As the push towards exascale hardware has increased the diversity of system architectures, performance portability has become a critical aspect for scientific software. We describe the Kokkos Performance Portable Programming Model that allows developers to write single source applications for diverse high-performance computing architectures. Kokkos provides key abstractions for both the compute and memory hierarchy of modern hardware. We describe the novel abstractions that have been added to Kokkos version 3 such as hierarchical parallelism, containers, task graphs, and arbitrary-sized atomic operations to prepare for exascale era architectures. We demonstrate the performance of these new features with reproducible benchmarks on CPUs and GPUs.

Particle advection is a foundational operation for many flow visualization techniques, including streamlines, Finite-Time Lyapunov Exponents (FTLE) calculation, and stream surfaces. The workload for particle advection problems varies greatly, including significant variation in computational requirements. With this study, we consider the performance impacts from hardware architecture on this problem, studying distributed-memory systems with CPUs with varying amounts of cores per node, and with nodes with one to three GPUs. Our goal was to explore which architectures were best suited to which workloads, and why. While the results of this study will help inform visualization scientists which architectures they should use when solving certain flow visualization problems, it is also informative for the larger HPC community, since many simulation codes will soon incorporate visualization via in situ techniques.