Rong Gu

Nanjing University

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i-10 index

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Co-Authors

Yihua Huang

Nanjing University

Haipeng Dai

Nanjing University

Chunfeng Yuan

Nanjing University

Guihai Chen

Shanghai Jiao Tong University

Zhaokang Wang

Nanjing University of Aeronautics and Astronautics

Meng Li

Nanjing University

Jiaqi Zheng

Nanjing University

Guanghui Zhu

Nanjing University

Wanchun Dou

Nanjing University

Yihua Huang

Nanjing University

Cooperative Institutions

Nanjing University

128

Chinese Academy of Sciences

Tsinghua University

Tongji University

Peking University

Nanjing University of Science and Technology

University of Chinese Academy of Sciences

Huazhong University of Science and Technology

Nanjing University of Posts and Telecommunications

Zhejiang University

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Laser removal of fiber optic coating

Jim Angell J.B. Bernstein Rong Gu Phil Miller Gaku Ogura

There is an increasing need in the field of fiber optic telecommunications for stripped optical fiber. Many products, for example, require hermetic packaging. In order to hermetically seal around a fiber, the protective jacket must be stripped away, and replaced with a thin metal coating, and/or a metal ferrule to create the glass / metal seal. In many cases it is most practical to be able to strip the fiber in the middle, or mid span. Existing stripping methods include acid stripping and mechanical stripping, each of which, have limitations making them unsuitable for certain applications. A key aspect of a stripped fiber is its mechanical strength, typically measured as the point at which the fiber breaks under tensile stress. We report here the development of a laser stripping process, known as Reso-Strip, that is well suited for mid span stripping, and which yields clean fibers with tensile strengths well above industry standards.

Stripping (fiber)

Ferrule

10.2351/1.5059844

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Citations (1)

BENU: Distributed Subgraph Enumeration with Backtracking-Based Framework

2022 IEEE 38th International Conference on Data Engineering (ICDE) (2019)

Zhaokang Wang Rong Gu Weiwei Hu Chunfeng Yuan Yihua Huang

Given a small pattern graph and a large data graph, the task of subgraph enumeration is to find all the subgraphs of the data graph that are isomorphic to the pattern graph. The state-of-the-art distributed algorithms like SEED and CBF turn subgraph enumeration into a distributed multi-way join problem. They are inefficient in communication as they have to shuffle partial matching results that are much larger than the data graph itself during the join. They also spend non-trivial costs on constructing indexes for data graphs. Different from those join-based algorithms, we develop a new backtracking-based framework BENU for distributed subgraph enumeration. BENU divides a subgraph enumeration task into a group of local search tasks that can be executed in parallel. Each local search task follows a backtracking-based execution plan to enumerate subgraphs. The data graph is stored in a distributed database and is queried as needed. BENU only queries the necessary edges of the data graph and avoids shuffling partial matching results. We also develop an efficient implementation for BENU. We set up an in-memory database cache on each machine. Taking advantage of the inter-task and intra-task locality, the cache significantly reduces the communication cost with controllable memory usage. We conduct extensive experiments to evaluate the performance of BENU. The results show that BENU is scalable and outperforms the state-of-the-art methods by up to an order of magnitude.

Backtracking

Enumeration

Factor-critical graph

Distributed memory

10.1109/icde.2019.00021

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Citations (28)

A Time-Cost Based Automatic Scheduling Framework for Matrix Computation on Various Distributed Computing Platforms

2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2016)

Rong Gu Zhi‐Qiang Liu Chunfeng Yuan Yihua Huang

Matrix computation is considered to be the core of many machine learning and graph algorithm workloads. In traditional single-node age, numerical analysis platforms like R and Matlab provide matrix programming model natively. As data is increasingly scaled up in the Big Data era, there is an increasing demand to seamlessly integrate large-scale matrix computation into distributed data-parallel computing systems. Therefore a variety of matrix computation libraries have been implemented on these distributed computing platforms such as MPI, Hadoop and Spark. However, a specific matrix-based algorithm has quite different performance over different platforms and it is very challenging for data scientists to specify the platform or combination of platforms for a given algorithm workflow to achieve the best performance. To solve this problem, in this paper, we put forward a time-cost based scheduling framework that can automatically specify the best platforms for the matrix operations and schedule the execution workflow. We have implemented a system prototype which using R as the user language and MPI, R and Spark as the backend computing platforms. The experimental results show that our time-cost based model has good accuracy with less than 10% error rate on average. Moreover, the scheduling framework built on it achieves efficient performance in applications.

