CUBE: A scalable framework for large-scale industrial simulations

Abstract

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 (IB) methods to accurately capture moving, complex geometries. The asymmetric workload of the IB 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.