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Data Compression Conference (DCC)

作者： Sun, Hui Ma, Huidong Zheng, Yingfeng Xie, Haonan Yan, Meng Zhong, Cheng Liu, Xiaoguang Wang, Gang Nankai Univ Coll Comp Sci Nankai Baidu Joint Lab Parallel & Distributed Software Technol Lab Tianjin 300350 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China

ISBN: (纸本)9798350385885;9798350385878

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. © 2024 IEEE.

关键词： Benchmark Data Compression DNA Long Reads Lossless

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MRASS: Dynamic Task Scheduling enabled High Multi-cluster Resource Availability in JointCloud 13

MRASS: Dynamic Task Scheduling enabled High Multi-cluster Re...

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13th IEEE International Conference on Joint Cloud Computing (JCC)

作者： Gao, Fei Wang, Huaimin Shi, Peichang Fu, Xiang Zhong, Tao Kong, Jinzhu Natl Univ Def Technol Nat Key Lab Parallel & Distributed Proc Coll Comp Sci Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci Key Lab Software Engn Complex Syst Changsha 410073 Peoples R China Kylin Software Co Ltd Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311100 Peoples R China

ISBN: (数字)9781665462853

ISBN: (纸本)9781665462853

As the new paradigm of JointCloud Computing matures, enterprises are trying to build multiple Kubernetes clusters on different clouds to deploy tasks, with the advantages of disaster backup, low latency, and avoidance of single vendor lock-in, etc. Tasks in a JointCloud environment, always have highly diversified resource demands on CPU, memory, disk, and network. However, the mismatch between these tasks and heterogeneous clusters can easily cause many resource fragments, resulting in low resource availability. Therefore, the task scheduling strategy is the key to solving the above problem. The existing task schedule strategies for multi-clusters are always aiming at clusters' load balancing instead of increasing the resource availability. In this paper, we propose a dynamic task scheduling framework with the design of multi-cluster resource high-availability schedule strategy (MRASS) based on historical task resource consumption. MRASS conducts a cooperation model between multiple clusters and tasks, and proposes an indicator of resource availability, which is used to optimize the proportion of remaining resources of the cluster to keep approaching the proportion of resource requirements of future tasks, thereby execute more tasks within limited resources. Extensive numerical results confirm that the strategy has stable performance and performs well with different initial cluster resource setting, task resource type and task number. Compared with the existing algorithm, MRASS can place up to 20% more tasks, and the success rate of first placement of tasks can reach over 98%.

关键词： JointCloud multi-resource availability resource fragments multi-cluster cooperation

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A scalable parallel preconditioned conjugate gradient method for bundle adjustment

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APPLIED INTELLIGENCE 2022年第1期52卷 753-765页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Natl Univ Def Technol Sch Comp Sci Lab Software Engn Complex Syst Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sch Comp Sci Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Hubei Univ Sci & Technol Sch Resources Environm Sci & Engn Water & Land Resources Res Ctr Middle Reaches Yan Xianning 437100 Hubei Peoples R China

Bundle adjustment is a fundamental problem in computer vision, with important applications such as 3D structure reconstruction from 2D images. This paper focuses on large-scale bundle adjustment tasks, e.g., city-wide 3D reconstruction, which require highly efficient solutions. For this purpose, it is common to apply the Levenberg-Marquardt algorithm, whose bottleneck lies in solving normal equations. The majority of recent methods focus on achieving scalability through modern hardware such as GPUs and distributed systems. On the other hand, the core of the solution, i.e., the math underlying the optimizer for the normal equations, remains largely unimproved since the proposal of the classic parallel bundle adjustment (PBA) algorithm, which increasingly becomes a major limiting factor for the scalability of bundle adjustment. This paper proposes parallel preconditioned conjugate gradient (PPCG) method, a novel parallel method for bundle adjustment based on preconditioned conjugate gradient, which achieves significantly higher efficiency and scalability than existing methods on the algorithmic level. The main idea is to exploit the sparsity of the Hessian matrix and reduce its structure parameters through an effective parallel Schur complement method;the result of this step is then fed into our carefully designed PPCG method which reduces matrix operations that are either expensive (e.g., large matrix reverse or multiplications) or scales poorly to multi-processors (e.g., parallel Reduce operators). Extensive experiments demonstrate that PPCG outperforms existing optimizers by large margins, on a wide range of datasets.

关键词： Structure from motion Bundle adjustment Preconditioned conjugate gradient

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Transductive Relation-Propagation With Decoupling Training for Few-Shot Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第11期33卷 6652-6664页

作者： Ma, Yuqing Bai, Shihao Liu, Wei Wang, Shuo Yu, Yue Bai, Xiao Liu, Xianglong Wang, Meng Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Natl Univ Def Technol Coll Comp Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing 100191 Peoples R China Hefei Univ Technol Sch Comp & Informat Sci Hefei 230009 Peoples R China

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.

