HyLiPoD: Parallel Particle Advection Via a Hybrid of Lifeline Scheduling and Parallelization-Over-Data

Performance characteristics of parallel particle advection algorithms can vary greatly based on workload. With this short paper, we build a new algorithm based on results from a previous bake-off study which evaluated the performance of four algorithms on a variety of workloads. Our algorithm, called HyLiPoD, is a “meta-algorithm,” i.e., it considers the desired workload to choose from existing algorithms to maximize performance. To demonstrate HyliPoD’s benefit, we analyze results from 162 tests including concurrencies of up to 8192 cores, meshes as large as 34 billion cells, and particle counts as large as 300 million. Our findings demonstrate that HyLiPoD’s adaptive approach allows it to match the best performance of existing algorithms across diverse workloads.