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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Petit, Quentin R. Li, Chong Emad, Nahid Huawei Paris Research Center Université Paris-Saclay Boulogne-Billancourt France Huawei Paris Research Center Distributed and Parallel Software Lab Boulogne-Billancourt France Université Versailles Saint-Quentin Maison de la Simulation LI-PaRAD Saclay France

ISBN: (纸本)9781665480451

Deep learning (DL) requires high-performance processing on big data. Graph Neural Networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph's sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore. © 2022 IEEE.

关键词： Matrix algebra

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Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

arXiv

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arXiv 2022年

作者： Zhou, Jiawei Li, Xiaoguang Shang, Lifeng Luo, Lan Zhan, Ke Hu, Enrui Zhang, Xinyu Jiang, Hao Cao, Zhao Yu, Fan Jiang, Xin Liu, Qun9 Chen, Lei The Hong Kong University of Science and Technology Hong Kong Huawei Noah's Ark Lab Distributed and Parallel Software Lab Huawei China

To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR). However, there still remains a large discrepancy between the provided upstream signals and the downstream question-passage relevance, which leads to less improvement. To bridge this gap, we propose the HyperLink-induced Pre-training (HLP), a method to pre-train the dense retriever with the text relevance induced by hyperlink-based topology within Web documents. We demonstrate that the hyperlink-based structures of dual-link and co-mention can provide effective relevance signals for large-scale pre-training that better facilitate downstream passage retrieval. We investigate the effectiveness of our approach across a wide range of open-domain QA datasets under zero-shot, few-shot, multi-hop, and out-of-domain scenarios. The experiments show our HLP outperforms the BM25 by up to 7 points as well as other pre-training methods by more than 10 points in terms of top-20 retrieval accuracy under the zero-shot scenario. Furthermore, HLP significantly outperforms other pre-training methods under the other scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Hypertext systems

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Fast and scalable in-network lock management using lock fission 24

Fast and scalable in-network lock management using lock fiss...

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Proceedings of the 18th USENIX Conference on Operating Systems Design and Implementation

作者： Hanze Zhang Ke Cheng Rong Chen Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China and Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (纸本)9781939133403

distributed lock services are extensively utilized in distributed systems to serialize concurrent accesses to shared resources. The need for fast and scalable lock services has become more pronounced with decreasing task execution times and expanding dataset scales. However, traditional lock managers, reliant on server CPUs to handle lock requests, experience significant queuing delays in lock grant latency. Advanced network hardware (e.g. programmable switches) presents an avenue to manage locks without queuing delays due to their high packet processing power. Nevertheless, their constrained memory capacity restricts the manageable lock scale, thereby limiting their effect in large-scale *** paper presents FISSLOCK, a fast and scalable distributed lock service that exploits the programmable switch to improve (tail) latency and peak throughput for millions of locks. The key idea behind FISSLOCK is the concept of lock fission, which decouples lock management into grant decision and participant maintenance. FISSLOCK leverages the programmable switch to decide lock grants synchronously and relies on servers to maintain participants (i.e., holders and waiters) asynchronously. By using the programmable switch for routing, FISSLOCK enables on-demand fine-grained lock migration, thereby reducing the lock grant and release delays. FISSLOCK carefully designs and implements grant decision procedure on the programmable switch, supporting over one million locks. Evaluation using various benchmarks and a real-world application shows the efficiency of FISSLOCK. Compared to the state-of-the-art switch-based approach (NetLock), FISSLOCK cuts up to 79.1% (from 43.0%) of median lock grant time in the microbenchmark and improves transaction throughput for TATP and TPC-C by 1.76× and 2.28×, respectively.

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LRCB: A Comprehensive Benchmark Evaluation of Reference-free Lossless Compression Tools for Genomics Sequencing Long Reads Data

LRCB: A Comprehensive Benchmark Evaluation of Reference-free...

