Decision Sort and its Parallel Implementation

TL;DR

This paper introduces Decision Sort, a linear-time sorting algorithm for datasets with known key domains, and discusses its parallel implementation and advantages for large or partitioned data.

Contribution

The paper presents Decision Sort and its parallel version, demonstrating their efficiency and broad applicability for large-scale and partitioned data sorting tasks.

Findings

Decision Sort operates in O(n+k) time for known key domains.

Parallel implementation improves efficiency for large datasets.

Particularly effective for incremental and large-scale data sorting.

Abstract

In this paper, a sorting technique is presented that takes as input a data set whose primary key domain is known to the sorting algorithm, and works with an time efficiency of O(n+k), where k is the primary key domain. It is shown that the algorithm has applicability over a wide range of data sets. Later, a parallel formulation of the same is proposed and its effectiveness is argued. Though this algorithm is applicable over a wide range of general data sets, it finds special application (much superior to others) in places where sorting information that arrives in parts and in cases where input data is huge in size.