Robin Hood hashing
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Journal of Algorithms
ACM SIGGRAPH 2006 Papers
External perfect hashing for very large key sets
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Real-time parallel hashing on the GPU
ACM SIGGRAPH Asia 2009 papers
Addressing for random-access storage
IBM Journal of Research and Development
SMI 2012: Full A runtime cache for interactive procedural modeling
Computers and Graphics
Real-time 3D reconstruction at scale using voxel hashing
ACM Transactions on Graphics (TOG)
Hardware-oblivious parallelism for in-memory column-stores
Proceedings of the VLDB Endowment
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Recent spatial hashing schemes hash millions of keys in parallel, compacting sparse spatial data in small hash tables while still allowing for fast access from the GPU. Unfortunately, available schemes suffer from two drawbacks: Multiple runs of the construction process are often required before success, and the random nature of the hash functions decreases access performance. We introduce a new parallel hashing scheme which reaches high load factor with a very low failure rate. In addition our scheme has the unique advantage to exploit coherence in the data and the access patterns for faster performance. Compared to existing approaches, it exhibits much greater locality of memory accesses and consistent execution paths within groups of threads. This is especially well suited to Computer Graphics applications, where spatial coherence is common. In absence of coherence our scheme performs similarly to previous methods, but does not suffer from construction failures. Our scheme is based on the Robin Hood scheme modified to quickly abort queries of keys that are not in the table, and to preserve coherence. We demonstrate our scheme on a variety of data sets. We analyze construction and access performance, as well as cache and threads behavior.