检索结果-内蒙古大学图书馆

PANDA: Adaptive Prefetching and Decentralized Scheduling for Dataflow architectures

ACM Transactions on architecture and Code Optimization 1000年

作者： Shantian Qin Zhihua Fan Wenming li Zhen Wang Xuejun An Xiaochun Ye Dongrui Fan State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China

Dataflow architectures are considered promising architecture, offering a commendable balance of performance, efficiency, and flexibility. Abundant prior works have been proposed to improve the performance of dataflow architectures. Nevertheless, these solutions can be further improved due to the lack of efficient data prefetching and flexible task scheduling. In this paper, we propose a novel dataflow architecture with adaptive prefetching and decentralized scheduling (PANDA). Firstly, we present an application-adaptive data prefetching method and on-chip memory microarchitecture designed to overlap memory access latency. Secondly, we introduce a decentralized dataflow scheduling approach and processing element (PE) microarchitecture aimed at improving hardware utilization. Experimental results show that in a wide range of real-world applications, PANDA attains up to 2.53 × performance improvement and 1.79 × energy efficiency improvement over the state-of-the-art dataflow architectures.

关键词： Prefetching Decentralized Dynamic Scheduling Reconfigurable On-chip Memory architecture

来源：评论

学校读者我要写书评

暂无评论

Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

引用

ACM Transactions on Software Engineering and Methodology 1000年

作者： Quanjun Zhang Chunrong Fang Yi Zheng Yaxin Zhang Yuan Zhao Rubing Huang Jianyi Zhou Yun Yang Tao Zheng Zhenyu Chen Department of Computing Technologies Swinburne University of Technology Australia and State Key Laboratory for Novel Software Technology Nanjing University China State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Macau University of Science and Technology China Huawei Cloud Computing Technologies Co. Ltd. China Department of Computing Technologies Swinburne University of Technology Australia State Key Laboratory for Novel Software Technology Nanjing University China and Shenzhen Research Institute of Nanjing University China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training *** this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66%

关键词： Unit Testing Assertion Generation Pre-trained Language Models AI4SE

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

来源：评论

学校读者我要写书评

暂无评论

Advanced Parallel Processing Technologies 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Chenggang Wu Albert Cohen

ISBN: (数字)9783642452932

ISBN: (纸本)9783642452925

This book constitutes the refereed post-proceedings of the 10th International Symposium on Advanced Parallel Processing Technologies, APPT 2013, held in Stockholm, Sweden, in August 2013. The 30 revised full papers presented were carefully reviewed and selected from 62 submissions. The papers cover a wide range of topics capturing some of the state of the art and practice in parallel architecture, parallel software, concurrent and distributed systems, and cloud computing, with a highlight on computing systems for big data applications.

关键词： Software Engineering Algorithm Analysis and Problem Complexity computer Communication Networks Operating Systems Programming Languages, Compilers, Interpreters

来源：评论

学校读者我要写书评

暂无评论

An X Language-Driven Framework for Systematic Development of Digital Twin Healthcare Systems

引用

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Kunyu Xie Lin Zhang Yuan Yang Xiaohe Li Ridha Khedri Zhen Chen M. Jamal Deen Simulation Technology Innovation Center Hangzhou International Innovation Institute of Beihang University Hangzhou 311115 Zhejiang China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Intelligent Manufacturing Systems Technology Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenzhen Third People’s Hospital & Second Hospital Affiliated to Southern University of Science and Technology Shenzhen 518112 Guangdong China Dept. of Computing and Software McMaster University Hamilton Ontario Canada L8S 4K1 Department of Electrical and Computer Engineering McMaster University Hamilton ON L8S 4L8 Canada

The rapid advancements in big data and the Internet of Things (IoT) have significantly accelerated the digital transformation of medical institutions, leading to the widespread adoption of Digital Twin Healthcare (DTH). The Cloud DTH Platform (CDTH) serves as a cloud-based framework that integrates DTH models, healthcare resources, patient data, and medical services. By leveraging real-time data from medical devices, the CDTH platform enables intelligent healthcare services such as disease prediction and medical resource optimization. However, the platform functions as a system of systems (SoS), comprising interconnected yet independent healthcare services. This complexity is further compounded by the integration of both black-box AI models and domain-specific mechanistic models, which pose challenges in ensuring the interpretability and trustworthiness of DTH models. To address these challenges, we propose a Model-Based Systems Engineering (MBSE)-driven DTH modeling methodology derived from systematic requirement and functional analyses. To implement this methodology effectively, we introduce a DTH model development approach using the X language, along with a comprehensive toolchain designed to streamline the development process. Together, this methodology and toolchain form a robust framework that enables engineers to efficiently develop interpretable and trustworthy DTH models for the CDTH platform. By integrating domain-specific mechanistic models with AI algorithms, the framework enhances model transparency and reliability. Finally, we validate our approach through a case study involving elderly patient care, demonstrating its effectiveness in supporting the development of DTH models that meet healthcare and interpretability requirements.

