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The issue of productivity and energy is an important objective of the optimization of parallel applications. The size of the problem for a large number of data on multiprocessor platforms forces the use of parallel algorithms. Efficient management of large memories using modern processors in Big data processing requires innovative techniques and efficient algorithms. For years have found the results of tests conducted on methods for use in various computing environments and improvements. This article shows the energy consumption analysis by parallel sorting algorithms. Sort algorithms are used in information systems and databases, to select and organize the information. The subject of this article is research into energy consumption and computational complexity for parallel sorting methods by merging compared to classic methods. The tests carried out confirm the reduction of energy consumption by using parallel sorting algorithms. The presented parallel fast sort and parallel modified merge sort for large task dimensions have less power consumption than classic methods and can be used successfully in NoSQL databases. [ABSTRACT FROM AUTHOR]

Modern architectures make possible development in new algorithms for large data sets and distributed computing. The newly proposed versions can benefit both from faster computing on the multi core architectures, and intelligent programming techniques that use efficient procedures available in the latest programming studios. Frequently used algorithms to sort arrays of data in NoSQL databases is merge sort, where as NoSQL we understand any database without typical SQL programming interpreter. The author describes how to use the parallelization of the sorting processes for the modified method of sorting by merging for large data sets. The subject of this research is the claim that the parallelization of the sorting method is faster and beneficial for multi-core systems. Presented results show how the number of processors influences the sorting performance. The results are presented in theoretical assumptions and confirmed in practical benchmark tests. The method is compared to other sorting methods like quick sort, heap sort, and merge sort to show potential efficiency. [ABSTRACT FROM AUTHOR]