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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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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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Power and performance analysis of the Graph 500 benchmark on the Single-chip Cloud Computer

Power and performance analysis of the Graph 500 benchmark on...

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International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Zhiquan Lai King Tin Lam Cho-Li Wang Jinshu Su College of Computer National University of Defense Technology Changsha China Department of Computer Science The University of Hong Kong Hong Kong China National Key Laboratory of Parallel and Distributed Processing (PDL) Changsha China

ISBN: (纸本)9781479947638

The concerns of data-intensiveness and energy awareness are actively reshaping the design of high-performance computing (HPC) systems nowadays. The Graph500 is a widely adopted benchmark for evaluating the performance of computing systems for data-intensive workloads. In this paper, we introduce a data-parallel implementation of Graph500 on the Intel Single-chip Cloud Computer (SCC). The SCC features a non-coherent many-core architecture and multi-domain on-chip DVFS support for dynamic power management. With our custom-made shared virtual memory programming library, memory sharing among threads is done efficiently via the shared physical memory (SPM) while the library has taken care of the coherence. We conduct an in-depth study on the power and performance characteristics of the Graph500 workloads running on this system with varying system scales and power states. Our experimental results are insightful for the design of energy-efficient many-core systems for data-intensive applications.

关键词： Kernel Benchmark testing Computer architecture Programming Power measurement Computational modeling Runtime

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Latency-Aware Dynamic Voltage and Frequency Scaling on Many-Core Architectures for Data-Intensive Applications

Latency-Aware Dynamic Voltage and Frequency Scaling on Many-...

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International Conference on Cloud Computing and Big Data (CloudCom-Asia)

作者： Zhiquan Lai King Tin Lam Cho-Li Wang Jinshu Su Youliang Yan Wangbin Zhu National University of Defense Technology China The University of Hong Kong Hong Kong National Key Laboratory of Parallel and Distributed Processing (PDL) China Huawei Technologies Co. Ltd. Shenzhen China

Low power is the first-class design requirement for HPC systems. Dynamic voltage and frequency scaling (DVFS) has become the commonly used and efficient technology to achieve a trade-off between power consumption and system performance. However, most the prior work using DVFS did not take into account the latency of voltage/frequency scaling, which is a critical factor in real hardware determining the power efficiency of the power management algorithm. This paper, firstly, investigate the latency features of DVFS on a real many-core hardware platform. Secondly, we propose a latency-aware DVFS algorithm for profile-based power management to avoid aggressive power state transitions. At last, we evaluate our algorithm on Intel SCC platform using a data-intensive benchmark, Graph 500 benchmark. The experimental results not only show impressive potential for energy saving in data-intensive applications (up to 31% energy saving and 60% EDP reduction), but also evaluate the efficiency of our latency-aware DVFS algorithm which achieves 12.0% extra energy saving and 5.0% extra EDP reduction, moreover, increases the execution performance by 22.4%.

关键词： Benchmark testing Runtime Hardware Heuristic algorithms Time-frequency analysis Voltage measurement

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A distributed approach to consistent order delivery of concurrent events in asynchronous DVEs

ICETC 2010 - 2010 2nd International Conference on Education ...

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ICETC 2010 - 2010 2nd International Conference on Education Technology and Computer 2010年 4卷 V495-V498页

作者： Zhou, Hangjun Zhang, Wei Peng, Yuxing Li, Sikun Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Department of Information Management Hunan College of Finance and Economics Changsha Hunan 410205 China

ISBN: (纸本)9781424463688

In large-scale asynchronous distributed virtual environments(DVEs), one of the difficult problems is to deliver the concurrent events in a consistent order at each node. Generally, the previous consistency control approaches can be classified into two categories: causal order and time stamped order. However, causal order approaches can merely preserve the cause-effect relation of events and time stamped order approaches seem intrinsically complex to be used in serverless large-scale asynchronous DVEs. In this paper, we proposed a novel distributed algorithm to identify the concurrent events and preserve the consistent order delivery of them at different nodes. Simulation studies are also carried out to compare the performance of this algorithm with that of the previous ones. The results show that the new algorithm can effectively deliver the concurrent events in consistent order at each node and is more efficient than the previous algorithms in large-scale asynchronous DVEs. © 2010 IEEE.

关键词： Virtual reality

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Enabling Tissue-Scale Cardiac Simulations Using Heterogeneous Computing on Tianhe-2

Enabling Tissue-Scale Cardiac Simulations Using Heterogeneou...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Johannes Langguth Qiang Lan Namit Gaur Xing Cai Mei Wen Chun-Yuan Zhang Simula Research Laboratory Lysaker Norway College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing Changsha China Department of Informatics University of Oslo Oslo Norway

ISBN: (纸本)9781509053827

We develop a simulator for 3D tissue of the human cardiac ventricle with a physiologically realistic cell model and deploy it on the supercomputer Tianhe-2. In order to attain the full performance of the heterogeneous CPU-Xeon Phi design, we use carefully optimized codes for both devices and combine them to obtain suitable load balancing. Using a large number of nodes, we are able to perform tissue-scale simulations of the electrical activity and calcium handling in millions of cells, at a level of detail that tracks the states of trillions of ryanodine receptors. We can thus simulate arrythmogenic spiral waves and other complex arrhythmogenic patterns which arise from calcium handling deficiencies in human cardiac ventricle tissue. Due to extensive code tuning and parallelization via OpenMP, MPI, and SCIF/COI, large scale simulations of 10 heartbeats can be performed in a matter of hours. Test results indicate excellent scalability, thus paving the way for detailed whole-heart simulations in future generations of leadership class supercomputers.

