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arXiv 2023年

作者： Zhu, Xi Wang, Likang Zhou, Caifa Cao, Xiya Gong, Yue Chen, Lei Riemann Laboratory Huawei Technologies 2012 Laboratories China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Parallel Distributed Computing Laboratory Huawei Technologies 2012 Laboratories China

The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment. Recent advancements in deep learning have led to the rapid development of approaches that integrate multi-sensory measurements to enhance perception capabilities. This paper surveys the latest deep learning integration techniques applied to the perception module in autonomous driving systems, categorizing integration approaches based on "what, how, and when to integrate." A new taxonomy of integration is proposed, based on three dimensions: multi-view, multi-modality, and multi-frame. The integration operations and their pros and cons are summarized, providing new insights into the properties of an "ideal" data integration approach that can alleviate the limitations of existing methods. After reviewing hundreds of relevant papers, this survey concludes with a discussion of the key features of an optimal data integration approach. © 2023, CC BY-NC-SA.

关键词： Autonomous vehicles

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PTQ-SO: A Scale Optimization-based Approach for Post-training Quantization of Edge computing

PTQ-SO: A Scale Optimization-based Approach for Post-trainin...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kangkang Liu Ningjiang Chen College of Computer and Electronic Information Guangxi University Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the increasing performance of deep convolutional neural networks, they have been widely used in many computer vision tasks. However, a huge convolutional neural network model requires a lot of memory and computing resources, which makes it difficult to meet the requirements of low latency and reliability of edge computing when the model is deployed locally on resource-limited devices in edge environments. Quantization is a kind of model compression technology, which can effectively reduce model size, calculation cost and inference delay, but the quantization noise will cause the accuracy of the quantization model to decrease. Aiming at the problem of precision loss caused by model quantization, this paper proposes a post-training quantization method based on scale optimization. By reducing the influence of redundant parameters in the model on the quantization parameters in the process of model quantization, the scale factor optimization is realized to reduce the quantization error and thus improve the accuracy of the quantized model, reduce the inference delay and improve the reliability of edge applications. The experimental results show that under different quantization strategies and different quantization bit widths, the proposed method can improve the accuracy of the quantized model, and the absolute accuracy of the optimal quantization model is improved by 1.36%. The improvement effect is obvious, which is conducive to the application of deep neural network in edge environment.

关键词： Quantization (signal) Accuracy Computational modeling Computer network reliability Delays Reliability Convolutional neural networks

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Secure Live Migration of SGX Enclaves on Untrusted Cloud

Secure Live Migration of SGX Enclaves on Untrusted Cloud

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International Conference on Dependable Systems and Networks (DSN)

作者： Jinyu Gu Zhichao Hua Yubin Xia Haibo Chen Binyu Zang Haibing Guan Jinming Li Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Institute of Parallel and Distributed Systems (IPADS) Shanghai Jiao Tong University Huawei Technologies Inc.

ISBN: (纸本)9781538605431

The recent commercial availability of Intel SGX (Software Guard eXtensions) provides a hardware-enabled building block for secure execution of software modules in an untrusted cloud. As an untrusted hypervisor/OS has no access to an enclave's running states, a VM (virtual machine) with enclaves running inside loses the capability of live migration, a key feature of VMs in the cloud. This paper presents the first study on the support for live migration of SGX-capable VMs. We identify the security properties that a secure enclave migration process should meet and propose a software-based solution. We leverage several techniques such as two-phase checkpointing and self-destroy to implement our design on a real SGX machine. Security analysis confirms the security of our proposed design and performance evaluation shows that it incurs negligible performance overhead. Besides, we give suggestions on the future hardware design for supporting transparent enclave migration.

关键词： Program processors Hardware Security Virtual machine monitors Cloud computing Context

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A distributed Relation Detection Approach in the Internet of Things

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Mobile Information Systems 2017年第1期2017卷

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

In the Internet of Things, it is important to detect the various relations among objects for mining useful knowledge. Existing works on relation detection are based on centralized processing, which is not suitable for the Internet of Things owing to the unavailability of a server, one-point failure, computation bottleneck, and moving of objects. In this paper, we propose a distributed approach to detect relations among objects. We first build a system model for this problem that supports generic forms of relations and both physical time and logical time. Based on this, we design the distributed Relation Detection Approach (DRDA), which utilizes a distributed spanning tree to detect relations using in-network processing. DRDA can coordinate the distributed tree-building process of objects and automatically change the depth of the routing tree to a proper value. Optimization among multiple relation detection tasks is also considered. Extensive simulations were performed and the results show that the proposed approach outperforms existing approaches in terms of the energy consumption. © 2017 Weiping Zhu et al.

