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10th ACM Workshop on Information Hiding and Multimedia Security (IH-MMSec)

作者： Li, Denghao Wang, Jianzong Kong, Lingwei Si, Shijing Huang, Zhangcheng Huang, Chenyu Xiao, Jing Ping An Technol Shenzhen Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781450393553

Machine learning techniques have been widely applied in modern financial activities. Participants in the field are aware of the importance of data privacy. Vertical federated learning (VFL) was proposed as a solution to multi-party secure computation for machine learning to obtain the huge data required by the models as well as keep the privacy of the data holders. However, previous research majorly analyzed the algorithms under ideal conditions. Data imbalance in VFL is still an open problem. In this paper, we propose a privacy-preserving sampling strategy for imbalanced VFL based on federated graph embedding of the samples, without leaking any distribution information. The participants of the federation provide partial neighbor information for each sample during the intersection stage and the controversial negative sample will be filtered out. Experiments were conducted on commonly used financial datasets and one real-world dataset. Our proposed approach obtained the leading F1 score on all tested datasets on comparing with the baseline under sampling strategies for VFL.

关键词： Federated Learning Data Imbalance Private Under-sampling Federated graph Financial Domain graph computing

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Linked Data Processing for Human-in-the-Loop in Cyber-Physical Systems

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第5期8卷 1238-1248页

作者： Zheng, Zhigao Mumtaz, Shahid Khosravi, Mohammad R. Menon, Varun G. Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Univ Santiago Inst Telecomunicacoes P-3810193 Aveiro Portugal Persian Gulf Univ Dept Comp Engn Bushehr *** Iran Shiraz Univ Technol Telecommun Grp Shiraz *** Iran SCMS Sch Engn & Technol Dept Comp Sci & Engn Ernakulam 683576 India

There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.

关键词： Data models Data processing Programming graphics processing units Computer architecture Computational modeling Smart devices Cyber-physical systems (CPSs) data partition graph computing Human-in-the-Loop (HITL) new architecture programming model semantic model

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Location Planning of PEV Fast Charging Station: An Integrated Approach Under Traffic and Power Grid Requirements

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第1期22卷 483-492页

作者： Mao, Daijiafan Tan, Jun Wang, Jiankang GEIRI North Amer San Jose CA 95134 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

Plug-in Electric Vehicles (PEV) are characterized as a type of unconventional electric load, and their Fast Charging Stations (FCS) are capital-intensive transportation service infrastructure. Therefore, FCS location planning must consider the requirements of electricity and transportation infrastructures simultaneously. This paper, for the first time, proposes a graph-computing based integrated location planning model, which maximizes PEV charging convenience while ensuring the power grid's reliability. In addition, the proposed model captures the long-term costs of critical grid assets induced by uncertainty and impulsiveness of charging demand. Finally, the model can be easily scaled to various coupling configurations and temporal resolutions through a graph-based scheme. The proposed model is cast as a multi-objective mixed-integer problem and is solved by the cross-entropy (CE) optimization algorithm, in which the computational efficiency is significantly improved with graph parallel computing techniques. The proposed planning model and graph implementation method are validated on a synthetic power-transportation coupled network.

关键词： Planning Computational modeling Transportation Load modeling Loading Power grids Reliability Fast charging station graph computing impact assessment location planning power grid

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VPC: Pruning connected components using vector-based path compression for graph500 (vol 3, pg 271, 2021)

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CCF TRANSACTIONS ON HIGH PERFORMANCE computing 2021年第4期3卷 427-427页

作者： Bai, Hao Gan, Xinbiao Xu, Tianjing Jia, Menghan Tan, Wen Chen, Juan Zhang, Yiming Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China

graphs are an effective approach for data representation and organization, and graph analysis is a promising killer application for AI systems. However, recently emerging extremely large graphs (consisting of trillions of vertices and edges) exceed the capacity of any small-/medium-scale clusters and thus necessitate the adoption of supercomputers for efficient graph processing. graph500 is the de facto standard for benchmarking supercomputers’ graph processing performance, and connected component (CC) is an important basic algorithm for graph500’s BFS and SSSP tests. However, current CC algorithms are inefficient on supercomputers and fast CC is expensive and challenging. In this paper, we propose VPC, an efficient method that prunes connected components using vector-based path compression. It includes the following innovations: (i) The data structure of the traversal algorithm is customized with the two-dimensional adjacency vector. (ii) The vector-based path compression is proposed for the union-find algorithm. (iii) Parallel VPC is proposed customized with Tianhe. Experimental results validate that the two-dimensional adjacency vector has better performance than other data structures and the vector-based path compression is used in the realization of the union-find algorithm. When the scale is 26, the performance of our algorithm is 1.38×graphic xlink:href="42514_2021_70_Article_***" />, 1.69×graphic xlink:href="42514_2021_70_Article_***" /> and 2.57×graphic xlink:href="42514_2021_70_Article_***" /> that of other algorithms. The union-find algorithm is used for connected components, and the performance of the algorithm is 5.14×graphic xlink:href="42514_2021_70_Article_***" /> and 5.01×graphic xlink:href="42514_2021_70_Article_***" /> that of BFS and DFS respectively.

