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International Conference on Parallel Processing (ICPP)

作者： Chuntao Jiang Zhibin Yu Hai Jin Xiaofei Liao Lieven Eeckhout Yonggang Zeng Chengzhong Xu Service Computing Technology and System Lab Huazhong University of Science and Technology China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Electronics and Information Systems Ghent University Belgium Wayne State University USA

Warm up is a crucial issue in sampled micro architectural simulation to avoid performance bias by constructing accurate states for micro-architectural structures before each sampling unit. Not until very recently have researchers proposed Time-Based Sampling (TBS) for the sampled simulation of multi-threaded applications. However, warm up in TBS is challenging and complicated, because (i) full functional warm up in TBS causes very high overhead, limiting overall simulation speed, (ii) traditional adaptive functional warm up for sampling single-threaded applications cannot be readily applied to TBS, and (iii) check pointing is inflexible (even invalid) due to the huge storage requirements and the variations across different runs for multi-threaded applications. In this work, we propose Shorter On-Line (SOL) warm up, which employs a two-stage strategy, using 'prime' warm up in the first stage, and an extended 'No-State-Loss (NSL)' method in the second stage. SOL is a single-pass, on-line warm up technique that addresses the warm up challenges posed in TBS in parallel simulators. SOL is highly accurate and efficient, providing a good trade-off between simulation accuracy and speed, and is easily deployed to different TBS techniques. For the PARSEC benchmarks on a simulated 8-core system, two state-of-the-art TBS techniques with SOL warm up provide a 7.2× and 37× simulation speedup over detailed simulation, respectively, compared to 3.1× and 4.5× under full warm up. SOL sacrifices only 0.3% in absolute execution time prediction accuracy on average.

关键词： Adaptation models Computational modeling Checkpointing Hardware Benchmark testing Acceleration Measurement

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Collaborative Tensor Factorization and its Application in POI Recommendation

Collaborative Tensor Factorization and its Application in PO...

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IEEE International Conference on Networking, Sensing and Control

作者： Wenjing Luan Guanjun Liu Changjun Jiang Department of Computer Science & Technology Tongji University Shanghai P. R. China MoE Key Lab of Embedded System & Service Computing Tongji University Shanghai P. R. China

ISBN: (纸本)9781467399760

The rapid prevalence of location-based services provides an opportunity to understand people's mobility behavior, helps people easily decide where to go at the next moment, and helps salesmen select the most suitable commercial sites to delivery advertisements. In this paper, we propose a POI recommendation method based on collaborative tensor factorization. Firstly, we present a generalized objective function for collaboratively factorizing an n-mode tensor with m feature matrices. Secondly, all users' check-in behaviors are modeled as a 3-mode tensor and three feature matrices are extracted to characterize the time distribution, category distribution and POI correlation. Thirdly, we fill the missing entries by using our collaborative tensor factorization approach such that a user's rating to a POI at a specific time is recovered. Our experiment on a real check-in database shows that our method can provide more accurate recommendation.

关键词： Tensor factorization context feature extraction POI recommendation

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Multi-stage network embedding for exploring heterogeneous edges

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Ye, Fanghua Xie, Xing Shi, Xuanhua Jin, Hai The National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China The Department of Computer Science University College London London United Kingdom Microsoft Research Asia Beijing China

The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this paper, we focus on exploring the heterogeneous edges for network representation learning. By considering each relationship as a view that depicts a specific type of proximity between nodes, we propose a multi-stage non-negative matrix factorization (MNMF) model, committed to utilizing abundant information in multiple views to learn robust network representations. In fact, most existing network embedding methods are closely related to implicitly factorizing the complex proximity matrix. However, the approximation error is usually quite large, since a single low-rank matrix is insufficient to capture the original information. Through a multi-stage matrix factorization process motivated by gradient boosting, our MNMF model achieves lower approximation error. Meanwhile, the multi-stage structure of MNMF gives the feasibility of designing two kinds of non-negative matrix factorization (NMF) manners to preserve network information better. The united NMF aims to preserve the consensus information between different views, and the independent NMF aims to preserve unique information of each view. Concrete experimental results on realistic datasets indicate that our model outperforms three types of baselines in practical applications. © 2021, CC BY.

关键词： Non-negative matrix factorization

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Knowledge granulation in interval-valued information systems

Knowledge granulation in interval-valued information systems

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International Workshop on Intelligent systems and Applications

作者： Zhang, Nan Miao, Duoqian Gao, Can Department of Computer Science and Technology Tongji University Shanghai 201804 China Department of Computer Science University of Regina Regina SK S4S 0A2 Canada Key Lab. of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G4 Canada

ISBN: (纸本)9781424498574

Rough set theory, proposed by Pawlak, has been proved to be a mathematical tool to deal with vagueness and uncertainty in intelligent information processing. In this paper, we propose the concept of knowledge granulation in interval-valued information systems, and discuss some important properties. From these properties, it can be shown that the proposed knowledge granulation provides important approaches to measuring the discernibility of different knowledge. It may be helpful for rule evaluation and knowledge discovery in interval-valued information systems. © 2011 IEEE.

