gperf is a "software-tool generating-tool" designed to automate the generation of perfect hash functions. This paper describes the features, algorithms, and object-oriented design and implementation strategies incorporated in gperf.Italso presents the results from an empirical comparison between gperf-generated recognizers and other popular techniques for reserved word lookup. gperf is distributed with the GNU libg++ library and is used to generate the keyword recognizers for the GNU C and GNU C++ compilers. 1 Introduction Perfect hash functions are a time and space efficient implementation of static search sets, which are ADTs with operations like initialize, insert,andretrieve. Static search sets are common in system software applications. Typical static search sets include compiler and interpreter reserved words, assembler instruction mnemonics, and shell interpreter builtin commands. Search set elements are called keywords.Key- words are inserted into the set once, usually at c...

Using a trace of address references, we compared the efficiency of several different hashing functions, such as cyclic redundancy checking (CRC) polynomials, Fletcher checksum, folding of address octets using the exclusive-or operation, and bit extraction from the address. Guidelines are provided for determining the size of hash mask required to achieve a specified level of performance. 1 INTRODUCTION The trend toward networks becoming larger and faster, addresses becoming larger, has impelled a need to explore alternatives for fast address recognition. This problem is actually a special case of the general problem of searching through a large data base and finding the information associated with a given key. For example, Datalink adapters on local area networks (LAN) need to recognize the multicast destination addresses of frames on the LAN. Bridges, used to interconnect two or more LANs, have to recognize the destination addresses of every frame and decide quickly whether to receive...

A new algorithm for generating order preserving minimal perfect hash functions is presented. The algorithm is probabilistic, involving generation of random graphs. It uses expected linear time and requires a linear number words to represent the hash function, and thus is optimal up to constant factors. It runs very fast in practice. Keywords: Data structures, probabilistic algorithms, analysis of algorithms, hashing, random graphs

Our previous research on one-probe access to large collec-tions of data indexed by alphanumeric keys has produced the first practical minimal perfect hash functions for this prob-lem. Here, a new algorithm is described for quickly finding minimal perfect hash functions whose specification space is very close to the theoretical lower bound, i.e., around 2 bits per key. The various stages of processing are detailed, along with analytical and empirical results, including timing for a set of over 3.8 million keys that was processed on a NeXTsta-tion in about 6 hours. 1