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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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Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet Networks 23rd

Enhancing Resolution of Inferring Hi-C Data Integrating U-Ne...

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23rd International Conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Zhao, Fengjiao Li, Na Zhong, Cheng School of Computer Electronics and Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities Guangxi Nanning530004 China

ISBN: (纸本)9783031299261

The Hi-C data is the basis of three-dimensional chromosome structure reconstruction. In this paper, a method integrating U-Net and ResNET network is proposed to infer high resolution Hi-C data from existing low-resolution Hi-C data. Firstly, the U-Net is used as the backbone network to make full use of the shallow layer details and deep layer essential features of Hi-C data. Secondly, the residual module is introduced into the coding path and decoding path of the network to increase the depth of the model and prevent network degradation. Finally, the down sampling is used to reduce the calculation amount to decrease the required running time for the network model. The experimental results on real datasets showed that the proposed method had better performance than existing methods, and the inferred high resolution Hi-C data was closer to the real Hi-C data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chromosomes

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion

arXiv

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arXiv 2024年

作者： Shan, Yongxue Zhou, Jie Peng, Jie Zhou, Xin Yin, Jiaqian Wang, Xiaodong National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Denfense Technology Changsha China

In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no current studies specifically address the shared knowledge within KGC. To bridge this gap, we introduce a multi-level Shared Knowledge Guided learning method (SKG) that operates at both the dataset and task levels. On the dataset level, SKG-KGC broadens the original dataset by identifying shared features within entity sets via text summarization. On the task level, for the three typical KGC subtasks – head entity prediction, relation prediction, and tail entity prediction – we present an innovative multi-task learning architecture with dynamically adjusted loss weights. This approach allows the model to focus on more challenging and underperforming tasks, effectively mitigating the imbalance of knowledge sharing among subtasks. Experimental results demonstrate that SKG-KGC outperforms existing text-based methods significantly on three well-known datasets, with the most notable improvement on WN18RR (MRR: 66.6%→72.2%, Hit@1: 58.7%→67.0%). Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Funnel: An Efficient Sparse Attention Accelerator with Multi-Dataflow Fusion

Funnel: An Efficient Sparse Attention Accelerator with Multi...

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International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Shenghong Ma Jinwei Xu Jingfei Jiang Yaohua Wang Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The self-attention mechanism is the core component of Transformer, which provides a powerful ability to understand the sequence context. However, the self-attention mechanism also suffers from a large amount of redundant computation. Model sparsification can effectively reduce computational load, but the irregularity of non-zeros introduced by sparsification significantly decreases hardware efficiency. This paper proposes Funnel, an accelerator that dynamically predicts sparse attention patterns and efficiently processes unstructured sparse data. Firstly, we adopt a fast quantization method based on lookup table to minimize the cost of sparse patterns prediction. Secondly, we propose Funnel computing Unit (FCU), a hardware architecture that efficiently handles sparse attention through multi-dataflow fusion. Sampled Dense-Dense Matrix Multiplication (SDDMM) and Sparse-Dense Matrix Multiplication (SpMM) are core components of sparse attention mechanism. FCU unifies the computation ways of matrix inner product and row-wise product to support SDDMM and SpMM at the same time, which greatly reduces the storage and movement overhead of intermediate results. Lastly, we devise a lightweight buffer and data tiling strategy tailored to the proposed accelerator, aimed at enhancing data reuse. Experiments demonstrate that our accelerator achieves 0.10-0.25 sparsity with small accuracy loss. When computing the self-attention layer, it attains hardware efficiency ranging from 60% to 85%. Compared to CPU and GPU, it achieves 5.60x and 8.20x speedup. Compared to the state-of-the-art attention accelerators A 3 , SpAtten, FTRANS, and Sanger, it achieves 7.37x, 4.52x, 9.58x, and 3.08x speedup.

关键词： Quantization (signal) Graphics processing units computer architecture Transformer cores Transformers Prediction algorithms Hardware Spatiotemporal phenomena Sparse matrices Sorting

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LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences

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Frontiers of computer Science 2024年第4期18卷 39-47页

作者： Ziwang FU Feng LIU Qing XU Xiangling FU Jiayin QI School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Beijing 100876China Shanghai International School of Chief Technology Officer East China Normal UniversityShanghai 200062China School of Computer Science and Technology East China Normal UniversityShanghai 200062China School of Cyberspace Security Guangzhou UniversityGuangdong 510006China

Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion *** approaches use directional pairwise attention or a message hub to fuse language,visual,and audio ***,these fusion methods are often quadratic in complexity with respect to the modal sequence length,bring redundant information and are not *** this paper,we propose an efficient neural network to learn modality-fused representations with CB-Transformer(LMR-CBT)for multimodal emotion recognition from unaligned multi-modal ***,we first perform feature extraction for the three modalities respectively to obtain the local structure of the ***,we design an innovative asymmetric transformer with cross-modal blocks(CB-Transformer)that enables complementary learning of different modalities,mainly divided into local temporal learning,cross-modal feature fusion and global self-attention *** addition,we splice the fused features with the original features to classify the emotions of the ***,we conduct word-aligned and unaligned experiments on three challenging datasets,IEMOCAP,CMU-MOSI,and *** experimental results show the superiority and efficiency of our proposed method in both *** with the mainstream methods,our approach reaches the state-of-the-art with a minimum number of parameters.