关键词： Information systems

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MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

arXiv

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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Accelerating GPGPU architecture simulation 13

Accelerating GPGPU architecture simulation

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Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems

作者： Zhibin Yu Lieven Eeckhout Nilanjan Goswami Tao Li Lizy John Hai Jin Chengzhong Xu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China ELIS Department Ghent University Ghent Belgium Intelligent Design of Efficient Architectures Lab University of Florida Gainesville USA Department of Electrical and Computer Engineering University of Texas at Austin Austin USA Service Computing Technologies and System Lab/Cluster and Grid Computing Lab HUST Wuhan China Department of Electrical and Computer Engineering Wayne State University Detroit USA

ISBN: (纸本)9781450319003

Recently, graphics processing units (GPUs) have opened up new opportunities for speeding up general-purpose parallel applications due to their massive computational power and up to hundreds of thousands of threads enabled by programming models such as CUDA. However, due to the serial nature of existing micro-architecture simulators, these massively parallel architectures and workloads need to be simulated sequentially. As a result, simulating GPGPU architectures with typical benchmarks and input data sets is extremely time-consuming. This paper addresses the GPGPU architecture simulation challenge by generating miniature, yet representative GPGPU kernels. We first summarize the static characteristics of an existing GPGPU kernel in a profile, and analyze its dynamic behavior using the novel concept of the divergence flow statistics graph (DFSG). We subsequently use a GPGPU kernel synthesizing framework to generate a miniature proxy of the original kernel, which can reduce simulation time significantly. The key idea is to reduce the number of simulated instructions by decreasing per-thread iteration counts of loops. Our experimental results show that our approach can accelerate GPGPU architecture simulation by a factor of 88X on average and up to 589X with an average IPC relative error of 5.6%.

关键词： micro-architecture simulation performance general purpose graphics processing unit (GPGPU)

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LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

arXiv

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arXiv 2023年

作者： Huang, Kai Zhao, Junqiao Zhu, Zhongyang Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Local geometric information, i.e., normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://***/tiev-tongji/LOG-LIO. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical radar

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LIMOT: A Tightly-Coupled system for LiDAR-Inertial Odometry and Multi-Object Tracking

arXiv

引用

arXiv 2023年

作者： Zhu, Zhongyang Zhao, Junqiao Huang, Kai Tian, Xuebo Lin, Jiaye Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China The School of Surveying and Geo-Informatics Tongji University Shanghai China

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Moreover, the accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicles. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial odometry system that is capable of accurately estimating the poses of both ego-vehicle and objects. We propose a trajectory-based dynamic feature filtering method, which filters out features belonging to moving objects by leveraging tracking results before scan-matching. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on the KITTI tracking dataset and self-collected dataset show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other baseline methods. Our open-source implementation is available at https://***/tiev-tongji/LIMOT. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis

arXiv

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arXiv 2024年

作者： Huang, Kai Zhao, Junqiao Lin, Jiaye Zhu, Zhongyang Song, Shuangfu Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncertainty is often overlooked by the community. Moreover, the existing uncertainty propagation methods suffer from computational inefficiency. This paper proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface characteristics, along with an efficient local uncertainty analytical method for LiDAR-based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, which enables a fast approximation of uncertainty propagation. This approach eliminates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from O(n) to O(1) when a new point is added. Simulations and experiments on public datasets are conducted to validate the accuracy and efficiency of our formulations. The proposed methods have been integrated into a LIO system, which is available at https://***/tiev-tongji/LOG-LIO2. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition

arXiv

引用

arXiv 2023年

作者： Zhao, Junqiao Zhang, Fenglin Cai, Yingfeng Tian, Gengxuan Mu, Wenjie Ye, Chen Feng, Tiantian Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between frame sequences or extracting sequence descriptors for direct retrieval. However, the former is usually based on the assumption of constant velocity, which is difficult to hold in practice, and is computationally expensive and subject to sequence length. Although the latter overcomes these problems, existing sequence descriptors are constructed by aggregating features of multiple frames only, without interaction on temporal information, and thus cannot obtain descriptors with spatio-temporal discrimination. In this paper, we propose a sequence descriptor that effectively incorporates spatio-temporal information. Specifically, spatial attention within the same frame is utilized to learn spatial feature patterns, while attention in corresponding local regions of different frames is utilized to learn the persistence or change of features over time. We use a sliding window to control the temporal range of attention and use relative positional encoding to construct sequential relationships between different features. This allows our descriptors to capture the intrinsic dynamics in a sequence of frames. Comprehensive experiments on challenging benchmark datasets show that the proposed approach outperforms recent state-of-the-art methods. The code is available at https://***/tiev-tongji/Spatio-Temporal-SeqVPR. Copyright © 2023, The Authors. All rights reserved.

关键词： Query processing

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