Rapid calculation for avalanche maps by GPGPU-based snow avalanche model

Abstract

The required time for producing snow avalanche maps is influenced by computation speed of simulations. Commonly, integrating terrain assessment with dynamic flow simulation aids in mapping dangerous areas for human and structural threats. This approach enables the evaluation of avalanche paths, as well as the assessment of flow rate and thickness during avalanche movement. However, the substantial computational cost of the simulation results in long calculation times when using the Central Processing Unit (CPU). In this study, a new rapid snow avalanche simulator was developed by applying massively parallel computation with the General-Purpose computing on Graphics Processing Unit (GPGPU) technique. By avoiding slower data transfer and utilizing faster memory, computational speed could be accelerated up to 80 times faster than conventional simulation using a CPU. Additionally, the rapid calculation models were validated based on the Mt. Nasu event in 2017, and pilot studies of the avalanche map of Mt. Nasu in Japan demonstrated the usefulness of the developed model for vulnerability evaluation. A total of 123 simulations were conducted for each susceptible source area, and all simulations were completed within only 6.5 h. This high-performance calculation can significantly reduce the time cost of producing and expanding conventional avalanche maps.