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An open-source, hardware accelerated industry prototype for multidimensional glyphs 14

An open-source, hardware accelerated industry prototype for ...

引用

14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image processing 2020, CGVCVIP 2020 and 5th IADIS international conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and 9th IADIS international conference theory and Practice in Modern Computing 2020, TPMC 2020, Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020

作者： Rees, Dylan Laramee, Robert S. Swansea University Swansea United Kingdom University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9789898704214

Data-glyphs are a common, useful metaphor for visualizing multidimensional data. However, to represent a larger number of glyphs requires an increasing amount of computational power to derive the data-dependent geometry of each glyph. We present a novel visualization implementation for computing and rendering customized data-glyphs for an industry application. Our software utilizes the parallel processing power of the Graphics processing Unit (GPU) to enable interactive exploration of thousands of multidimensional data-glyphs. this is achieved by passing normalized data values to the OpenGL geometry shader to construct the geometry of each glyph reflecting the data values. the technique is applicable to a variety of glyph designs and supports fast selection between different designs. We demonstrate this technique to render more than 100,000 glyphs at interactive frame rates. To address the challenge of reproducing the technique, we provide a simplified open-source code example implementation that can be used by anyone, including our industry partner. © Proceedings of the 14th IADIS international conference Computer Graphics, Visualization, Computer Vision and Image processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS international conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS international conference theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.

关键词： Graphics processing unit

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parallel Belief Propagation Optimized by Coloring on GPUs 20th

Parallel Belief Propagation Optimized by Coloring on GPUs

引用

20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Hou, Junteng Si, Chengxiang Wang, Shupeng Wu, Guangjun Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Belief propagation (BP) is a message passing algorithm that infers over probabilistic graphical models. Its main computational workload, messages update, is suitable for GPU's massively parallel architecture. However, the efficiency of fully parallel BP is low, and traditional algorithms implemented on GPUs occupy amount of computing and memory resources. In this paper, we propose several GPU-friendly BP algorithms optimized by coloring. Color Wave (CW) algorithm performs multi-step coloring on residuals of non-convergent vertices to quickly obtain multiple disjoint partitions and vertices with the largest residuals in each partition, and then updates batches of messages in a fixed order. these operations are all suitable for parallelization and require little additional memory. To save time in each iteration, the Color Extract (CE) algorithm only update messages on edges with the largest residuals among all adjacent edges. the Random Drop (RD) algorithm steadily increases the convergence degree by progressively reducing the messages update ratio of non-convergent edges. the experiments on different GPUs show that our algorithms perform well throughout the calculation process. Compared with state-of-the-art algorithms, CW algorithm converged most of the messages in previous iterations. the convergence degree of CE is higher than all other algorithms in most calculation processes. RD converges fast and always has a high degree of convergence.

关键词： Belief propagation GPUs parallel optimization Coloring operations Progressively reducing update ratio

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A Binary Code Sequence Based Tracking Algorithm in Wireless Sensor Networks 19th

A Binary Code Sequence Based Tracking Algorithm in Wireless ...

引用

19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Zhang, Yang Ren, Qianqian Pan, Yu Li, Jinbao Heilongjiang Univ Key Lab Database & Parallel Comp Heilongjiang Pro Harbin Peoples R China Heilongjiang Univ Key Lab Elect Engn Coll Heilongjiang Prov Harbin Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9783030389611;9783030389604

this paper proposes a binary code sequence based tracking algorithm in wireless sensor network. the proposed algorithm can release the influence of sensed data on localization results via building the map between target's occurrence region and a binary code sequence. To solve the ambiguity problem existing in occurrence region determination, the paper further gives a Voronoi diagram based location refinement algorithm. the simulation results show the tracking results under difference trajectories.

关键词： Target localization RSSI Voronoi

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Efficient parallel algorithms for betweenness- and closeness-centrality in dynamic graphs 34

Efficient parallel algorithms for betweenness- and closeness...

