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2009 International Conference on Apperceiving Computing and Intelligence Analysis, ICACIA 2009

作者： Wang, Zheng-Fei Tang, Ai-Guo Wang, Wei Department of Computer Hunan Business College Changsha 410205 China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China School of Computer Science Fudan University Shanghai 200433 China

ISBN: (纸本)9781424452040

There are a lot of important and sensitive data in databases, which need to be protected from attacks. To secure the data, Cryptography support is an effective mechanism. However, a tradeoff must be made between the performance and the security because encryption and decryption greatly degrade the query performance. In this paper, a framework is proposed that can quickly implement query over encrypted data based on B+ tree. When data are stored in the form of cipher, it not only stores the encrypted data themselves, but also creates a B+ tree index for the data before encrypting them. When querying the encrypted data, firstly, it locates the encrypted records related to the querying predicate based on the B+ tree index;secondly, it decrypts the encrypted records to accomplish the results. Meanwhile, it must encrypt the B+ tree itself to protect it from leaking confidential information. According to the structure of the B+ tree, it encrypts each node of the B+ tree separately. The results of experiments show that the query performance over the encrypted data decreases about 20 percent compared with the plaintext query performance, and validate the functionality and usability of our approach. ©2009 IEEE.

关键词： Cryptography

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Graph pooling via coarsened graph infomax

arXiv

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arXiv 2021年

作者： Pang, Yunsheng Zhao, Yunxiang Li, Dongsheng The University of Melbourne National Lab for Parallel and Distributed Processing College of Computer National University of Defense Technology

Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning. Existing graph pooling methods either suffer from high computational complexity or cannot capture the global dependencies between graphs before and after pooling. To address the problems of existing graph pooling methods, we propose Coarsened Graph Infomax Pooling (CGIPool) that maximizes the mutual information between the input and the coarsened graph of each pooling layer to preserve graph-level dependencies. To achieve mutual information neural maximization, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples. Extensive experimental results on seven datasets illustrate the superiority of CGIPool comparing to the state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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SemanticCast: Content-Based Data Distribution over Self-Organizing Semantic Overlay Networks

SemanticCast: Content-Based Data Distribution over Self-Orga...

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IEEE International Conference on parallel and distributed Computing, Applications and Technologies (PDCAT)

作者： Zhong Zheng Yijie Wang National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

Many applications demand distributing data with different contents efficiently in the network environment with unreliable links and a high node churn. Existing approaches mostly focus on optimizing either efficiency or robustness of data distribution, and fail to ensure both of them simultaneously. In this paper, we propose Semantic Cast - a content-based data distribution approach over self-organizing semantic overlay networks. Semantic Cast maintains a self-organizing semantic overlay based on view exchange (called Crowd). In Crowd, each node seeks neighbors with more similar interests by periodically exchanging its neighbor list (called view) with a chosen neighbor. Through these nodes' self-organizing behavior, various interest communities emerge in the overlay. For data distribution over Crowd, Semantic Cast adopts random walk to route data between interest communities, and adopts flooding to disseminate data inside the interested communities. The experimental results show that compared to existing approaches, Semantic Cast can support efficient content-based data distribution in the unreliable and highly dynamic network environment.

关键词： Semantics Peer to peer computing Communities Routing Robustness Convergence Subscriptions

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parallelization of the Training for Face Detection with Transactional Memory

Parallelization of the Training for Face Detection with Tran...

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The International Conference on Automation and Robotics(ICAR 2011)

作者： Kun Zeng Key Laboratory and technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology

The development of multi-core processor makes the parallelization of traditional sequential algorithms increasingly important. Meanwhile, transactional memory serves a good parallel programming model. This paper takes the advantage of software transactional memory to parallelize the Multi-Exit Asymmetric Adaboost algorithm for face detection. The parallel version is evaluated on three different implementations of software transactional memory. The experiment results show that the transactional memory based parallelization outperforms the traditional lock based approach. A speedup of nearly seven is achieved on a eight-core machine on an eight-core system.

关键词： Transactional Memory Face Detection Machine Learning Optimistic Algorithm Pessimistic Algorithm

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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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International Joint Conference on Neural Networks

作者： Mingyang Geng Yiying Li Bo Ding Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learning-based approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot vision-based trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the single-robot system.

关键词： Task analysis Robot kinematics Machine learning Multi-robot systems Quality of service Cloud computing

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Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs

Accelerate Graph Neural Network Training by Reusing Batch Da...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Zhejiang Ran Zhiquan Lai Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.

关键词： Training Productivity Deep learning Design methodology Conferences Graphics processing units Programming

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SCGraph: Accelerating Sample-based GNN Training by Staged Caching of Features on GPUs

SCGraph: Accelerating Sample-based GNN Training by Staged Ca...

