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International Conference on parallel processing (ICPP)

作者： Feng Wang Hao Jiang Ke Zuo Xing Su Jingling Xue Canqun Yang School of Computer Science National University of Defense Technology Changsha China School of Computer Science and Engineering University of New South Wales NSW Australia Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

This paper presents the design and implementation of a highly efficient Double-precision General Matrix Multiplication (DGEMM) based on Open BLAS for 64-bit ARMv8 eight-core processors. We adopt a theory-guided approach by first developing a performance model for this architecture and then using it to guide our exploration. The key enabler for a highly efficient DGEMM is a highly-optimized inner kernel GEBP developed in assembly language. We have obtained GEBP by (1) maximizing its compute-to-memory access ratios across all levels of the memory hierarchy in the ARMv8 architecture with its performance-critical block sizes being determined analytically, and (2) optimizing its computations through exploiting loop unrolling, instruction scheduling and software-implemented register rotation and taking advantage of A64 instructions to support efficient FMA operations, data transfers and prefetching. We have compared our DGEMM implemented in Open BLAS with another implemented in ATLAS (also in terms of a highly-optimized GEBP in assembly). Our implementation outperforms the one in ALTAS by improving the peak performance (efficiency) of DGEMM from 3.88 Gflops (80.9%) to 4.19 Gflops (87.2%) on one core and from 30.4 Gflops (79.2%) to 32.7 Gflops (85.3%) on eight cores. These results translate into substantial performance (efficiency) improvements by 7.79% on one core and 7.70% on eight cores. In addition, the efficiency of our implementation on one core is very close to the theoretical upper bound 91.5% obtained from micro-benchmarking. Our parallel implementation achieves good performance and scalability under varying thread counts across a range of matrix sizes evaluated.

关键词： Registers Kernel Computational modeling Program processors Assembly Memory management

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Using Redundant Threads for Fault Tolerance of OpenMP Programs

Using Redundant Threads for Fault Tolerance of OpenMP Progra...

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International Conference on Information science and Applications (ICISA)

作者： Hongyi Fu Yan Ding Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China Section #620 School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781424459421

As the wide application of multi-core processor architecture in the domain of high performance computing, fault tolerance for shared memory parallel programs becomes a hot spot of research. For years, checkpointing has been the dominant fault tolerance technology in this field, and recently, many research works have been engaged with it. However, to those programs which deal with large amount of data, checkpointing may induce massive I/O transfer, which will adversely affect scalability. To deal with such a problem, this paper proposes a fault tolerance approach, making use of redundancy, for shared memory parallel programs. Our scheme avoids saving and restoring computational state during the program's execution, hence does not involve I/O operations, so presents explicit advantage over checkpointing in scalability. In this paper, we introduce our approach and the related compiler tool in detail, and give the experimental evaluation result.

关键词： Fault tolerance Checkpointing Scalability Hardware Application software Multicore processing High performance computing Laboratories distributed processing

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Applying linux high-availability and load balancing servers for video-on-demand (Vod) systems

Applying linux high-availability and load balancing servers ...

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5th Pacific Rim Conference on Multimedia, PCM 2004

作者： Yang, Chao-Tung Wang, Ko-Tzu Li, Kuan-Ching Lee, Liang-Teh High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung407 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung433 Taiwan Dept. of Computer Science and Engineering Tatung University Taipei104 Taiwan

ISBN: (纸本)9783540239772

In this research paper, we integrate and implement High-Availability (HA) and Load-Balancing technologies to clusters of workstations, increasing both the availability and scalability of services and resources in these systems. The cluster of workstations is commonly built and used as web-based VOD servers. Thus, this paper presents the hardware and software configurations of cluster systems working as VOD servers. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Cluster computing

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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479906505

Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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Personalized Reputation Model in Cooperative distributed Systems

