Evolution of physical-based simulations and Computational Fluid Dynamics (CFD) in particular has fundamentally reshaped the design and engineering process in the last several decades. However, in spite of noteworthy success, today's CFD still remains limited to a small design space. A critical bottleneck is the inability of current numerical methods and algorithms to predict turbulent-separated flows.

At the same time, today’s supercomputing platforms are based on massive parallelism and heterogeneous processor designs to deliver exascale performance. This project aims at transforming CFD simulation capabilities through a combination of novel numerical schemes and deep learning techniques suited for hybrid architectures and efficient parallel algorithms and implementation strategies to scale on large-scale distributed-memory systems.

HiPer is a new CFD solver that incorporates the above research advances to enable high-performance fluid simulations. Check-out our new multi-block structured mesh partitioner (SMP), pipelined distributed stencil algorithm (Pencil), and DL-based accelerator (CFDNet) thats part of HiPer.

This project is supported by an NSF OAC CAREER award.