Investigating the Impact of Suboptimal Hashing Functions

For the purpose of volumetric data, hashing acts to map multi-dimensional space into the one-dimensional space. Hashing is a popular method to store sparse data for the purporses of both gaming and computer graphics. Traditional methods used to hash 3D volumetric data utilise large prime numbers in an attempt to achieve well-distributed hash addresses to minimise addressing collisions. These methods generate hashing addressing through randomisation. However, it has been shown that when considering dynamic data, a low addressing collision rate cannot be guaranteed through this randomising technique. In this paper, a spatial hashing implementation is investigated, and whether varying performance parameters can be improved upon through the use of DECO Hashing. DECO leverages the inherent structure present in 3D data, which exists in the sense that each coordinate in 3D space is already unique. An open source version of Chisel is investigated - Open Chisel - and it is determined whether the algorithm can be improved upon through replacing the existing hashing function with DECO Hashing.