Personalized Reputation Model in Cooperative Distributed Sys...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Wei Liu Yang-Bin Tang Huai-Min Wang School of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing Changsha Hunan China Institute of Science National University of Defense Technology Changsha Hunan China

Reputation systems provide a promising way to build trust relationships between users in distributed cooperation systems, such as file sharing, streaming, distributed computing and social network, through which a user can distinguish good services or users from malicious ones and cooperate with them. However, most reputation models mainly focus on evaluating the qualities of different services in one dimension, but care less about the preferences of different users. This paper proposes a personalized reputation model which provides each user a personalized trust view on others according to his preference. In our approach, we aggregate the users' preferences with collaborative filtering method and qualify it with user similarity which is integrated into the computing of reputation values. The experimental results suggest that our model can resist possible kinds of malicious behaviors efficiently.

关键词： Computational modeling Measurement Indexes Aggregates Resists Peer to peer computing Electronic mail

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Density estimation with adaptive sparse grids for large data sets 14

Density estimation with adaptive sparse grids for large data...

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Peherstorfer, Benjamin Pflüger, Dirk Bungartz, Hans-Joachim Aerospace Computational Design Laboratory AeroAstro Massachusetts Institute of Technology United States Simulation of Large Systems Group Institute for Parallel and Distributed Systems University of Stuttgart Germany Scientific Computing Group Department of Computer Science Technische Universität München Germany

ISBN: (纸本)9781510811515

Nonparametric density estimation is a fundamental problem of statistics and data mining. Even though kernel density estimation is the most widely used method, its performance highly depends on the choice of the kernel bandwidth, and it can become computationally expensive for large data sets. WTe present an adaptive sparse-grid-based density estimation method which discretizes the estimated density function on basis functions centered at grid points rather than on kernels centered at the data points. Thus, the costs of evaluating the estimated density function are independent from the number of data points. We give details on how to estimate density functions on sparse grids and develop a cross validation technique for the parameter selection. We show numerical results to confirm that our sparse-grid-based method is well-suited for large data sets, and, finally, employ our method for the classification of astronomical objects to demonstrate that it is competitive to current kernel-based density estimation approaches with respect to classification accuracy and runtime. Copyright © SIAM.

关键词： Data mining

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An efficient local address generation for the block-cyclic distribution

An efficient local address generation for the block-cyclic d...

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IEEE International Conference on Algorithms and Architectures for parallel processing (ICAP)

作者： Oh-Young Kwon Tae-Geun Kim Tack-Don Han Sung-Bong Yang Shin-Dug Kim Distributed Computing Laboratory Systems Engineering Research Institute Taejon South Korea Parallel Processing System Laboratory Department of Computer Science Yonsei University Seoul South Korea

ISBN: (纸本)0780342291

In order to generate local addresses for an array section A(l:h:s) with block-cyclic distribution, an efficient compiling method is required. In this paper, two local address generation methods for the block-cyclic distribution are presented. One is a simple local address generation method that is modified from the virtual-block scheme. The other is a linear-time /spl Delta/M table construction method. The array elements of A(l:h:s) to be accessed at run-time build up a family of lines. By using the equation of the lines, a /spl Delta/M table can be generated in O(k) time. Experimental results show that a simple local address generation method has poor performance but a linear-time /spl Delta/M table generation method is faster than other algorithms in /spl Delta/M table generation time and access time for 10,000 array elements.

关键词： Runtime Equations distributed computing Systems engineering and theory parallel processing Laboratories Computer science parallel languages Upper bound Automata

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Mining Software Profile across Multiple Repositories for Hierarchical Categorization

Mining Software Profile across Multiple Repositories for Hie...