SPARK (programming language)

10.1109/ipdpsw.2016.53

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Explore the Brain Response to Naturalistic and Continuous Music Using EEG Phase Characteristics

Lecture notes in computer science (2016)

Jie Li Hongfei Ji Rong Gu Lusong Hou Zhicheng Zhang

Stimulus (psychology)

EEG-fMRI

Naturalism

10.1007/978-3-319-42291-6_29

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Citations (4)

Wheelchair Control Based on Multimodal Brain-Computer Interfaces

Lecture notes in computer science (2013)

Jie Li Hongfei Ji Lei Cao Rong Gu Bin Xia

Motor Imagery

Interface (matter)

10.1007/978-3-642-42054-2_54

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Citations (10)

Fluid-Shuttle: Efficient Cloud Data Transmission Based on Serverless Computing Compression

IEEE/ACM Transactions on Networking (2024)

Rong Gu Shulin Wang Haipeng Dai Xiaofei Chen Zhaokang Wang

10.1109/tnet.2024.3402561

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Citations (4)

SAFE

Rui Xia Haipeng Dai Jiaqi Zheng Rong Gu Xiaoyu Wang

Virtualized network functions (VNFs) enable software applications to replace traditional middleboxes, which is more flexible and scalable in the network service provision. This paper focuses on ensuring Service Availability via Failure Elimination (SAFE) using VNF scaling, that is, given the resource requirements of VNF instances, finding an optimal and robust instance consolidation strategy, which can recover from one instance failure quickly. To address the above problem, we present a framework based on rounding and dynamic programming. First, we discretize the range of resource requirements for VNF instances deployment into several sub-ranges, so that the number of instance types becomes a constant. Second, we further reduce the number of instance types by gathering several small instances into a bigger one. Third, we propose an algorithm built on dynamic programming to solve the instance consolidation problem with a limited number of instance types. We set up a testbed to profile the functional relationship between resource and throughput for different types of VNF instances, and conduct simulations to validate our theoretical results according to profiling results. The simulation results show that our algorithm outperforms the standby deployment model by 27.33% on average in terms of the number of servers required. Furthermore, SAFE has marginal overhead, around 7.22%, compared to instance consolidation strategy without VNF backup consideration.

Testbed

10.1145/3337821.3337832

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Citations (10)

DGST: Efficient and scalable suffix tree construction on distributed data-parallel platforms

Parallel Computing (2019)

Guanghui Zhu Guo Chen Le Lü Zhi Huang Chunfeng Yuan

Tree (set theory)

10.1016/j.parco.2019.06.002

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Citations (7)

Laser and competing process technologies for microfabrication

Craig R. Friedrich Rong Gu Xinming Huang

Nearly since its invention, laser technology has been used for medical treatment ranging from tattoo removal to correcting visual disorders. Industrial lasers have also been widely used for fabricating medical components. More recently, lasers are being used for microfabrication of new types of medical devices and for subsequent processing. Along a parallel path, many other microfabrication technologies have been developed and applied to the fabrication of microscale medical devices. While each process technology has its own unique attributes, many of these processes can be integrated to allow more flexibility in design and production. The paper provides a brief overview of the major microfabrication processes which can be integrated with laser processing. Because these processes continue to be developed, this overview provides a snapshot of the present state of development.

Microscale chemistry

10.2351/1.5059614

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Citations (1)

A Survey of Multi-Dimensional Indexes: Past and Future Trends

IEEE Transactions on Knowledge and Data Engineering (2024)

Li Ming-xin Han‐Cheng Wang Haipeng Dai Meng Li Chengliang Chai

Index structures are powerful tools for improving query performance and reducing disk access in database systems. Multi-dimensional indexes, in particular, are used to filter records effectively based on multiple attributes. Classical multi-dimensional index structures, such as KD-Tree, Quadtree, and R-Tree, have been widely used in modern databases. However, advancements in hardware and algorithms have led to the emergence of new types of multi-dimensional index structures. In this paper, we begin by reviewing classical multi-dimensional indexes. Next, we explore the approaches that leverage modern hardware features, such as Solid-State Drive, Non-Volatile Memory, Dynamic Random Access Memory, and Graphics Processing Unit, to improve the performance of multi-dimensional indexes in various aspects. Then, we investigate the novel work of multi-dimensional indexes that apply state-of-the-art machine learning techniques. Finally, we discuss the challenges and future research directions for multi-dimensional indexing methods.

Leverage (statistics)

Quadtree

Access method

10.1109/tkde.2024.3364183

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Citations (3)