell-winning AWP-ODC application continues to push the boundaries of earthquake simulation by leveraging the enhanced performance of MVAPICH on both CPU and GPU based architectures. This presentation highlights the recent improvements to the code and its application to broadband deterministic 3D wave propagation simulations of earthquake ground motions, incorporating high-resolution surface topography and detailed underground structures. The results of these simulations provide critical insights into the potential impacts of major earthquakes, contributing to more effective disaster preparedness and mitigation strategies. Additionally, the presentation will address the scientific and technical challenges encountered during the process and discuss the implications for future large-scale seismic studies on Exascale computing systems.more » « lessFree, publicly-accessible full text available August 19, 2025
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The ShakeOut simulations of a M7.8 earthquake on the southern San Andreas fault (2008) studies predicted unexpectedly large ground motions throughout Southern California due to waveguide e#ects from interconnected sedimentary basins in 3D velocity model. Here, we re-examine the ground motion predictions from the ShakeOut scenario using the most recent updates on the velocity models and the realistic surface topography from the Digital Elevation Model. The exceptional scalability and performance of the AWP-ODC due to the most recent advancements allows for examination of plenty earth models including the irregular surface topography at low computational cost.more » « lessFree, publicly-accessible full text available August 19, 2025
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AWP-ODC is a 4th-order finite difference code used for linear wave propagation, Iwan-type nonlinear dynamic rupture and wave propagation, and Strain Green Tensor simulation2. We have ported and verified the linear and topography version of AWP-ODC, with discontinuous mesh as well as topography, to HIP so that it can also run on AMD GPUs. The topography code achieved a 99.6% parallel efficiency on 4,096 nodes on Frontier, a Leadership Computing Facility at Oak Ridge National Laboratory. We have also implemented CUDA-aware features and on-the-fly GDR compression in the linear version of the ported HIP code. These enhancements significantly improve data transfer efficiency between GPUs, reducing communication overhead and boosting overall performance. We have also extended CUDA-aware features to the topography version and are actively working on incorporating GDR compression into this version as well. We see 154% benefits over IMPI in MVAPICH2-GDR with CUDA-aware support and on-the-fly compression for linear AWP-ODC on Lonestar-6 A100 nodes. Furthermore, we have successfully integrated a checkpointing feature into the nonlinear IWAN version of AWP-ODC, prepared for future extreme-scale simulation during Texascale Days of Frontera at TACC.more » « lessFree, publicly-accessible full text available August 19, 2025
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Recent studies have shown that users of visual analytics tools can have difficulty distinguishing robust findings in the data from statistical noise, but the true extent of this problem is likely dependent on both the incentive structure motivating their decisions, and the ways that uncertainty and variability are (or are not) represented in visualisations. In this work, we perform a crowd-sourced study measuring decision-making quality in visual analytics, testing both an explicit structure of incentives designed to reward cautious decision-making as well as a variety of designs for communicating uncertainty. We find that, while participants are unable to perfectly control for false discoveries as well as idealised statistical models such as the Benjamini-Hochberg, certain forms of uncertainty visualisations can improve the quality of participants’ decisions and lead to fewer false discoveries than not correcting for multiple comparisons. We conclude with a call for researchers to further explore visual analytics decision quality under different decision-making contexts, and for designers to directly present uncertainty and reliability information to users of visual analytics tools. The supplementary materials are available at: https://osf.io/xtsfz/.more » « lessFree, publicly-accessible full text available May 11, 2025
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Free, publicly-accessible full text available May 1, 2025
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The Gordon Bell winning AWP-ODC application has a long history of boosted performance with MVAPICH on both CPU and GPU-based architectures. This talk will highlight a recent compression support implemented by the MVAPICH team, and its benefits to the large-scale earthquake simulation on the leadership class computing systems. The presentation will conclude with a discussion of the opportunities and technical challenges associated with the development of earthquake simulation software for Exascale computing.more » « less
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AWP-ODC is a 4th-order finite difference code used by the SCEC community for linear wave propagation, Iwan-type nonlinear dynamic rupture and wave propagation, and Strain Green Tensor simulation. We have ported and verified the CUDA-version of AWP-ODC-SGT, a reciprocal version used in the SCEC CyberShake project, to HIP so that it can also run on AMD GPUs. This code achieved sustained 32.6 Petaflop/s performance and 95.6% parallel efficiency at full scale on Frontier, a Leadership Computing Facility at Oak Ridge National Laboratory. The readiness of this community software on AMD Radeon Instinct GPUs and EPYC CPUs allows SCEC to take advantage of exascale systems to produce more realistic ground motions and accurate seismic hazard products. We have also deployed AWP-ODC to Azure to leverage the array of tools and services that Azure provides for tightly coupled HPC simulation on commercial cloud. We collaborated with Internet 2/Azure Accelerator supporting team, as part of Microsoft Internet2/Azure Accelerator for Research Fall 2022 Program, with Azure credits awarded through Cloudbank, an NSF-funded initiative. We demonstrate the AWP performance with a benchmark of ground motion simulation on various GPU based cloud instances, and a comparison of the cloud solution to on-premises bare-metal systems. AWP-ODC currently achieves excellent speedup and efficiency on CPU and GPU architectures. The Iwan-type dynamic rupture and wave propagation solver faces significant challenges, however, due to the increased computational workload with the number of yield surfaces chosen. Compared to linear solution, the Iwan model adds 10x-30x more computational time plus 5x-13x more memory consumption that require substantial code changes to obtain excellent performance. Supported by NSF’s Characteristic Science Applications (CSA) program for the Leadership-Class Computing Facility (LCCF) at Texas Advanced Computing Center (TACC), we are porting and improving the performance of this nonlinear AWP-ODC software, preparing for the next generation NSF LCCF system called Horizon, to be installed at TACC. During Texascale days on the current TACC’s Frontera, we carried out an Iwan-type nonlinear dynamic rupture and wave propagation simulation of a Mw7.8 scenario earthquake on the southern San Andreas fault. This simulation modeled 83 seconds of rupture with a grid spacing of 25 m to resolve frequencies up to 4 Hz with a minimum shear-wave velocity of 500 m/s.more » « less