A comparison of sorting algorithms for the connection machine CM-2
SPAA '91 Proceedings of the third annual ACM symposium on Parallel algorithms and architectures
Software—Practice & Experience
A framework for adaptive sorting
Discrete Applied Mathematics
Splaysort: fast, versatile, practical
Software—Practice & Experience
Proceedings of the eighth annual ACM symposium on Parallel algorithms and architectures
Fast Parallel Sorting Under LogP: Experience with the CM-5
IEEE Transactions on Parallel and Distributed Systems
Load balanced parallel radix sort
ICS '98 Proceedings of the 12th international conference on Supercomputing
A killer adversary for quicksort
Software—Practice & Experience
Optimistic sorting and information theoretic complexity
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Some Combinatorial Properties of Certain Trees With Applications to Searching and Sorting
Journal of the ACM (JACM)
Samplesort: A Sampling Approach to Minimal Storage Tree Sorting
Journal of the ACM (JACM)
Improved master theorems for divide-and-conquer recurrences
Journal of the ACM (JACM)
Concrete Math
Optimal Sampling Strategies in Quicksort and Quickselect
SIAM Journal on Computing
SIAM Journal on Computing
Nordic Journal of Computing
Optimal Sampling Strategies in Quicksort
ICALP '98 Proceedings of the 25th International Colloquium on Automata, Languages and Programming
Building a new sort function for a C library
Software—Practice & Experience
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The purpose of the paper is twofold. First, we want to search for a more efficient sample sort. Secondly, by analyzing a variant of Samplesort, we want to settle an open problem: the average case analysis of Proportion Extend Sort (PEsort for short). An efficient variant of Samplesort given in the paper is called full sample sort. This algorithm is simple. It has a shorter object code and is almost as fast as PEsort. Theoretically, we show that full sample sort with a linear sampling size performs at most n log n = O(n) comparisons and O(n log n) exchanges on the average, but O(n log2 n) comparisons in the worst case. This is an improvement on the original Samplesort by Frazer and McKellar, which requires n log n + O(n log log n) comparisons on the average and O(n2) comparisons in the worst case. On the other hand, we use the same analyzing approach to show that PEsort, with any p 0, performs also at most n log n + O(n) comparisons on the average. Notice, Cole and Kandathil analyzed only the case p = 1 of PEsort. For any p 0, they did not. Namely, their approach is suitable only for a special case such as p = 1, while our approach is suitable for the generalized case.