关键词： Task analysis Training Knowledge engineering Semantics Learning systems laboratories Extraterrestrial measurements Decoupling training few-shot learning knowledge transfer relation-propagation graph

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Lite it fly: An All-Deformable-Butterfly Network

arXiv

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arXiv 2023年

作者： Lin, Rui Li, Jason Chun Lok Zhou, Jiajun Huang, Binxiao Ran, Jie Wong, Ngai Distributed and Parallel Software Lab Huawei China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns. The lately proposed deformable butterfly (DeBut) decomposes the filter matrix into generalized, butterfly-like factors, thus achieving network compression orthogonal to the traditional ways of pruning or low-rank decomposition. This work reveals an intimate link between DeBut and a systematic hierarchy of depthwise and pointwise convolutions, which explains the empirically good performance of DeBut layers. By developing an automated DeBut chain generator, we show for the first time the viability of homogenizing a DNN into all DeBut layers, thus achieving an extreme sparsity and compression. Various examples and hardware benchmarks verify the advantages of All-DeBut networks. In particular, we show it is possible to compress a PointNet to Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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distributed Relation Discovery in Internet of Things

Distributed Relation Discovery in Internet of Things

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International Conference on Cloud Computing and Big Data (CCBD)

作者： Zhu, Weiping Lu, Hongliang Cui, Xiaohui Wuhan Univ Int Sch Software Wuhan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781479966219

Internet of things is an emerging technology that aims to connect various smart objects in our daily life. It facilitates the information exchange and control among objects. In the Internet of things, it is important to discover various relations among objects for analyzing and mining useful knowledge. Existing works on relation discovery mainly focus on centralized processing. It is not suitable for Internet of things due to unavailable of server, one-point failure, computation bottleneck, and security and business concerns. In this paper, we propose a distributed approach to discover the relations among objects in Internet of things. We first build the distritbuted system model which may include multiple relation discovery tasks. Based on that, we design an approach utilizing distributed spanning tree to extract the relations. Rather than sending all the information to the server, the objects only need to send the information to a local leader object in our algorithm. We also discuss more about how to improve the performance of the proposed approach and how to relax the system constraints. Extensive simulation have been done and the results show that the proposed approach outperforms existing approaches in terms of the data amount of transmission.

关键词： distributed Relation Discovery Internet of Things

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A direct execution approach to simulating mobile agent algorithms

A direct execution approach to simulating mobile agent algor...

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International Symposium on parallel and distributed Processing and Applications (ISPA 2003)

作者： Li, XH Cao, JN He, YX Hong Kong Polytech Univ Dept Comp Internet & Mobile Comp Lab Kowloon Hong Kong Peoples R China Wuhan Univ State Key Lab Software Engn Data & Knowledge Engn Lab Wuhan 430072 Hubei Peoples R China Wuhan Univ Sch Comp Parallel & Distributed Comp Lab Wuhan 430072 Hubei Peoples R China

Mobile agent technology has been applied to develop the solutions for various kinds of parallel and distributed computing problems. However, performance evaluation of mobile agent algorithms remains a difficult task, mainly due to the characteristics of mobile agents such as distributed and asynchronous execution, autonomy and mobility. This paper proposes a general approach based on direct execution simulation for evaluating the performance of mobile agent algorithms by collecting and analyzing the information about the agents during their execution. We describe the proposed generic simulation model, named MADES, the architecture of a software environment based on MADES, and a prototype implementation. A mobile agent-based distributed load balancing algorithm has been used for experiments with the prototype.

关键词： mobile agent direct execution simulation parallel and distributed simulation MADES

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Leveraging Multi-view Inter-passage Interactions for Neural Document Ranking 22

Leveraging Multi-view Inter-passage Interactions for Neural ...

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15th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Fu, Chengzhen Hu, Enrui Feng, Letian Dou, Zhicheng Jia, Yantao Chen, Lei Yu, Fan Cao, Zhao Huawei Technol Co Ltd Distributed & Parallel Software Lab Shenzhen Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9781450391320

The configuration of 512 window size prevents transformers from being directly applicable to document ranking that requires larger context. Hence, recent works propose to estimate document relevance with fine-grained passage-level relevance signals. A limitation of such models, however, is that scoring each passage independently falls short in modeling inter-passage interactions and leads to unsatisfactory results. In this paper, we propose a Multiview inter-passage Interaction based Ranking model (MIR), to combine intra-passage interactions and inter-passage interactions in a complementary manner. The former captures local semantic relations inside each passage, whereas the latter draws global dependencies between different passages. Moreover, we represent inter-passage relationships via multi-view attention patterns, allowing information propagation at token, sentence, and passage-level. The representations at different levels of granularity, being aware of global context, are then aggregated into a document-level representation for ranking. Experimental results on two benchmarks show that modeling inter-passage interactions brings substantial improvements over existing passage-level methods.

关键词： Document ranking Inter-passage attention Intra-passage attention

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distributed and parallel Sparse Computing for Very Large Graph Neural Networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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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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Flora: Dual-Frequency LOss-Compensated ReAl-Time Monocular 3D Video Reconstruction 37

Flora: Dual-Frequency LOss-Compensated ReAl-Time Monocular 3...

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37th AAAI Conference on Artificial Intelligence (AAAI) / 35th Conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Wang, Likang Gong, Yue Wang, Qirui Zhou, Kaixuan Chen, Lei Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Data Sci & Analyt Thrust Guangzhou Peoples R China Huawei Technol Distributed & Parallel Software Lab Shenzhen Peoples R China Huawei Technol Riemann Lab Shenzhen Peoples R China Huawei Technol Fundamental Software Innovat Lab Shenzhen Peoples R China

ISBN: (纸本)9781577358800

In this work, we propose a real-time monocular 3D video reconstruction approach named Flora for reconstructing delicate and complete 3D scenes from RGB video sequences in an end-to-end manner. Specifically, we introduce a novel method with two main contributions. Firstly, the proposed feature aggregation module retains both color and reliability in a dual-frequency form. Secondly, the loss compensation module solves missing structure by correcting losses for falsely pruned voxels. The dual-frequency feature aggregation module enhances reconstruction quality in both precision and recall, and the loss compensation module benefits the recall. Notably, both proposed contributions achieve great results with negligible inferencing overhead. Our state-of-the-art experimental results on real-world datasets demonstrate Flora's leading performance in both effectiveness and efficiency. The code is available at https://***/NoOneUST/Flora.

关键词： Image reconstruction

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