The Usefulness of Multilevel Hash Tables with Multiple Hash Functions in Large Databases

In this work, attempt is made to select three good hash functions which uniformly distribute hash values that permute their internal states and allow the input bits to generate different output bits. These functions are used in different levels of hash tables that are coded in Java Programming Language and a quite number of data records serve as primary data for testing the performances. The result shows that the two-level hash tables with three different hash functions give a superior performance over one-level hash table with two hash functions or zero-level hash table with one function in term of reducing the conflict keys and quick look-up for a particular element. The result assists to reduce the complexity of join operation in query language from O(n2) to O(1) by placing larger query result, if any, in multilevel hash tables with multiple hash functions and generate shorter query result

Avoiding Rotated Bitboards with Direct Lookup

This paper describes an approach for obtaining direct access to the attacked squares of sliding pieces without resorting to rotated bitboards. The technique involves creating four hash tables using the built in hash arrays from an interpreted, high level language. The rank, file, and diagonal occupancy are first isolated by masking the desired portion of the board. The attacked squares are then directly retrieved from the hash tables. Maintaining incrementally updated rotated bitboards becomes unnecessary as does all the updating, mapping and shifting required to access the attacked squares. Finally, rotated bitboard move generation speed is compared with that of the direct hash table lookup method.Comment: 7 pages, 1 figure, 4 listings; replaced test positions, fixed typo

Analysing the Performance of GPU Hash Tables for State Space Exploration

In the past few years, General Purpose Graphics Processors (GPUs) have been used to significantly speed up numerous applications. One of the areas in which GPUs have recently led to a significant speed-up is model checking. In model checking, state spaces, i.e., large directed graphs, are explored to verify whether models satisfy desirable properties. GPUexplore is a GPU-based model checker that uses a hash table to efficiently keep track of already explored states. As a large number of states is discovered and stored during such an exploration, the hash table should be able to quickly handle many inserts and queries concurrently. In this paper, we experimentally compare two different hash tables optimised for the GPU, one being the GPUexplore hash table, and the other using Cuckoo hashing. We compare the performance of both hash tables using random and non-random data obtained from model checking experiments, to analyse the applicability of the two hash tables for state space exploration. We conclude that Cuckoo hashing is three times faster than GPUexplore hashing for random data, and that Cuckoo hashing is five to nine times faster for non-random data. This suggests great potential to further speed up GPUexplore in the near future.Comment: In Proceedings GaM 2017, arXiv:1712.0834