Massively Parallel Stencil Code Solver with Autonomous Adaptive Block Distribution

In the last decades, simulations have been established in several fields of science and industry to study various phenomena by solving, inter alia, partial differential equations. For an efficient use of current and future high performance computing systems, with many thousands of computation ranks, high node-level performance, scalable communication, and the omission of unnecessary calculations are of high priority in the development of new solvers. The challenge of contemporary simulation applications is to bridge the gap between the scales of the various physical processes. We introduce the NAStJA framework, a block-based MPI parallel solver for arbitrary algorithms, based on stencil code or other regular grid methods. NAStJA decomposes the domain of spatially complex structures into small cuboid blocks. A special feature of NAStJA is the dynamic block adaption which modifies the calculation domain around the region where the computation currently takes place, and hence avoids unnecessary calculations. This often occurs, inter alia, in phase-field simulations. Block creation and deletion is managed autonomously within local neighborhoods. A basic load balancing mechanism allows a re-distribution of newly created blocks to the involved computing ranks. The use of a multi-hop network, to distribute information to the entire domain, avoids collective all-gather communications. Thus, we can demonstrate excellent scaling. The present scaling tests substantiate the enormous advantage of this adaptive method. For certain simulation scenarios, we can show that the calculation effort and memory consumption can be reduced to only 3.5 percent, compared to the classical full-domain reference simulation. The overhead of 70-100 percent for the dynamic adapting block creation is significantly lower than the gain. The approach is not restricted to phase-field simulations, and can be employed in other domains of computational science to exploit sparsity of computing regions.