We introduce a new platform-portable hash table and collision-resolution approach, HashFight, for use in visualization and data analysis algorithms. Designed entirely in terms of dataparallel primitives (DPPs), HashFight is atomics-free and consists of a single code base that can
be invoked across a diverse range of architectures. To evaluate its hashing performance, we compare the single-node insert and query throughput of Hash- Fight to that of two best-in-class GPU and CPU hash table implementations, using several experimental configurations and factors. Overall,
HashFight maintains competitive performance across both modern and older generation GPU and CPU devices, which differ in computational and memory abilities. In particular, HashFight achieves stable performance across all hash table sizes, and has leading query throughput for the largest sets
of queries, while remaining within a factor of 1.5X of the comparator GPU implementation on all smaller query sets. Moreover, HashFight performs better than the comparator CPU implementation across all configurations. Our findings reveal that our platform-agnostic implementation can perform
as well as optimized, platform-specific implementations, which demonstrates the portable performance of our DPP-based design.