Abstract
Sorting algorithms are among the most commonly used algorithms in computer science and modern software. Having efficient implementation of sorting is necessary for a wide spectrum of scientific applications. This paper describes the sorting algorithm written using the partitioned global address space (PGAS) model, implemented using the Parallel Computing in Java (PCJ) library. The iterative implementation description is used to outline the possible performance issues and provide means to resolve them. The key idea of the implementation is to have an efficient building block that can be easily integrated into many application codes. This paper also presents the performance comparison of the PCJ implementation with the MapReduce approach, using Apache Hadoop TeraSort implementation. The comparison serves to show that the performance of the implementation is good enough, as the PCJ implementation shows similar efficiency to the Hadoop implementation.
Highlights
Sorting is one of the most fundamental algorithmic problems found in a wide range of fields
The implementation using the Parallel Computing in Java (PCJ) library was presented in an iterative way that shows the possible performance problems and the ways to overcome them
The comparison of TeraSort implementations indicates that PCJ performance is similar to Hadoop for a properly configured cluster and even more efficient when using on clusters with drawbacks in the configuration
Summary
Sorting is one of the most fundamental algorithmic problems found in a wide range of fields. The basic metrics for data analysis include minimum, maximum, median, and top-K values. It is easy to write simple O(n) algorithms that are not using sorting to calculate the first three of those metrics, but finding the median value and its variants require more work. Hoare’s quickselect algorithm [1] can be used for finding the median of unsorted data, but its worst-case time complexity is O(n2). Some algorithms, like binary search, require data to be sorted before execution. Existing O(n) sorting algorithms, adapted for parallel execution, like count sort [2, 3] or radix sort [4, 5], require specific input data structure, which limits their application to more general cases
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