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

作者： Yuqi He Zhiquan Lai Zhejiang Ran Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Graph neural networks (GNNs) have been becoming important tools for processing structured graph data and successfully applied to multiple graph-based application scenarios. The existing GNN systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead of transferring vertex features between CPUs and GPUs is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. SCGraph classifies the graph vertices sorted by out-degrees. For high out-degree vertices, SCGraph sets grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, SCGraph expands training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on various benchmarks. Experimental results show that SCGraph improves the cache hit rate over GPUs up to 23.6%, and achieves up to 1.71x performance speedup over the state-of-the-art baselines while the convergence almost constant.

关键词： Training Memory management Loading Graphics processing units Benchmark testing Graph neural networks Data communication

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Random Walk Based Inverse Influence Research in Online Social Networks

Random Walk Based Inverse Influence Research in Online Socia...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Zhaoyan Jin Quanyuan Wu Dianxi Shi Huining Yan National Key Laboratory for Parallel and Distributed Processing NUDT Changsha Hunan P.R.China

ISBN: (纸本)9781479909735

In online social networks, social influence of a user reflects his or her reputation or importance in the whole network or to a personalized user. Social influence analysis can be used in many real applications, such as link prediction, friend recommendation and personalized searching. Personalized Page Rank, which ranks nodes according to the probabilities that a random walk starting from a personalized node stops at all nodes, is one of the most popular metrics for influence analysis. In this paper, we study the problem of inverse influence in online social networks. Different from Personalized Page Rank, the inverse influence for a personalized node ranks nodes according to the probabilities that all nodes stop at the personalized node in limited steps. We propose two computation models for inverse influence, i.e., the random walk based and the path based. Both of the models have high computation complexity, and cannot be used in large graphs, so we propose a Monte Carlo based approximation algorithm. Experiments from synthetic and real world datasets show that, our algorithm has equivalent or even better accuracy than related researches in link prediction, and thus can be used in friend recommendation in online social networks.

关键词： Prediction algorithms Social network services Equations Mathematical model Accuracy Approximation algorithms Measurement

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FINT: FIELD-AWARE INTERACTION NEURAL NETWORK FOR CTR PREDICTION

arXiv

引用

arXiv 2021年

作者： Zhao, Zhishan Yang, Sen Liu, Guohui Feng, Dawei Xu, Kele iQIYI Inc. China National Key Lab of Parallel and Distributed Processing Changsha China National University of Defense Technology Changsha China

As a critical component for online advertising and marketing, click-through rate (CTR) prediction has drawn lots of attention from both industry and academia. Recently, deep learning has become the mainstream methodological choice for CTR. Despite sustainable efforts have been made, existing approaches still pose several challenges. On the one hand, high-order interaction between the features is under-explored. On the other hand, high-order interactions may neglect the semantic information from the low-order fields. In this paper, we proposed a novel prediction method, named FINT, that employs the Field-aware INTeraction layer which explicitly captures high-order feature interactions while retaining the low-order field information. To empirically investigate the effectiveness and robustness of the FINT, we perform extensive experiments on the three realistic databases: KDD2012, Criteo and Avazu. The obtained results demonstrate that the FINT can significantly improve the performance compared to the existing methods, without increasing the amount of computation required. Moreover, the proposed method brought about 2.72% increase to the advertising revenue of iQIYI, a big online video app through A/B testing. To better promote the research in CTR field, we released our code as well as reference implementation at: https://***/zhishan01/FINT. © 2021, CC BY.

关键词： Deep learning

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Power Optimization for GPU Programs Based on Software Prefetching

Power Optimization for GPU Programs Based on Software Prefet...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yisong Lin Tao Tang Guibin Wang Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense and Technology Changsha China

GPUs render higher computing unit density than contemporary CPUs and thus exhibit much higher power consumption despite its higher power efficiency. The power consumption has become an important issue that impacts CPU's applications, thereby necessitating the low power optimization technology for GPUs. Software prefetching is an efficient way to alleviate the memory wall problem which overlaps the computing and memory access latencies. However, software prefetching will cause some power overhead because it increases the number and density of the instructions. Thus, we should consider the balance between the performance income and the power overhead when applying the optimization. To address this problem, in this paper we first analyze the multi-thread execution model of GPU and validate the potential space of software prefetching optimization. Then we give the software prefetching method for GPU programs to improve the performance. Aiming at two different objects: energy optimization under performance constraint and performance optimization under power constraint, we discuss the optimization methods based on software prefetching and dynamic voltage scaling technologies. The experimental results show that our method can efficiently optimize the energy consumption (performance) under the performance (power) constraint.

关键词： Prefetching Graphics processing unit Optimization Power demand Registers

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