引用

34th ACM international conference on Supercomputing, ICS 2020

作者： Shukla, Kshitij Regunta, Sai Charan Tondomker, Sai Harsh Kothapalli, Kishore International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781450379830

Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. there have been several studies that show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi-core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this paper, we show novel parallel algorithms for updating the betweenness- and closeness-centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness-and closeness-centrality by Jamour et al. [16] and Sariyüce et al. [27], respectively. Besides, our algorithms incorporate mechanisms to exploit the structural properties of graphs for enhanced performance. We implement our algorithms on two parallel architectures: an Intel 24-core CPU and an Nvidia Tesla V100 GPU. To the best of our knowledge, we are the first to show GPU algorithms for the above two problems. We conduct detailed experiments to study the impact of various parameters associated with our algorithms and their implementation. Our results on a collection of real-world graphs indicate that our algorithms achieve a significant speedup over corresponding state-of-the-art algorithms. © 2020 ACM.

关键词： Graphics processing unit

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Term Rewriting on GPUs 9th

Term Rewriting on GPUs

引用

9th international conference on Fundamentals of Software Engineering, FSEN 2021

作者： van Eerd, Johri Groote, Jan Friso Hijma, Pieter Martens, Jan Wijs, Anton Verum Software Tools BV Eindhoven Netherlands Eindhoven University of Technology Eindhoven Netherlands VU Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783030892463

We present a way to implement term rewriting on a GPU. We do this by letting the GPU repeatedly perform a massively parallel evaluation of all subterms. We find that if the term rewrite systems exhibit sufficient internal parallelism, GPU rewriting substantially outperforms the CPU. Since we expect that our implementation can be further optimized, and because in any case GPUs will become much more powerful in the future, this suggests that GPUs are an interesting platform for term rewriting. As term rewriting can be viewed as a universal programming language, this also opens a route towards programming GPUs by term rewriting, especially for irregular computations. © 2021, IFIP international Federation for Information processing.

关键词： Graphics processing unit

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CC-MOEA: A parallel Multi-objective Evolutionary Algorithm for Recommendation Systems 20th

CC-MOEA: A Parallel Multi-objective Evolutionary Algorithm f...

引用

20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Wei, Guoshuai Wu, Quanwang Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Recommender systems have nowadays been widely used in a variety of applications such as Amazon and Ebay. Traditional recommendation techniques mainly focus on recommendation accuracy only. In reality, other metrics such as diversity and novelty also play a key role for modern recommendation systems. Although some works based on multi-objective evolutionary algorithm have been proposed for multi-objective recommendation, they are usually very time-consuming because of the large data size of the RSs and the long-term evolution iterations and hence it greatly limits their application in practice. To address these shortcomings, this paper first designs a multi-objective recommendation system, taking into account diversity and novelty as well as accuracy. then, a novel parallel multi-objective evolutionary algorithm called CC-MOEA is proposed to optimize these conflicting metrics. CC-MOEA is devised grounded on NSGA-II and a cooperative coevolutionary island model, and a parallel global non-dominated selection method is introduced to reduce the runtime of finding the global optimal individuals. Furthermore, a new initialization method and a crossover operator are specifically designed. the experimental results reveal that CC-MOEA outperforms some state-of-the-art algorithms in terms of hypervolume and runtime.

关键词： Accuracy Cooperative coevolutionary Multi-objective evolutionary algorithm parallel Recommendation system

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Studying the Performance of Vector-Based Quicksort Algorithm 13th

Studying the Performance of Vector-Based Quicksort Algorithm

引用

13th international conference on parallel processing and Applied Mathematics, PPAM 2019

作者： Marowka, Ami Parallel Research Lab Jerusalem Israel

ISBN: (纸本)9783030432218

the performance of parallel algorithms is often inconsistent with their preliminary theoretical analyses. Indeed, the difference is increasing between the ability to theoretically predict the performance of a parallel algorithm and the results measured in practice. this is mainly due to the accelerated development of advanced parallel architectures, whereas there is still no agreed model for parallel computation, which has implications for the design of parallel algorithms. In this study, we examined the practical performance of Cormen’s Quicksort parallel algorithm. We determined the performance of the algorithm with different parallel programming approaches and examine the capacity of theoretical performance analyses of the algorithm for predicting the actual performance. © 2020, Springer Nature Switzerland AG.

