Top-K and selection operations are critical in data processing and analysis, and their efficient implementation on GPUs is increasingly important due to the growing demands of data analysis. Existing methods, primaril...
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Top-K and selection operations are critical in data processing and analysis, and their efficient implementation on GPUs is increasingly important due to the growing demands of data analysis. Existing methods, primarily relying on the bucket partition execution model, encounter challenges such as uneven bucket distribution and latency in merging processes. To address these issues, we introduce a novel Split-Bucket Partition (SBP) execution model that specifically addresses these challenges. Additionally, we propose task and control flow optimizations targeted at top-K and selection algorithms, which further contribute to performance improvements. Our optimized algorithms significantly outperform existing approaches, delivering performance gains of up to 2.3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.3$$\end{document} times and 1.6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.6$$\end{document} times for different bucket partitioning rules. Our algorithms show robust performance improvements in non-uniform data scenarios, with gains ranging from 1.9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.9$$\end{document} times to 15.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$15.5$$\end{document} times. However, it should be noted that the SB
selection finding, and its most common form median finding, are used as a measure of central tendency for problems in biology, databases, and graphics. These problems often require selection finding as a subcomponent ...
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ISBN:
(纸本)9781479912926;9781479912933
selection finding, and its most common form median finding, are used as a measure of central tendency for problems in biology, databases, and graphics. These problems often require selection finding as a subcomponent where it can be called many times, and as such speed is important. The Map/Reduce framework has been shown to be an important tool for creating scalable applications. There are a number of valid implementations of the selection algorithms inside of a Map/Reduce framework, certain of which are compared in this paper. However, as the volume of data increases, subtle theoretical algorithmic implementation differences can lead to significant differences in practical application. Therefore, an efficient and scalable selection finding method has the potential to provide general benefit to a number of applications. This paper compares algorithms that have been redesigned or created for the Map/Reduce framework for the purpose of selection finding, or, finding the k-th ranked element in an unordered set. This paper takes the concepts used from two existing selection algorithms and translates them into a novel method using the Map/Reduce framework with two variations. Each approach uses a different methodology to reduce the total amount of workload needed for a selection. All the algorithms are compared together for scalability and efficiency in a computing cluster environment with up to 256 processing cores. The results show that the methods proposed in this paper outperform several common alternatives in identifying medians with Hadoop, including using sorting, Pig, and BinMedian methods. Our implementations are also available upon request.
It is well known that a series-parallel multigraph G can be constructed recursively from its edges. This construction is represented by a binary decomposition tree. This is a rooted binary tree T in which each vertex ...
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It is well known that a series-parallel multigraph G can be constructed recursively from its edges. This construction is represented by a binary decomposition tree. This is a rooted binary tree T in which each vertex q corresponds to some series-parallel submultigraph of G, denoted by G(q)
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