Efficiently Simulating Lagrangian Particles in Large-Scale Ocean Flows – Data Structures and their Impact on Geophysical Applications

Abstract

Studying oceanography by using Lagrangian simulations has been adopted for a range of scenarios, such as the determining the fate of microplastics in the ocean, simulating the origin locations of microplankton used for palaeoceanographic reconstructions, for studying the impact of fish aggregation devices on the migration behaviour of tuna. These simulations are complex and represent a considerable runtime effort to obtain trajectory results, which is the prime motivation for enhancing the performance of Lagrangian particle simulators. This paper analyses and compares established performance enhancing technique from Eulerian simulators with the computational conditions and demands of Lagrangian simulators. A performance enhancement strategy specifically targeting physics-based Lagrangian particle simulations is outlined to address the performance gaps, and techniques for closing the performance gap are presented and implemented. Realistic experiments are derived from three specific oceanographic application scenarios, and the suggested performance-enhancing techniques are benchmarked in detail, so to allow for a good attribution of speed-up measurements to individual techniques. The impacts and insights from the performance enhancement strategy are further discussed for Lagrangian simulations in other geoscientific applications. The experiments show that I/O-enhancing techniques, such as dynamic loading and buffering, lead to considerable speed-up on-par with an idealised parallelisation of the process over 20 nodes. Conversely, alternative data structures to a CPU cache-efficient structure-of-arrays do not fulfill the theoretically-expected performance increase, which also demonstrates the importance of good cache alignment for Lagrangian physics simulations.