Writing high performance solvers for engineering applications is a delicate task. These codes are often developed on an application to application basis, highly optimized to solve a certain problem. Here, we present our work on developing a general simulation framework for efficient computation of time resolved approximations of complex industrial flow problems - Complex Unified Building cubE method (Cube). To address the challenges of emerging, modern supercomputers, suitable data structures and communication patterns are developed and incorporated into Cube. We use a Cartesian grid together with various immersed boundary methods to accurately capture moving, complex geometries. The asymmetric workload of the immersed boundary is balanced by a predictive dynamic load balancer, and a multithreaded halo-exchange algorithm is employed to efficiently overlap communication with computations. Our work also concerns efficient methods for handling the large amount of data produced by large-scale flow simulations, such as scalable parallel I/O, data compression and in-situ processing.
@article{arxiv.1808.04099,
title = {CUBE: A scalable framework for large-scale industrial simulations},
author = {Niclas Jansson and Rahul Bale and Keiji Onishi and Makoto Tsubokura},
journal= {arXiv preprint arXiv:1808.04099},
year = {2018}
}