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.