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.eduDepartment of Computer ScienceWashington 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. Conventional methods of implementing timer strategies do not provide adequate predictability and scalability. This paper describes the design, performance, and optimizations for building a family of timer strategies that are targeted for various types of real-time applications. This paper makes several contributions to the design and optimization of real-time Timer strategies. 1. Timer strategies play an integral role in real-time systems. 2. A Timer strategy is an abstract data type (ADT) that uses deadlines to rank the priority of timers that are scheduled with it. 2.1. 2.2. 3. 4. 5.
Dr. 6. Eternally Confuzzled - The Art of Hashing. By Julienne WalkerLicense: Public Domain In 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. An 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. Unfortunately, not all keys are easily used as a table address. Strings, for example, are not suitable, because array subscripting requires an integer. 1 unsigned index = key % N; Uses for hashing Pigeonhole Principle Perfect hashing XOR hash. Www.cse.wustl.edu/~schmidt/PDF/C++-USENIX-90.pdf. .:: General Purpose Hash Function Algorithms - By Arash Partow ::. Introduction Hash functions are by definition and implementation generally regarded as Pseudo Random Number Generators (PRNG).
From this generalization it can be assumed that the performance of hash functions and comparisons between other hash functions can be determined by modeling the functions 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 specific 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. In reality it is very difficult to find a perfect hash function for an arbitrary set of data, and furthermore the practical applications of perfect hashing and its variant minimal perfect hashing are quite limited. Data Distribution Hash Function Efficiency Bit Biases. Perfect Hashing. 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. For each bucket, use something like the code below to construct a perfect hash function that maps the n keys in that bucket to 0..n-1 uniquely, where n is about 200.
In addition, build an offset table with one value per bucket, where the value is the sum of the number of keys in all previous buckets. Perfect hashing guarantees that you get no collisions at all. C code and a sanity test Here is my C code for minimal perfect hashing, plus a test case. The generator is run like so, "perfect -nm < samperf.txt", and it produces the C files phash.h and phash.c. Usage There are options (taken by both perfect and the sanity test): perfect [-{NnIiHhDdAa}{MmPp}{FfSs}] < key.txt.
CMPH - C Minimal Perfect Hashing Library. Sitemap. MIT's Introduction to Algorithms, Lecture 08 (visit www.catonmat.net for notes) Compgeom.cs.uiuc.edu/~jeffe/teaching/373/notes/06-hashing.pdf.