关键词： Digital Twin Model-based system engineering X Language Medical Cyber-Physical Systems Digital Twin Healthcare Design

来源：评论

学校读者我要写书评

暂无评论

End-to-End Explainable Fake News Detection Via Evidence-Claim Variational Causal Inference

引用

ACM Transactions on Information Systems 1000年

作者： Jinguang Wang Shengsheng Qian Jun Hu Wenxiang Dong Xudong Huang Richang Hong School of Public Security and Emergency Management Anhui University of Science and Technology China and State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science and Technology China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computing National University of Singapore Singapore Institute of Dataspace Hefei Comprehensive National Science Center China School of Computer Science and Information Engineering Hefei University of Technology China

Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-end Explainable Fake News Detection Network (\(EExpFND\)). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that \(EExpFND\) sets a new benchmark in performance. The code is available at https://***/r/EExpFND-F5C6.

关键词： Social media Explainable fake news detection Causal inference

来源：评论

学校读者我要写书评

暂无评论

Software Engineering for OpenHarmony: A Research Roadmap

引用

ACM computing Surveys 1000年

作者： Li Li Xiang Gao Hailong Sun Chunming Hu Xiaoyu Sun Haoyu Wang Haipeng Cai Ting Su Xiapu Luo Tegawendé Bissyande Jacques Klein John Grundy Tao Xie Haibo Chen Huaimin Wang Beihang University Beijing China School of Software Beihang University Beijing China Australian National University Canberra Australia Huazhong University of Science and Technology Wuhan China Washington State University Pullman United States Software Institute Shanghai Key Laboratory of Trustworthy Computing shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong SnT University of Luxembourg Luxembourg Luxembourg Faculty of Information Technology Monash University Clayton Australia Computer Science Peking University Beijing China Shanghai Jiao Tong University Shanghai China Natl Univ Def Technol Changsha China

Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, namely Android and iOS. OpenHarmony, a newly open-sourced mobile platform, has rarely been considered, although it is the one requiring the most attention as OpenHarmony is expected to occupy one-third of the market in China (if not in the world). To fill the gap, we present to the mobile software engineering community a research roadmap for encouraging our fellow researchers to contribute promising approaches to OpenHarmony. Specifically, we start by presenting a tertiary study of mobile software engineering, attempting to understand what problems have been targeted by the mobile community and how they have been resolved. We then summarize the existing (limited) achievements of OpenHarmony and subsequently highlight the research gap between Android/iOS and OpenHarmony. This research gap eventually helps in forming the roadmap for conducting software engineering research for OpenHarmony.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ShuffleInfer: Disaggregate LLM Inference for Mixed Downstream Workloads

引用

ACM Transactions on architecture and Code Optimization 1000年

作者： CunChen Hu HeYang Huang LiangLiang Xu XuSheng Chen Chenxi Wang Jiang Xu Shuang Chen Hao Feng Sa Wang Yungang Bao Ninghui Sun Yizhou Shan Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture beijing Beijing China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture beijing Beijing China Xidian University Xian China Huawei Cloud Computing Technologies Co Ltd Guiyang China Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Transformer-based large language model (LLM) inference serving is now the backbone of many cloud services. LLM inference consists of a prefill phase and a decode phase. However, existing LLM deployment practices often overlook the distinct characteristics of these phases, leading to significant interference. To mitigate interference, our insight is to carefully schedule and group inference requests based on their characteristics. We realize this idea in ShuffleInfer through three pillars. First, it partitions prompts into fixed-size chunks so that the accelerator always runs close to its computation-saturated limit. Second, it disaggregates prefill and decode instances so each can run independently. Finally, it uses a smart two-level scheduling algorithm augmented with predicted resource usage to avoid decode scheduling hotspots. Results show that ShuffleInfer improves time-to-first-token (TTFT), job completion time (JCT), and inference efficiency in turns of performance per dollar by a large margin, e.g., it uses 38% less resources all the while lowering average TTFT and average JCT by 97% and 47%, respectively.

关键词： LLM Serving Disaggregated Interference Schedule

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：