Abstract
The extensive amount of data and contents generated today will require a paradigm shift in processing and management techniques for these data. One of the important data processing operations is the data sorting. Using multiple passes in external merge sort has a great influence on speeding up the sorting of extremely large data files. Since in large files, the swapping time is dominant in many applications, algorithms that minimize the swapping operations are normally superior to those which only focus on CPU time optimizations. In sorting extremely large files, external algorithms, such as the merge sort, are normally used. It is shown that using multiple passes over the data set, as proposed in our algorithm, has resulted in a great improvement in the number of swaps, thus, reducing the overall sorting time. Moreover, the proposed technique is suitable to be used with the emerging parallelization techniques such as GPUs. The reported results show the superiority of the proposed technique for “CPU only” and hybrid CPU–GPU implementations.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.