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International Conference on Software Maintenance (ICSM)

作者： Tao Wang Huaimin Wang Gang Yin Charles X. Ling Xiang Li Peng Zou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Computer Science The University of Western Ontario London Ontario Canada Academy of Equipment Beijing China

ISBN: (纸本)9781467352185

The large amounts of software repositories over the Internet are fundamentally changing the traditional paradigms of software maintenance. Efficient categorization of the massive projects for retrieving the relevant software in these repositories is of vital importance for Internet-based maintenance tasks such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, which are only verified on relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software maintenance requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles across multiple repositories. We design a SVM-based categorization framework to classify the massive number of software hierarchically. To improve the categorization performance, we aggregate different types of profile attributes from multiple repositories and design a weighted combination strategy which assigns greater weights to more important attributes. Extensive experiments are carried out on more than 18,000 projects across three repositories. The results show that our approach achieves significant improvements by using weighted combination, and the overall precision, recall and F-Measure can reach 71.41%, 65.60% and 68.38% in appropriate settings. Compared to the previous work, our approach presents competitive results with 123 finer-grained and multi-layered categories. In contrast to those using source code or byte code, our approach is more effective for large-scale and language-independent software categorization.

关键词： Databases Collaboration Servers Software maintenance Internet Data mining

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Vision-Language Navigation: A Survey and Taxonomy

arXiv

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arXiv 2021年

作者： Wu, Wansen Chang, Tao Li, Xinmeng Yin, Quanjun Zhu, Jiancheng The College of Systems Engineering National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

Vision-Language Navigation (VLN) tasks require an agent to follow human language instructions to navigate in previously unseen environments. This challenging field involving problems in natural language processing, computer vision, robotics, etc., has spawn many excellent works focusing on various VLN tasks. This paper provides a comprehensive survey and an insightful taxonomy of these tasks based on the different characteristics of language instructions in these tasks. Depending on whether the navigation instructions are given for once or multiple times, this paper divides the tasks into two categories, i.e., single-turn and multi-turn tasks. For single-turn tasks, we further subdivide them into goal-oriented and route-oriented based on whether the instructions designate a single goal location or specify a sequence of multiple locations. For multi-turn tasks, we subdivide them into passive and interactive tasks based on whether the agent is allowed to question the instruction or not. These tasks require different capabilities of the agent and entail various model designs. We identify progress made on the tasks and look into the limitations of existing VLN models and task settings. Finally, we discuss several open issues of VLN and point out some opportunities in the future, i.e., incorporating knowledge with VLN models and implementing them in the real physical world. © 2021, CC BY.

关键词： Taxonomies

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PCGraph: Accelerating GNN Inference on Large Graphs via Partition Caching

PCGraph: Accelerating GNN Inference on Large Graphs via Part...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Lizhi Zhang Zhiquan Lai Yu Tang Dongsheng Li Feng Liu Xiaochun Luo National Laboratory for Parallel and Distributed Processing (PDL) Computer College National University of Defence Technology Changsha China PLA News Media Center Beijing China

Graph neural networks (GNNs) have been emerging as powerful learning tools for unstructured data and successfully applied to many graph-based application domains. Sampling-based GNN inference is commonly adopted in existing graph learning frameworks to handle large-scale graphs. However, this approach is restricted by the problems of redundant vertex embedding computation in GPU and inefficient loading of vertex features from CPU to GPU. In this paper, we propose PCGraph, a system that supports adaptive GNN inference and feature partition caching. PCGraph significantly reduces the vertex embedding computation time by adaptive GNN inference technology, which selects the optimal inference algorithm and minimizes vertex embedding computation. PCGraph also reduces the redundant data transfer between the CPU and GPU by partition the target vertices and caching their corresponding partitions in turn. We evaluate PCGraph against two state-of-the-art industrial GNN frameworks, i.e., PyG and DGL, on a diverse array of benchmarks. Experimental results show that PCGraph reduces up to 99% vertex embedding computation and 98.5% data loading time, and achieves up to 360× performance speedup over the state-of-the-art baselines.

关键词： Adaptive systems Loading Graphics processing units Benchmark testing Data transfer Inference algorithms Graph neural networks

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