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Author Biggar, Paul ♦ Nash, Nicholas ♦ Williams, Kevin ♦ Gregg, David
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Sorting ♦ Branch prediction ♦ Caching ♦ Pipeline architectures
Abstract Sorting is one of the most important and well-studied problems in computer science. Many good algorithms are known which offer various trade-offs in efficiency, simplicity, memory use, and other factors. However, these algorithms do not take into account features of modern computer architectures that significantly influence performance. Caches and branch predictors are two such features and, while there has been a significant amount of research into the cache performance of general purpose sorting algorithms, there has been little research on their branch prediction properties. In this paper, we empirically examine the behavior of the branches in all the most common sorting algorithms. We also consider the interaction of cache optimization on the predictability of the branches in these algorithms. We find insertion sort to have the fewest branch mispredictions of any comparison-based sorting algorithm, that bubble and shaker sort operate in a fashion that makes their branches highly unpredictable, that the unpredictability of shellsort's branches improves its caching behavior, and that several cache optimizations have little effect on mergesort's branch mispredictions. We find also that optimizations to quicksort, for example the choice of pivot, have a strong influence on the predictability of its branches. We point out a simple way of removing branch instructions from a classic heapsort implementation and also show that unrolling a loop in a cache-optimized heapsort implementation improves the predicitability of its branches. Finally, we note that when sorting random data two-level adaptive branch predictors are usually no better than simpler bimodal predictors. This is despite the fact that two-level adaptive predictors are almost always superior to bimodal predictors, in general.
ISSN 10846654
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-06-12
Publisher Place New York
e-ISSN 10846654
Journal Journal of Experimental Algorithmics (JEA)
Volume Number 12
Page Count 39
Starting Page 1
Ending Page 39


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Source: ACM Digital Library