Glyph-based visualization is an effective tool for depicting multivariate information. Since sorting is one of the most common analytical tasks performed on individual attributes of a multi-dimensional dataset, this motivates the hypothesis that introducing glyph sorting would significantly enhance the usability of glyph-based visualization. In this article, we present a glyph-based conceptual framework as part of a visualization process for interactive sorting of multivariate data. We examine several technical aspects of glyph sorting and provide design principles for developing effective, visually sortable glyphs. Glyphs that are visually sortable provide two key benefits: (1) performing comparative analysis of multiple attributes between glyphs and (2) to support multi-dimensional visual search. We describe a system that incorporates focus and context glyphs to control sorting in a visually intuitive manner and for viewing sorted results in an interactive, multi-dimensional glyph plot that enables users to perform high-dimensional sorting, analyse and examine data trends in detail. To demonstrate the usability of glyph sorting, we present a case study in rugby event analysis for comparing and analysing trends within matches. This work is undertaken in conjunction with a national rugby team. From using glyph sorting, analysts have reported the discovery of new insight beyond traditional match analysis.
Sorting is the computational process of rearranging a sequence of items into ascending or descending order @cite_39 . Many sorting algorithms have been proposed, including bubble sort by Demuth @cite_17 , merge sort by von Neumannr @cite_39 , and quick sort by Hoare @cite_4 . Since best and worse case performance runtime can vary drastically with such algorithms, further research continues to propose new sorting techniques @cite_30 and adaptive approaches that utilise ordered data @cite_33 . Our work is not focused on a faster sorting algorithm per say, but combining the benefits of sorting with glyph-based visualization.
@cite_30 @cite_4 @cite_33 @cite_39 @cite_17
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Sorting is a kernel algorithm for a wide range of applications. We present a new algorithm, GPU-Warpsort, to perform comparison-based parallel sort on Graphics Processing Units (GPUs). It mainly consists of a bitonic sort followed by a merge sort. Our algorithm achieves high performance by efficiently mapping the sorting tasks to GPU architectures. Firstly, we take advantage of the synchronous execution of threads in a warp to eliminate the barriers in bitonic sorting network. We also provide sufficient homogeneous parallel operations for all the threads within a warp to avoid branch divergence. Furthermore, we implement the merge sort efficiently by assigning each warp independent pairs of sequences to be merged and by exploiting totally coalesced global memory accesses to eliminate the bandwidth bottleneck. Our experimental results indicate that GPU-Warpsort works well on different kinds of input distributions, and it achieves up to 30 higher performance than previous optimized comparison-based GPU sorting algorithm on input sequences with millions of elements. Sorting is an important component of many applications, and parallel sorting algorithms have been studied extensively in the last three decades. One of the earliest parallel sorting algorithms is bitonic sort, which is represented by a sorting network consisting of multiple butterfly stages. The paper studies bitonic sort on modern parallel machines which are relatively coarse grained and consist of only a modest number of nodes, thus requiring the mapping of many data elements to each processor. Under such a setting optimizing the bitonic sort algorithm becomes a question of mapping the data elements to processing nodes (data layout) such that communication is minimized. The authors developed a bitonic sort algorithm which minimizes the number of communication steps and optimizes the local computation. The resulting algorithm is faster than previous implementations, as experimental results collected on a 64 node Meiko CS-2 show. This paper presents an algorithm for fast sorting of large lists using modern GPUs. The method achieves high speed by efficiently utilizing the parallelism of the GPU throughout the whole algorithm. Initially, GPU-based bucketsort or quicksort splits the list into enough sublists then to be sorted in parallel using merge-sort. The algorithm is of complexity nlogn, and for lists of 8 M elements and using a single Geforce 8800 GTS-512, it is 2.5 times as fast as the bitonic sort algorithms, with standard complexity of n(logn)^2, which for a long time was considered to be the fastest for GPU sorting. It is 6 times faster than single CPU quicksort, and 10 faster than the recent GPU-based radix sort. Finally, the algorithm is further parallelized to utilize two graphics cards, resulting in yet another 1.8 times speedup. Sorting is a very important task in computer science and becomes a critical operation for programs making heavy use of sorting algorithms. General-purpose computing has been successfully used on Graphics Processing Units GPUs to parallelize some sorting algorithms. Two GPU-based implementations of the quicksort were presented in literature: the GPU-quicksort, a compute-unified device architecture CUDA iterative implementation, and the CUDA dynamic parallel CDP quicksort, a recursive implementation provided by NVIDIA Corporation. We propose CUDA-quicksort an iterative GPU-based implementation of the sorting algorithm. CUDA-quicksort has been designed starting from GPU-quicksort. Unlike GPU-quicksort, it uses atomic primitives to perform inter-block communications while ensuring an optimized access to the GPU memory. Experiments performed on six sorting benchmark distributions show that CUDA-quicksort is up to four times faster than GPU-quicksort and up to three times faster than CDP-quicksort. An in-depth analysis of the performance between CUDA-quicksort and GPU-quicksort shows that the main improvement is related to the optimized GPU memory access rather than to the use of atomic primitives. Moreover, in order to assess the advantages of using the CUDA dynamic parallelism, we implemented a recursive version of the CUDA-quicksort. Experimental results show that CUDA-quicksort is faster than the CDP-quicksort provided by NVIDIA, with better performance achieved using the iterative implementation. Copyright © 2015 John Wiley & Sons, Ltd.