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2011 2nd International Conference on Artificial Intelligence, Management science and Electronic Commerce, AIMSEC 2011

作者： Zhang, Baida Xu, Shuai Zhang, Feng Bi, Yuan Huang, Linqi National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China Department of Information Engineering Academy of Armored Force Engineering Beijing 100000 China Electronic Information Industry Co. Ltd Beijing 100085 China Naval Academy of Armament Beijing 100036 China School of Resources and Safety Engineering Central South University Changsha 410083 China

ISBN: (纸本)9781457705366

Lots of toolboxes of accelerating MatLab using GPU are available now[1], but, users are confused by which toolbox is best suitable for a particular task. Three toolboxes-Jacket, GPUmat, and parallel Computing Toolbox of MatLab are selected. For each toolbox, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which toolbox is appropriate for a given task. Strategies of whether a function should execute on GPU are given after a formula analysis. The analysis is also a framework for program automatically decides which function is cost-efficient to execute on GPU. A series of benchmark of different types of computing, including data transfer between GPU and CPU, data matrix Generation, matrix operation and GPU functions were tested in all three toolboxes. And the results show that Jacket is the best one. Some advices to improve the performance of toolboxes are given in the end. © 2011 IEEE.

关键词： Graphics processing unit

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FedNAT: Byzantine-robust Federated Learning through Activation-based Attention Transfer

FedNAT: Byzantine-robust Federated Learning through Activati...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Mengxin Wang Liming Fang Kuiqi Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China

Federated learning (FL) is a decentralized machine learning framework that prioritizes privacy by allowing clients to train statistical models without sharing their private data, thus eliminating the impact of data fortresses. However, the presence of Byzantine attacks, such as data poisoning and backdoor attack, threatens the robustness of FL schemes. Currently, existing mainstream defense methods are susceptible to multiple adaptive attacks, some of which even violate the privacy principle of FL. Furthermore, these defense schemes become less robust when subjected to targeted poisoning attacks with highly non-IID data distributions. In this work, we propose FedNAT, a novel Byzantine-robust FL framework for whittling away these limitations mentioned above. Specifically, FedNAT first performs a privacy-respecting attention refinement on the activation layer outputs of the local uploads. Then, the server scores the local attentions by calculating their Wasserstein distances and clusters them through the k-median algorithm for global attention aggregation, thus rejecting poisoned local attentions for untargeted attacks. After this process, the global attention is transferred to local attention through the FedNAT loss function, which erases backdoors through the distillation concept. We conduct a comprehensive experimental evaluation to demonstrate that FedNAT significantly outperforms existing robust FL schemes in defending against Byzantine poisoning attacks under both IID and highly non-IID data proportions.

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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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Benchmarking the task graph scheduling algorithms

Benchmarking the task graph scheduling algorithms

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International Symposium on parallel processing

作者： Y.-K. Kwok I. Ahmad Parallel Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

The problem of scheduling a weighted directed acyclic graph (DAG) to a set of homogeneous processors to minimize the completion time has been extensively studied. The NP-completeness of the problem has instigated researchers to propose a myriad of heuristic algorithms. While these algorithms are individually reported to be efficient, it is not clear how effective they are and how well they compare against each other. A comprehensive performance evaluation and comparison of these algorithms entails addressing a number of difficult issues. One of the issues is that a large number of scheduling algorithms are based upon radically different assumptions, making their comparison on a unified basis a rather intricate task. Another issue is that there is no standard set of benchmarks that can be used to evaluate and compare these algorithms. Furthermore, most algorithms are evaluated using small problem sizes, and it is not clear how their performance scales with the problem size. The authors first provide a taxonomy for classifying various algorithms into different categories according to their assumptions and functionalities. They then propose a set of benchmarks which are of diverse structures without being biased towards a particular scheduling technique and still allow variations in important parameters. They have evaluated 15 scheduling algorithms, and compared them using the proposed benchmarks. Based upon the design philosophies and principles behind these algorithms, they interpret the results and discuss why some algorithms perform better than the others.

关键词： Scheduling algorithm Processor scheduling parallel processing Concurrent computing Heuristic algorithms Laboratories computer science Taxonomy Algorithm design and analysis Scalability

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An Effective Design to Improve the Efficiency of DPUs on FPGA

An Effective Design to Improve the Efficiency of DPUs on FPG...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Yutian Lei Qingyong Deng Saiqin Long Shaohui Liu Sangyoon Oh The Key Laboratory of Hunan Province for Internet of Things and Information Security The School of Computer Science Science and Technology on Parallel and Distributed Processing Laboratory Xiangtan University Xiangtan China The Artificial Intelligence Center PengCheng Laboratory shenzhen China Ajou University Suwon-si Republic of Korea

The following topics are dealt with: cloud computing; learning (artificial intelligence); resource allocation; security of data; mobile computing; neural nets; optimisation; parallel processing; scheduling; and storag... 详细信息

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

关键词： Deep learning Schedules Multicore processing Logic gates Throughput Acceleration Field programmable gate arrays

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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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Optimal and near-optimal allocation of precedence-constrained tasks to parallel processors: defying the high complexity using effective search techniques

Optimal and near-optimal allocation of precedence-constraine...

