Coherent parallel hashing

these pixels are shown as colors in (b). We store the image in a hash table under a 0.99 load factor: the hash table contains only 3.73 million entries. These are used as keys for hashing. (c) The table obtained with a typical randomizing hash function: Keys are randomly spread and all coherence is lost. (d) Our spatially coherent hash table, built in parallel on the GPU. The table is built in 15 ms on a GeForce GTX 480, and the image is reconstructed from the hash in 3.5 ms. The visible structures are due to preserved coherence. This translates to faster access as neighboring threads perform similar operations and access nearby memory. (e) Neighboring keys are kept together during probing, thereby improving the coherence of memory accesses of neighboring threads. Recent spatial hashing schemes hash millions of keys in parallel, compacting sparse spatial data in small hash tables while still allow-ing for fast access from the GPU. Unfortunately, available schemes suffer from two drawbacks: Multiple runs of the construction pro-cess 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 ha