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Data Compression Conference (DCC)

作者： Hui Sun Huidong Ma Yingfeng Zheng Haonan Xie Meng Yan Cheng Zhong Xiaoguang Liu Gang Wang Nankai-Baidu Joint Lab Parallel and Distributed Software Technology Lab College of Computer Science Nankai University Tianjin China School of Electrical Engineering Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Nanning China

ISBN: (数字)9798350385878

ISBN: (纸本)9798350385885

The advancement of long reads sequencing technologies has led to a significant increase in biological sequencing big data. Although several reference-free compressors are available for saving long reads data storage space, choosing the suitable one is challenging due to the shortage of thorough and systematic evaluations of their lossless compression effectiveness, both dedicated and general-purpose. In this study, we performed benchmark examinations on 30 compressors, including 11 specialized for long reads and 19 general-purpose ones, using 31 real-world datasets with differing sequencing platforms, species, and lengths. Each lossless compressor was evaluated on 13 performance measures, including compression strength, compression robustness, as well as time and peak memory required for compression and decompression. Additionally, for future long reads data compressors, we outlined investigation directions with consideration for privacy-sensitive sequences data security, hardware parallel acceleration, parameter tuning framework, and system hardware-algorithm integration design. We summarized the results as the Long Reads Compression Benchmark, available at https://***/fahaihi/LRCB.

关键词： Sequential analysis Systematics Memory management Benchmark testing Compressors Loss measurement Time measurement

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Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking 21

Contrastive Learning of User Behavior Sequence for Context-A...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhu, Yutao Nie, Jian-Yun Dou, Zhicheng Ma, Zhengyi Zhang, Xinyu Du, Pan Zuo, Xiaochen Jiang, Hao Univ Montreal Montreal PQ Canada Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Distributed & Parallel Software Lab Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450384469

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked. To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking. Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking.

关键词： Context-aware Document Ranking Contrastive Learning User Behavior Sequence Data Augmentation

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Online Learning Bipartite Matching with Non-stationary Distributions

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2022年第5期16卷 83-83页

作者： Chen, Weirong Zheng, Jiaqi Yu, Haoyu Chen, Guihai Chen, Yixin Li, Dongsheng Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha 410073 Peoples R China

Online bipartite matching has attracted wide interest since it can successfully model the popular online carhailing problem and sharing economy. Existing works consider this problem under either adversary setting or i.i.d. setting. The former is too pessimistic to improve the performance in the general case;the latter is too optimistic to deal with the varying distribution of vertices. In this article, we initiate the study of the non-stationary online bipartite matching problem, which allows the distribution of vertices to vary with time and is more practical. We divide the non-stationary online bipartite matching problem into two subproblems, the matching problem and the selecting problem, and solve them individually. Combining Batch algorithms and deep Q-learning networks, we first construct a candidate algorithm set to solve the matching problem. For the selecting problem, we use a classical online learning algorithm, Exp3, as a selector algorithm and derive a theoretical bound. We further propose CDUCB as a selector algorithm by integrating distribution change detection into UCB. Rigorous theoretical analysis demonstrates that the performance of our proposed algorithms is no worse than that of any candidate algorithms in terms of competitive ratio. Finally, extensive experiments showthat our proposed algorithms havemuch higher performance for the non-stationary online bipartite matching problem comparing to the state-of-the-art.

关键词： Online bipartite matching non-stationary distributions online learning reinforcement learning

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Entity-relation triple extraction based on relation sequence information

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

作者： Zhang, Zhanjun Zhang, Haoyu Wan, Qian Liu, Jie Natl Univ Def Technol Coll Comp Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China Def Innovat Inst Beijing Peoples R China

Data overlap is a significant challenge in the task of entity-relation triple extraction. This task includes two research lines, line one first identifies entities and then predicts relations while line two completely shuffles the order. The methods in line two are more conducive to the optimization of the data overlap problem. Recent works have made breakthroughs in dealing with overlapping data, but there are still some defects such as difficulty in convergence and poor performance on datasets with numerous relations. To solve the above problems, we adopt a two-step strategy of first extracting subjects, and then predicting relation- object pairs. Considering the absence of connectivity between the two steps in the conventional method, we adopt the relation sequence as input in both steps and propose the TERS model. The relation sequence can connect two steps and improve the single-step and comprehensive extraction capability of the model. The TERS model consists of two modules. The first module performs information interaction and filters invalid subjects through the Text Relation Attention method. The second module implements multiple iterations of information interaction through the Information Flow method. The combination of these two modules contributes to the strong entity-relation triple extraction capability of our model. We evaluate our method on three public datasets. Extensive experiments show that our TERS model outperforms previous state-of-the-art models in triple extraction and overlapping data processing. Compared with other two-step extraction models, the advantages of our model are more obvious.