关键词： Internet of things

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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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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ACCURATE AND EFFICIENT FINE-TUNING OF QUANTIZED LARGE LANGUAGE MODELS THROUGH OPTIMAL BALANCE

arXiv

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arXiv 2024年

作者： Shen, Ao Wang, Qiang Lai, Zhiquan Li, Xionglve Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Hunan Changsha410073 China College of computer National University of Defense Technology Hunan Changsha410073 China

Large Language Models (LLMs) have demonstrated impressive performance across various domains. However, the enormous number of model parameters makes fine-tuning challenging, significantly limiting their application and deployment. Existing solutions combine parameter quantization with Low-Rank Adaptation (LoRA), greatly reducing memory usage but resulting in noticeable performance degradation. In this paper, we identify an imbalance in fine-tuning quantized pre-trained models: overly complex adapter inputs and outputs versus low effective trainability of the adaptation. We propose Quantized LLMs with Balanced-rank Adaptation (Q-BaRA), which simplifies the adapter inputs and outputs while increasing the adapter’s rank to achieve a more suitable balance for finetuning quantized LLMs. Additionally, for scenarios where fine-tuned LLMs need to be deployed as low-precision inference models, we introduce Quantization-Aware Fine-tuning with Higher Rank Adaptation (QA-HiRA), which simplifies the adapter inputs and outputs to align with the pre-trained model’s block-wise quantization while employing a single matrix to achieve a higher rank. Both Q-BaRA and QA-HiRA are easily implemented and offer the following optimizations: (i) Q-BaRA consistently achieves the highest accuracy compared to baselines and other variants, requiring the same number of trainable parameters and computational effort;(ii) QA-HiRA naturally merges adapter parameters into the block-wise quantized model after fine-tuning, achieving the highest accuracy compared to other methods. We apply our Q-BaRA and QA-HiRA to the LLaMA and LLaMA2 model families and validate their effectiveness across different fine-tuning datasets and downstream scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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A high-performance computational resource broker for grid computing environments

A high-performance computational resource broker for grid co...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Chao-Tung Yang Po-Chi Shih Kuan-Ching Li High-Performance Computing Laboratory Department of Computer Science and Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0769522491

Internet computing and grid technologies promise to change the way we tackle complex problems. They will enable large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. As grid computing is becoming a reality, there is a need for managing and monitoring the available resources worldwide, as well as the need for conveying these resources to the everyday user. This paper describes a resource broker with its main function as to match the available resources to the user's needs. The use of the resource broker provides a uniform interface to access any of the available and appropriate resources using user's credentials. The resource broker runs on top of the Globus toolkit. Therefore, it provides security and current information about the available resources and serves as a link to the diverse systems available in the grid.

关键词： Grid computing Resource management Bandwidth distributed computing Network servers Monitoring Information security Middleware Access protocols Java

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parallel global routing algorithms for standard cells

Parallel global routing algorithms for standard cells

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International Symposium on parallel Processing

作者： Z. Xing J. Chandy P. Banerjee Computer and Systems Research Laboratory University of Illinois Urbana IL USA Cadence Design Systems Chelmsford MA USA Center for Parallel and Distributed Computing Northwestern University Evanston IL USA

In this paper, we propose three different parallel algorithms based on a state-of-the-art global router called TimberWolfSC. The parallel algorithms have been implemented by using the Message Passing Interface (MPI), and have been evaluated on a wide range of parallel platforms such as the Sun SparcCenter 1000 and the Intel Paragon. Our experimental results show good speedups and qualities from two of these parallel algorithms. We have been able to reduce runtimes of some circuits from half an hour to 5 minutes, obtained speedups of about 4.0 to 5.0 on 8 processors, with less than 2-3% degradation of quality of the solutions.

关键词： Routing parallel algorithms distributed computing Message passing Pins Wire Contracts Concurrent computing Laboratories Sun

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Research on the weighted reconstruction of metabolic networks

Research on the weighted reconstruction of metabolic network...

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第五届中国蛋白质组学大会暨首届粤港蛋白质组学学术交流会

作者： Zhou Tingting Wang Zhenghua Chan, K.C.C. Beijing Proteome Research Center BeijingP.R.China102206 National Laboratory for Parallel & Distributed ProcessingNational University of Deference and TechnologyChangshaP.R.China 410073 Department of computingthe HongKong Polytechnic UniversityHonghumHongkong Department of computing the HongKong Polytechnic UniversityHonghumHongkong National Laboratory for Parallel & Distributed Processing National University of Deference and TechnologyChangshaP.R.China 410073

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