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Timer Hash
Design, Performance, and Optimization of Timer Strategies for Real-time ORBs Darrell Brunsch , and Douglas C. Schmidt brunsch@cs.wustl.edu , schmidt@cs.wustl.edu Department of Computer Science Washington University, St Louis, MO 63130, (TEL) (314) 935-4502 (FAX) (314) 935-7302 Abstract Real-time ORBs and their applications require predictable, efficient, and flexible timer support.
Eternally Confuzzled - The Art of Hashing
By Julienne Walker License: Public Domain I n Binary Search Trees I, II, and III, we considered methods for efficient searching of an ordered collection by using key comparisons. While these methods were indeed very fast, they were limited to O(log N) performance due to the comparison tree inherent in the data structure. Binary search trees are also somewhat complicated, especially when the chance of encountering a degenerate tree is minimized, or removed entirely. A n alternative method for searching uses they key itself an an address into the data structure, thus breaking the O(log N) barrier and allowing searches to be performed with an expected time complexity of O(1), which is as good as it gets when it comes to searching, and algorithms in general.
Description Hash functions are by definition and implementation pseudo random number generators (PRNG). From this generalization its generally accepted that the performance of hash functions and also comparisons between hash functions can be achieved by treating hash function as PRNGs. Analysis techniques such a Poisson distribution can be used to analyze the collision rates of different hash functions for different groups of data. In general there is a theoretical hash function known as the perfect hash function for any group of data. The perfect hash function by definition states that no collisions will occur meaning no repeating hash values will arise from different elements of the group.
.:: General Purpose Hash Function Algorithms - By Arash Partow ::.
A better way to do perfect hashing for large sets is by Botelho and Ziviani . I don't know if it can be as fast as this, but it scales way better. Their trick: instead of constructing the whole perfect hash function at once, use a normal hash h(key) to split the 200 zillion keys into a zillion buckets with about 200 keys each.
Perfect Hashing
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