关键词： Data overlap Entity-relation triple Relation sequence Text Relation Attention Information Flow

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Feature and Performance Comparison of FaaS Platforms 14

Feature and Performance Comparison of FaaS Platforms

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14th IEEE International Conference on software Engineering and Service Science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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Time Efficient Tag Searching in Large-Scale RFID Systems: A Compact Exclusive Validation Method

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2022年第4期21卷 1476-1491页

作者： Liu, Xuan Yin, Jiangjin Liu, Jia Zhang, Shigeng Xiao, Bin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Sci & Technol Parallel & Distributed Proc Laborat Changsha 410073 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

RFID technology has been widely applied in a range of applications such as inventory control, warehouse management and supply chain logistics. Many practical applications need to search a given set of tags (called wanted tags) to determine which of them are present in the system, which is usually called tag searching. Existing tag searching protocols suffer performance bottleneck and the time efficiency and need to be improved. The bottleneck stems from two factors. First, the existing methods validate the wanted tags in a random way due to the randomness of the hash function, which unavoidably generate many useless slots. Second, in order to achieve the predefined reliability requirement, the existing methods have to repeatedly validate target tags multiple times. In this paper, we design new tag searching techniques of compact exclusive validation that break through the existing performance bottleneck from two aspects. First, our protocols avoid slot waste. Different from random tag validation, our protocols validate tags orderly by mapping the wanted tags and the slots in a one-to-one manner, which makes the reader be able to validate tags in every slot. Second, our protocols avoid repeated tag responding. By combining two lightweight indicators, we rapidly filter out non-wanted tags so that there is no interference when validating the wanted tags. Hence, we need to validate each wanted tag only once, which avoids redundant validation and greatly improves time efficiency. Our protocols work with the assumption that the rough number of tags in the system can be obtained by using existing estimation algorithms, but they do not need to know exactly which tags are in the system. Theoretical analysis illustrates our methods achieve linear time complexity. Extensive experimental results show that, our best protocol can improve the time efficiency by up to 81 percent when compared with the-state-of-art solution.

关键词： Protocols Radiofrequency identification Interference Servers Cats Mobile computing Reliability Time efficiency tag searching radio frequency identification (RFID) systems

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You Ought to Look Around: Precise, Large Span Action Detection 25

You Ought to Look Around: Precise, Large Span Action Detecti...

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25th International Conference on Pattern Recognition (ICPR)

作者： Pan, Ge Zhang, Han Yu, Fan Song, Yonghong Zhang, Yuanlin Yuan, Han Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Huawei Technol Distributed & Parallel Software Lab Shenzhen Guangdong Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9781728188089

For the action localization task, pre-defined action anchors are the cornerstone of mainstream techniques. State-of-the-art models mostly rely on a dense segmenting scheme, where anchors are sampled uniformly over the temporal domain with a predefined set of scales. However, it is not sufficient because action duration varies greatly. Therefore, it is necessary for the anchors or proposals to have a variable receptive field. In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features. In this part, we employ a stronger backbone I3D with SPN (Segment Pyramid Network) instead of C3D to obtain multi-scale features;2) a simple but useful feature fusion module named LFE (Local Feature Extraction). Compared with the fully connected layer and global average pooling, our LFE model is more advantageous for network to fit and fuse features. 3) a new feature segment aligning method called TPGC (Two Pathway Graph Convolution), which allows one proposal to leverage semantic features of adjacent proposals to update its content and make sure the proposals have a variable receptive field. YOLA add only a small overhead to the baseline network, and is easy to train in an end-to-end manner, running at a speed of 1097 fps. YOLA achieves a mAP of 58.3%, outperforming all existing models including both RGB-based and two stream on THUMOS'14, and achieves competitive results on ActivityNet 1.3.

关键词： Anchors

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