Comparison of the performance of skip lists and splay trees in classification of internet packets

Due to the increasing number of Internet users and the volume of information exchanged by software applications, Internet packet traffic has increased significantly, which has highlighted the need to accelerate the processing required in network systems. Packet classification is one of the solutions implemented in network systems. The most important issue is to use an approach that can classify packets at the speed of the network and show optimum performance in terms of memory usage. In this study, we evaluated the performance in packet classification of two of the most important data structures used in decision trees, i.e. the skip list and splay tree. Our criteria for performance were the time of packet classification, the number of memory accesses, and memory usage of each event. These criteria were tested by the ACL and IPC rules with different numbers of rules as well as by different packet numbers. The results of the evaluation showed that the performance of skip lists is higher than that of splay trees. By increasing the number of classifying rules, both the difference in the speed of packet classification and the superiority of the performance of the skip list over that of the splay tree become more significant. The skip list also maintains its superiority over the splay tree in lower memory usage. The results of the experiments confirm the scalability of this method in comparison to the splay tree method.

GUNDAM: a toolkit for fast spatial correlation functions in galaxy surveys

ABSTRACT We describe the capabilities of a new software package to calculate two-point correlation functions (2PCFs) of large galaxy samples. The code can efficiently estimate 3D/projected/angular 2PCFs with a variety of statistical estimators and bootstrap errors, and is intended to provide a complete framework (including calculation, storage, manipulation, and plotting) to perform this type of spatial analysis with large redshift surveys. Gundam implements a very fast skip list/linked list algorithm that efficiently counts galaxy pairs and avoids the computation of unnecessary distances. It is several orders of magnitude faster than a naive pair counter, and matches or even surpass other advanced algorithms. The implementation is also embarrassingly parallel, making full use of multicore processors or large computational clusters when available. The software is designed to be flexible, user friendly and easily extensible, integrating optimized, well-tested packages already available in the astronomy community. Out of the box, it already provides advanced features such as custom weighting schemes, fibre collision corrections and 2D correlations. Gundam will ultimately provide an efficient toolkit to analyse the large-scale structure ‘buried’ in upcoming extremely large data sets generated by future surveys.