Abstract. We explain the counterintuitive observation that finding “good ” pivots (close to the median of the array to be partitioned) may not improve performance of quicksort. Indeed, an intentionally skewed pivot improves performance. The reason is that while the instruction count decreases with the quality of the pivot, the likelihood that the direction of a branch is mispredicted also goes up. We analyze the effect of simple branch prediction schemes and measure the effects on real hardware. 1

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 behaviour 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 which makes their branches highly unpredictable, that the unpredictability of shellsort’s branches improves its caching behaviour 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 show also 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.

With monthly feature peer-reviewed articles and technical contributions, the Journal's content is dynamic, innovative, thoughtprovoking and directly beneficial to the readers in their work. The number of submissions have increased dramatically over the last issues. Our ability to accommodate this growth is due in large part to the terrific work of our Editorial Board. Some of the papers have an introductory character, some of them access highly desired extensions for a particular method, and some of them even introduce completely new approaches to computer science research in a very efficient manner. This diversity was strongly desired and should contribute to evoke a picture of this field at large. As a consequence only 29 % of the received articles have been finally accepted for publication. With respect to all the contributions, we are happy to have assembled researchers whose names are linked to the particular manuscript they are discussing. Therefore, this issue may not just be used by the reader to get an introduction to the methods but also to the people behind that have been pivotal in the promotion of the respective research. By having in mind such future issues, we hope to establish a regular outlet for contributions and new findings in the field of Computer science and applications. Therefore, IJACSA in general, could serve as a reliable resource for everybody loosely or tightly attached to this field of science. And if only a single young researcher is inspired by this issue to contribute in the future to solve some of the problems sketched

Abstract — Sorting an array of integers is one of the most basic problems in Computer Science. Also it is an issue in high performance database applications. Though literature is imbued with a variety of sorting algorithms, different architectures need different optimizations to reduce sorting time. This paper presents a Multicore ready parallel sorting algorithm which has been designed with Multicore/Manycore architecture in mind. Our study shows that the proposed algorithm is excellent for large input size and multiple free cores. In essence algorithm has potential to be a success in situations when one has large input and machine is a Multicore machine. The paper does not neglect overhead involved with parallel programming and suggests two system calls to check the availability of free cores and to reserve a core for a fixed time quantum. Keywords — Multicore. I.