Counting sort

About: Counting sort is a research topic. Over the lifetime, 476 publications have been published within this topic receiving 15073 citations. The topic is also known as: Counting Sort.

Sorting networks and their applications

Abstract: To achieve high throughput rates today's computers perform several operations simultaneously. Not only are I/O operations performed concurrently with computing, but also, in multiprocessors, several computing operations are done concurrently. A major problem in the design of such a computing system is the connecting together of the various parts of the system (the I/O devices, memories, processing units, etc.) in such a way that all the required data transfers can be accommodated. One common scheme is a high-speed bus which is time-shared by the various parts; speed of available hardware limits this scheme. Another scheme is a cross-bar switch or matrix; limiting factors here are the amount of hardware (an m × n matrix requires m × n cross-points) and the fan-in and fan-out of the hardware.

Fast algorithms for sorting and searching strings

Abstract: We present theoretical algorithms for sorting and searching multikey data, and derive from them practical C implementations for applications in which keys are character strings. The sorting algorithm blends Quicksort and radix sort; it is competitive with the best known C sort codes. The searching algorithm blends tries and binary search trees; it is faster than hashing and other commonly used search methods. The basic ideas behind the algorithms date back at least to the 1960s, but their practical utility has been overlooked. We also present extensions to more complex string problems, such as partial-match searching.

Efficient C4.5 [classification algorithm]

Abstract: We present an analytic evaluation of the runtime behavior of the C4.5 algorithm which highlights some efficiency improvements. Based on the analytic evaluation, we have implemented a more efficient version of the algorithm, called EC4.5. It improves on C4.5 by adopting the best among three strategies for computing the information gain of continuous attributes. All the strategies adopt a binary search of the threshold in the whole training set starting from the local threshold computed at a node. The first strategy computes the local threshold using the algorithm of C4.5, which, in particular, sorts cases by means of the quicksort method. The second strategy also uses the algorithm of C4.5, but adopts a counting sort method. The third strategy calculates the local threshold using a main-memory version of the RainForest algorithm, which does not need sorting. Our implementation computes the same decision trees as C4.5 with a performance gain of up to five times.

Slowing Down Sorting Networks to Obtain Faster Sorting Algorithms

Abstract: Megiddo introduced a technique for using a parallel algorithm for one problem to construct an efficient serial algorithm for a second problem. We give a general method that trims a factor o f 0(logn) time (or more) for many applications of this technique.