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Accurate simulation of earthquake scenarios is essential for advancing seismic hazard analysis and risk mitigation strategies. At the San Diego Supercomputer Center (SDSC), our research focuses on optimizing the performance and reliability of large-scale earthquake simulations using the AWP-ODC software. By implementing GPU-aware MPI calls, we enable direct data processing within GPU memory, eliminating the need for explicit data transfers between CPU and GPU. This GPU-aware MPI achieves nearly ideal parallel efficiency at full scale across both Nvidia and AMD GPUs, leveraging the MVAPICH-PLUS support on Frontier at Oak Ridge National Laboratory and Vista at the Texas Advanced Computing Center. We utilized the MVAPICH-Plus 4.0 compiler to enable ZFP compression, which significantly enhances inter-node communication efficiency – a critical improvement given the communication bottleneck inherent in large-scale simulations. Our GPU-aware AWP-ODC versions include linear forward, topography and nonlinear Iwan-type solvers with discontinuous mesh support. On the Frontier system with MVAPICH 4.0, Hip-aware MPI calls on MI250X GPUs deliver nearly ideal weak-scaling speedup up to 8,192 nodes for both linear and topography versions. On TACC’s Vista system, CUDA-aware MPI calls on GH200 GPUs substantially outperform their non-GPU-aware counterparts across all three solver versions. This poster will present a detailed evaluation of GPU-aware AWP-ODC using MVAPICH, including the impact of ZFP message compression compared to the native versions. Our results highlight the importance of Mvapich support for GPU-ware MPI and on-the-fly compression techniques for accelerating and scaling earthquake simulations.
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This content will become publicly available on August 20, 2026
Optimization of a Finite Difference Wave Propagation code with GPU-aware MPI support using MVAPICH2
We integrate GPU-aware MVAPICH2 in AWP-ODC, a scalable finite difference code for wave propagation in nonlinear media. On OLCF Frontier, HIP-aware MVAPICH2 yields a 17.8% T2S improvement over the non-GPU-aware version and achieves 95% parallel efficiency on 65,536 AMD MI250X GCDs. On TACC Vista, CUDA-aware MVAPICH2 delivers a 3.5% performance gain across 2-256 Nvidia GH200 GPUs, with parallel efficiencies of 82% in the linear case and 92% in the computationally more intense nonlinear case. We deploy the code for production-scale earthquake simulations on leadership-class systems
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- Award ID(s):
- 2311833
- PAR ID:
- 10630251
- Publisher / Repository:
- Annual MVAPICH USER Group (MUG) Meeting
- Date Published:
- Format(s):
- Medium: X
- Location:
- Columbus
- Sponsoring Org:
- National Science Foundation
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