2015/06/16 by Martin Schreiber, Adam Peddle, Schreiber, Martin +5
Computer Science · Earth and Planetary Sciences · Mathematics · #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Meteorological Phenomena and Simulations #Numerical methods for differential equations #Parallel #Performance (cs.PF) #and Cluster Computing (cs.DC) #cs.DC #cs.PF
paper · pdf · doi:10.48550/arxiv.1506.05157
openalex publication_date 2015/06/16 · arxiv created 2016/02/27 · arxiv updated 2016/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With steadily increasing parallelism for high-performance architectures, simulations requiring a good strong scalability are prone to be limited in scalability with standard spatial-decomposition strategies at a certain amount of parallel processors. This can be a show-stopper if the simulation results have to be computed with wallclock time restrictions (e.g. for weather forecasts) or as fast as possible (e.g. for urgent computing). Here, the time-dimension is the only one left for parallelization and we focus on Parareal as one particular parallelization-in-time method. We discuss a software approach for making Parareal parallelization transparent for application developers, hence allowing fast prototyping for Parareal. Further, we introduce a decentralized Parareal which results in autonomous simulation instances which only require communicating with the previous and next simulation instances, hence with strong locality for communication. This concept is evaluated by a prototypical solver for the rotational shallow-water equations which we use as a representative black-box solver.