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Real-time parallel hashing on the gpu (2009)
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| Venue: | In ACM SIGGRAPH Asia 2009 papers, SIGGRAPH ’09 |
| Citations: | 29 - 6 self |
BibTeX
@INPROCEEDINGS{Alcantara09real-timeparallel,
author = {Dan A. Alcantara and Andrei Sharf and Fatemeh Abbasinejad and Shubhabrata Sengupta and Michael Mitzenmacher and John D. Owens and Nina Amenta},
title = {Real-time parallel hashing on the gpu},
booktitle = {In ACM SIGGRAPH Asia 2009 papers, SIGGRAPH ’09},
year = {2009}
}
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Abstract
Figure 1: Overview of our construction for a voxelized Lucy model, colored by mapping x, y, and z coordinates to red, green, and blue respectively (far left). The 3.5 million voxels (left) are input as 32-bit keys and placed into buckets of ≤ 512 items, averaging 409 each (center). Each bucket then builds a cuckoo hash with three sub-tables and stores them in a larger structure with 5 million entries (right). Close-ups follow the progress of a single bucket, showing the keys allocated to it (center; the bucket is linear and wraps around left to right) and each of its completed cuckoo sub-tables (right). Finding any key requires checking only three possible locations. We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of elements in real-time. We consider two parallel algorithms for the construction: a classical sparse perfect hashing approach, and cuckoo hashing, which packs elements densely by allowing an element to be stored in one of multiple possible locations. Our construction is a hybrid approach that uses both algorithms. We measure the construction time, access time, and memory usage of our implementations and demonstrate real-time performance on large datasets: for 5 million key-value pairs, we construct a hash table in 35.7 ms using 1.42 times as much memory as the input data itself, and we can access all the elements in that hash table in 15.3 ms. For comparison, sorting the same data requires 36.6 ms, but accessing all the elements via binary search requires 79.5 ms. Furthermore, we show how our hashing methods can be applied to two graphics applications: 3D surface intersection for moving data and geometric hashing for image matching.
Keyphrases
real-time parallel hash table hybrid approach image matching input data surface intersection single bucket geometric hashing large datasets graphic application much memory efficient data-parallel algorithm cuckoo hash construction time cuckoo hashing key-value pair 32-bit key access time large hash table memory usage possible location multiple possible location voxelized lucy model cuckoo sub-tables binary search real-time performance classical sparse perfect