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International Conference on parallel processing (ICPP)

作者： I. Ahmad Yu-Kwong Kwok Department of Computer Science Hong Kong University of Science and Technology Hong Kong China Parallel Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Obtaining an optimal schedule for a set of precedence-constrained tasks with arbitrary costs is a well-known NP-complete problem. However, optimal solutions are desired in many situations. In this paper we propose search-based algorithms for determining optimal schedules for moderately large problem sizes. The first algorithm which is based on the A* search technique uses a computationally efficient cost function for guiding the search with reduced complexity. We propose a number of state-pruning techniques to reduce the size of the search space. For further lowering the complexity, we parallelize the search. The parallel version is based on reduced interprocessor communication and is guided by static and dynamic load-balancing schemes to evenly distribute the search states to the processors. We also propose an approximate algorithm that guarantees a bounded deviation from the optimal solution but takes considerably shorter time. Based on an extensive experimental evaluation of the algorithms, we conclude that the parallel algorithm with pruning techniques is an efficient scheme for generating optimal solutions for medium to moderately large problems while the approximate algorithm is a useful alternative if slightly degraded solutions are acceptable.

关键词： Optimal scheduling Processor scheduling Computational efficiency Costs Concurrent computing Network topology Scheduling algorithm computer science Laboratories Multiprocessor interconnection networks

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distributed Relation Discovery in Internet of Things

Distributed Relation Discovery in Internet of Things

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International Conference on Cloud Computing and Big Data (CCBD)

作者： Weiping Zhu Hongliang Lu Xiaohui Cui International School of Software Wuhan University China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781479966226

Internet of things is an emerging technology that aims to connect various smart objects in our daily life. It facilitates the information exchange and control among objects. In the Internet of things, it is important to discover various relations among objects for analyzing and mining useful knowledge. Existing works on relation discovery mainly focus on centralized processing. It is not suitable for Internet of things due to unavailable of server, one-point failure, computation bottleneck, and security and business concerns. In this paper, we propose a distributed approach to discover the relations among objects in Internet of things. We first build the distritbuted system model which may include multiple relation discovery tasks. Based on that, we design an approach utilizing distributed spanning tree to extract the relations. Rather than sending all the information to the server, the objects only need to send the information to a local leader object in our algorithm. We also discuss more about how to improve the performance of the proposed approach and how to relax the system constraints. Extensive simulation have been done and the results show that the proposed approach outperforms existing approaches in terms of the data amount of transmission.

关键词： Internet of things Synchronization Buildings Servers Algorithm design and analysis Delays

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An Energy-Efficient Scheduling Algorithm for Sporadic Real-Time Tasks in Multiprocessor Systems

An Energy-Efficient Scheduling Algorithm for Sporadic Real-T...

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

作者： Dong-Song Zhang Fang-Yuan Chen Hong-Hua Li Shi-Yao Jin De-Ke Guo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China School of Computing Science Simon Fraser University Burnaby BC Canada National Laboratory for Information System Engineering School of Information Systems and Management National University of Defense Technology Changsha China

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads.

关键词： Real time systems Scheduling algorithm Time frequency analysis Scheduling Heuristic algorithms Power demand

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Locality Protected Dynamic Cache Allocation Scheme on GPUs

Locality Protected Dynamic Cache Allocation Scheme on GPUs

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

作者： Yang Zhang Zuocheng Xing Li Zhou Chunsheng Zhu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Electronic Science and Engineering National University of Defense Technology Changsha China Department of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781509032068

As we are approaching the exascale era in supercomputing, designing a balanced computer system with powerful computing ability and low energy consumption becomes increasingly important. GPU is a widely used accelerator in most recently applied supercomputers. It adopts massive multithreads to hide long latency and has high energy efficiency. In contrast to its strong computing power, GPUs have few on-chip resources with several MB of fast on-chip memory storage per SM (Streaming Multiprocessors). GPU caches exhibit poor efficiency due to the mismatch of the throughput-oriented execution model and its cache hierarchy design. Since the severe deficiency in on-chip memory, the benefit of high computing capacity of GPUs is pulled down by the poor cache performance dramatically, which limits system performance and energy-efficiency. In this paper, we put forward a locality protected scheme to make full use of the data locality based on the fixed capacity. We present a Locality Protected method based on instruction PC (LPP) to promote GPU performance. Firstly, we use a PC-based collector to collect the reuse information of each cache line. After getting the dynamic reuse information of the cache line, we take an intelligent cache allocation unit (ICAU) which coordinates the reuse information with LRU (Least Recently Used) replacement policy to find out the cache line with the least locality for eviction. The results show that LPP provides an up to 17.8% speedup and an average of 5.5% improvement over the baseline method.

关键词： Graphics processing units System-on-chip computer architecture Instruction sets Resource management Optimization Electronic mail

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