关键词： modality-fused representations cross-model blocks multimodal emotion recognition unaligned multimodal sequences computational affection

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Boundary-Driven Active Learning for Anomaly Detection in Time Series Data Streams

Boundary-Driven Active Learning for Anomaly Detection in Tim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaohui Zhou Yijie Wang Hongzuo Xu Mingyu Liu National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

The key to anomaly detection in time series data streams (TSDS) lies in the ability to adapt to evolving data. Active learning for anomaly detection has shown such ability by leveraging expert feedback. However, many studies in this research line strive to optimize performance by exhausting the query budget, lacking consideration of query necessity, which means some unnecessary queries may wrongly lead the model to overfit the trivial information and incur additional consumption in both human labeling and model execution. This paper proposes Boundary-driven Active Learning for Anomaly Detection (BALAD). BALAD utilizes deep one-class classification to construct a hypersphere boundary to sense data abnormality and filters out unnecessary queries by dividing the boundary region. We further harness the hypersphere boundary to quantitatively measure data difficulty, and a focal loss is introduced to prioritize hard samples. The boundary is flexibly adapted during each feedback iteration to accommodate changes in TSDS. Extensive experiments on six datasets demonstrate that BALAD significantly outperforms the state-of-the-art anomaly detection methods.

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Detecting Duplicate Contributions in Pull-Based Model CombiningTextual and Change Similarities

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Journal of computer Science & Technology 2021年第1期36卷 191-206页

作者： Zhi-Xing Li Yue Yu Tao Wang Gang Yin Xin-Jun Mao Huai-Min Wang Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology Changsha 410073China Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'***,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this *** not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant *** this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission *** a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing *** then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change *** evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.

关键词： Pull-request Duplicate detection textual similarity change similarity

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HSDP: Accelerating Large-scale Model Training via Efficient Sharded Data parallelism

HSDP: Accelerating Large-scale Model Training via Efficient ...

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International Symposium on parallel and distributed Processing with Applications, ISPA

作者： Yanqi Hao Zhiquan Lai Wei Wang Shengwei Li Weijie Liu Keshi Ge Dongsheng Li National Key Laboratory of Parallel and Distributed Computing(PDL) College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Large deep neural network (DNN) models have demonstrated exceptional performance across diverse downstream tasks. Sharded data parallelism (SDP) has been widely used to reduce the memory footprint of model states. In a DNN training cluster, a device usually has multiple inter-device links that connect to other devices, like NVLink and InfiniBand. However, existing SDP approaches employ a single link at any given time, encountering challenges in efficient training due to significant communication overheads. We observe that the inter-device links can work independently without affecting each other. To reduce the fatal communication overhead of distributed training of large DNNs, this paper introduces HSDP, an efficient SDP training approach that enables the simultaneous utilization of multiple inter-device links. HSDP partitions models in a novel fine-grained manner and orchestrates the communication processes of partitioned parameters while considering inter-device links. This design enables concurrent communication execution and reduces communication overhead. To further optimize the training performance of HSDP, we propose a HSDP planner. The HSDP planner first abstracts the model partition and execution of HSDP into a communication parallel strategy, and builds a cost model to estimate the performance of each strategy. We then formulate the strategy searching as an optimization problem and solve it with an off-the-shelf solver. Evaluations on representative DNN workloads demonstrate that HSDP achieves up to 1.30× speedup compared to the state-of-the-art SDP training approaches.

关键词： Training Performance evaluation Costs distributed databases Artificial neural networks parallel processing Throughput Search problems Data models Optimization

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CHIME: A Cache-Efficient and High-Performance Hybrid Index on Disaggregated Memory 24

CHIME: A Cache-Efficient and High-Performance Hybrid Index o...

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30th ACM Symposium on Operating Systems Principles, SOSP 2024

作者： Luo, Xuchuan Shen, Jiacheng Zuo, Pengfei Wang, Xin Lyu, Michael R. Zhou, Yangfan National Key Laboratory of Parallel and Distributed Computing Changsha China School of Computer Science Fudan University Shanghai China Duke Kunshan University Kunshan China Huawei Cloud Shenzhen China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400712517

Disaggregated memory (DM) is a widely discussed datacenter architecture in academia and industry. It decouples computing and memory resources from monolithic servers into two network-connected resource pools. Range indexes are widely adopted by storage systems on DM to efficiently locate and query remote data. However, existing range indexes on DM suffer from either high computing-side cache consumption or high memory-side read amplifications. In this paper, we propose CHIME, a hybrid index combining B+ trees with hopscotch hashing, to achieve low cache consumption and low read amplifications simultaneously. There are three challenges in constructing CHIME on DM, i.e., the complicated optimistic synchronization, the extra metadata access, and the read amplifications introduced by hopscotch hashing. CHIME leverages 1) a three-level optimistic synchronization scheme to synchronize read and write operations with various granularities, 2) an access-aggregated metadata management technique to eliminate extra metadata accesses by piggybacking and replicating metadata, and 3) an effective hotness-aware speculative read mechanism to mitigate the read amplifications of hopscotch hashing. Experimental results show that CHIME outperforms the state-of-the-art range indexes on DM by up to 5.1× with the same cache size and achieves similar performance with up to 8.7× lower cache consumption. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory architecture

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