关键词： Calcium Computational modeling Mathematical model Performance evaluation Hardware Instruction sets Supercomputers

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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Grid and Cooperative Computing - GCC 2004 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Hai Jin Yi Pan Nong Xiao Jianhua Sun

ISBN: (数字)9783540302087

ISBN: (纸本)9783540235644

Welcome to the proceedings of GCC2004 and the city of Wuhan. Grid computing has become a mainstream research area in computer science and the GCC conference has become one of the premier forums for presentation of new and exciting research in all aspectsofgridandcooperativecomputing. Theprogramcommitteeispleasedtopresent the proceedings of the 3rd International Conference on Grid and Cooperative Comp- ing (GCC2004), which comprises a collection of excellent technical papers, posters, workshops, and keynote speeches. The papers accepted cover a wide range of exciting topics, including resource grid and service grid, information grid and knowledge grid, grid monitoring,managementand organizationtools, grid portal, grid service, Web s- vices and their QoS, service orchestration, grid middleware and toolkits, software glue technologies, grid security, innovative grid applications, advanced resource reservation andscheduling,performanceevaluationandmodeling,computer-supportedcooperative work, P2P computing, automatic computing, and meta-information management. The conference continues to grow and this year a record total of 581 manuscripts (including workshop submissions) were submitted for consideration. Expecting this growth, the size of the program committee was increased from 50 members for GCC 2003 for 70 in GCC 2004. Relevant differences from previous editions of the conf- ence: it is worth mentioning a signi?cant increase in the number of papers submitted by authors from outside China; and the acceptance rate was much lower than for p- vious GCC conferences. From the 427 papers submitted to the main conference, the program committee selected only 96 regular papers for oral presentation and 62 short papers for poster presentation in the program.

关键词： Theory of Computation Computer Communication Networks Operating Systems Software Engineering Information Systems Applications (incl. Internet) Information Storage and Retrieval

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An Efficient Approximation for Weapon-Target Assignment

An Efficient Approximation for Weapon-Target Assignment

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ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM

作者： Lina Chen ChuanJun Ren Su Deng C4ISR Technology National Defense Science and Technology Key Laboratory National University of Defense Technology Changsha Hunan China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China

The weapon-target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research. The WTA problem can be formulated as a nonlinear integer programming problem and is known to be NP-complete. We present firstly a technique founded on the use weapon cell, which enables one-to-many mapping about weapon-target to become one-to-one mapping. Next, we introduce a framework of solving the WTA problem. Based on this framework, an approximation algorithm that is grounded on the rules about weapon cells and targets is proposed. Finally, the simulative results show that the proposed algorithm indeed is very efficient.

关键词： Weapons Heuristic algorithms Algorithm design and analysis Resource management Artificial neural networks Genetic algorithms Operations research

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Multi-task Pre-training Language Model for Semantic Network Completion

arXiv

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arXiv 2022年

作者： Li, Da Yang, Sen Xu, Kele Yi, Ming He, Yukai Wang, Huaimin Tencent Beijing100084 China Beijing Institute of Microbiology and Epidemiology Beijing100071 China State Key Laboratory of Pathogen and Biosecurity Beijing100071 China National University of Defense Technology Changsha410073 China National Key Lab of Parallel and Distributed Processing Changsha410073 China

Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language processing, it suffers from incompleteness. This paper focuses on knowledge graph completion by predicting linkage between entities, which is a fundamental yet critical task. Semantic matching is a potential solution for link prediction as it can deal with unseen entities, while the translational distance based methods struggle with the unseen entities. However, to achieve competitive performance as translational distance based methods, semantic matching based methods require large-scale datasets for the training purpose, which are typically unavailable in practical settings. Therefore, we employ the language model and introduce a novel knowledge graph architecture named LP-BERT, which contains two main stages: multi-task pre-training and knowledge graph fine-tuning. In the pre-training phase, three tasks are taken to drive the model to learn the relationship information from triples by predicting either entities or relations. While in the fine-tuning phase, inspired by contrastive learning, we design a triple-style negative sampling in a batch, which greatly increases the proportion of negative sampling while keeping the training time almost unchanged. Furthermore, we propose a new data augmentation method utilizing the inverse relationship of triples to improve the performance and robustness of the model. To demonstrate the effectiveness of our proposed framework, we conduct extensive experiments on three widely-used knowledge graph datasets, WN18RR, FB15k-237, and UMLS. The experimental results demonstrate the superiority of our methods, and our approach achieves state-of-the-art results on the WN18RR and FB15k-237 datasets. Significantly, the Hits@10 indicator is improved by 5% from the previous state-of-the-art result on the WN18RR dataset while reaching 100% on the UMLS dataset. Copy

关键词： Inverse problems

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