关键词： Python

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Research on Multi-stage GPU Collaborative Model 9th

Research on Multi-stage GPU Collaborative Model

引用

9th international conference on Computer Engineering and Networks, CENet2019

作者： Zhang, Longxiang Wang, Haifeng School of Information Science& Engineering Linyi Univesity Shuangling Road LinyiShandong276002 China

ISBN: (纸本)9789811537523

GPU heterogeneous cluster is extensively utilized in the field of data analysis and processing. Nevertheless, research and studies on collaborative activity model in computing elements of GPU heterogeneous clusters are still inadequate. To conduct research on GPU and multi-core CPU cooperative computing from a theoretical perspective, a multi-stage cooperative computing model (p-DCOT) is established. Bulk synchronous parallel (BSP) model is the core of p-DCOT. Cooperative computing is divided into three layers: data layer, computing layer and communication layer. Computing and communication are described and formalized by matrix. Lastly, representative computing examines the effectiveness of model and parameter analysis. the collaborative computing model finds out the collaborative computing in big data analysis and processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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Optimizing B⁺-Tree Searches on Coupled CPU-GPU architectures 20th

Optimizing B<SUP>+</SUP>-Tree Searches on Coupled CPU-GPU Ar...

引用

20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Huang, Han Luan, Hua Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the B+-tree is an important index in the fields of data warehousing and database management systems. With the development of new hardware technologies, the B+-tree needs to be revisited to fully take advantage of hardware resources. In this paper, we focus on optimization techniques to increase the searching performance of B+-trees on the coupled CPU-GPU architecture. First, we propose a hierarchical searching approach on the single coupled GPU to efficiently deal with leaf nodes of B+-trees. It adopts a flexible strategy to determine the number of work items in a work group to search one key in order to reduce irregular memory accesses and divergent branches in the work group. Second, we present a co-processing pipeline method on the coupled architecture. the CPU and the integrated GPU process the sorting and searching tasks simultaneously to hide sorting and partial searching latencies. A distribution model is designed to support the workload balance strategy based on real-time performance. Our performance study shows that the hierarchical searching scheme provides an improvement up to 36% on the GPU compared to the baseline algorithm with fixed number of work items and the co-processing pipeline method further increases the throughput by a factor of 1.8. To the best of our knowledge, this paper is the first study to consider both the CPU and the coupled GPU to optimize B+-trees searches.

关键词： B+-trees the coupled architecture Integrated GPU Co-processing

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A Distributed Framework for Online Stream Data Clustering 20th

A Distributed Framework for Online Stream Data Clustering

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Ding, Jiafeng Fang, Junhua Chao, Pingfu Xu, Jiajie Zhao, PengPeng Zhao, Lei Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9783030602451;9783030602444

the recent prevalence of positioning sensors and mobile devices generates a massive amount of spatial-temporal data from moving objects in real-time. As one of the fundamental processes in data analysis, the clustering on spatial-temporal data creates various applications, like event detection and travel pattern extraction. However, most of the existing works only focus on the offline scenario, which is not applicable to online time-sensitive applications due to their low efficiency and ignorance of temporal features. In this paper, we propose a distributed streaming framework for spatial-temporal data clustering, which accepts various clustering algorithms while ensuring low resource consumption and result correctness. the framework includes a dynamic partitioning strategy for continuous load-balancing and a cluster-merging algorithm based on convex hulls [10], which guarantees the result correctness. Extensive experiments on real dataset prove the effectiveness of our proposed framework and its advantage over existing solutions.

关键词： Real-time cluster analysis Distributed stream processing Spatial-temporal data mining Top-k query parallel computing

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