Acceleration strategies for large-scale sequential simulations using parallel neighbour search: Non-LVA and LVA scenarios

This paper describes the application of acceleration techniques into existing implementations of Sequential Gaussian Simulation and Sequential Indicator Simulation. These implementations might incorporate Locally Varying Anisotropy (LVA) to capture non-linear features of the underlying physical phenomena. The implementation focuses on a novel parallel neighbour search algorithm, which can be used on both non-LVA and LVA codes. Additionally, parallel shortest path executions and optimized linear algebra libraries are applied with focus on LVA codes. Execution time, speedup and accuracy results are presented. Non-LVA codes are benchmarked using two scenarios with approximately 50 million domain points each. Speedup results of 2x and 4x were obtained on SGS and SISIM respectively, where each scenario is compared against a baseline code published in Peredo et al. (2018). The aggregated contribution to speedup of both works results in 12x and 50x respectively. LVA codes are benchmarked using two scenarios with approximately 1.7 million domain points each. Speedup results of 56x and 1822x were obtained on SGS and SISIM respectively, where each scenario is compared against the original baseline sequential codes.

Automated summary

This paper discusses the application of acceleration techniques to Sequential Gaussian Simulation and Sequential Indicator Simulation, with a focus on a parallel neighbor search algorithm and optimized linear algebra libraries. The results show significant speedup for both non-LVA and LVA codes.