关键词： Connected components graph computing graph500 Path compression Two-dimensional vector

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Why blockchain needs graph: A survey on studies, scenarios, and solutions

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JOURNAL OF PARALLEL AND DISTRIBUTED computing 2023年第1期180卷

作者： Song, Jie Zhang, Pengyi Qu, Qiang Bai, Yongjie Gu, Yu Yu, Ge Northeastern Univ Software Coll Shenyang 110819 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Huawei Cloud Tech Co Ltd Blockchain Lab Shenzhen 518101 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China

The popularity of blockchain platforms and their applications in industry and academia keeps rising. The multifarious requirements stimulate another technique, graph data and algorithms, to join the blockchains;thus, studies, scenarios, and solutions about graph-related blockchains have emerged. This paper aims to see whether the state-of-the-art studies satisfy the applications through a comprehensive survey on graph-related blockchains. To answer why a blockchain needs graphs in general, we analyze literature about blockchain and graph, as well as use cases on the application-oriented and graph related scenarios collected from practical blockchain projects. The paper summarizes three graph-related blockchain studies: graph algorithms for blockchains, graph data in blockchains, and graph applications on blockchains. Based on these summarization, it figures out the gaps between the studies and the applications, that is, few of studies natively integrate graph computing into a blockchain. Here, the "graph integration" means processing on-chain graph data, which contains blockchain information, in a realtime, distributed, and consensual manner. We propose the prospect of the graph-integrated Blockchain Platform (GiBP for short), and explain why a GiBP is inevitable, the challenges of a GiBP, and the GiBPs' main functions and features people expect for future research. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Blockchain Blockchain applications Blockchain platform graph algorithm graph computing

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Ascetic: Enhancing Cross-Iterations Data Efficiency in Out-of-Memory graph Processing on GPUs 21

Ascetic: Enhancing Cross-Iterations Data Efficiency in Out-o...

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50th International Conference on Parallel Processing (ICPP)

作者： Tang, Ruiqi Zhao, Ziyi Wang, Kailun Gong, Xiaoli Zhang, Jin Wang, Wenwen Yew, Pen-Chung Nankai Univ Tianjin Peoples R China Univ Georgia Athens GA 30602 USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9781450390682

graph analytics are widely used in real-world applications, and GPUs are major accelerators for such applications. However, as graph sizes become significantly larger than the capacity of GPU memory, the performance can degrade significantly due to the heavy overhead required in moving a large amount of graph data between CPU main memory and GPU memory. Some existing approaches have tried to exploit data locality and addressed the issues of memory oversubscription on GPUs. However, these approaches have yet to take advantage of the data reuse cross iterations because of the data sizes in most large-graph analytics. In our studies, we have found that in most graph applications the graph traversals exhibit a roughly sequential scan over the graph data with an extremely large memory footprint. Based on the observation, we propose a novel framework, called Ascetic, to exploit temporal locality with very long reuse distances. In Ascetic, the GPU memory is divided into a Static Region and an On-demand Region. The static region can exploit data reuse across iterations. The on-demand region is designed to load the data requested in the iteration of the graph traversal while not found in the static region. We have implemented a prototype of the Ascetic framework and conducted a series of experiments on performance evaluation. The experimental results show that Ascetic can significantly reduce the data transfer overhead, and allow more overlapped execution between GPU and CPU, which leads to an average of 2.0x speedup over a state-of-the-art approach.

关键词： graph computing GPU memory oversubscription Partitionbased method Data Reuse

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Random Walks on Huge graphs at Cache Efficiency 21

Random Walks on Huge Graphs at Cache Efficiency

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28th ACM Symposium on Operating Systems Principles (SOSP)

作者： Yang, Ke Ma, Xiaosong Thirumuruganathan, Saravanan Chen, Kang Wu, Yongwei Tsinghua Univ Dept Comp Sci & Technol Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Hamad Bin Khalifa Univ Qatar Comp Res Inst Ar Rayyan Qatar Beijing HaiZhi XingTu Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781450387095

Data-intensive applications dominated by random accesses to large working sets fail to utilize the computing power of modern processors. graph random walk, an indispensable workhorse for many important graph processing and learning applications, is one prominent case of such applications. Existing graph random walk systems are currently unable to match the GPU-side node embedding training speed. This work reveals that existing approaches fail to effectively utilize the modern CPU memory hierarchy, due to the widely held assumption that the inherent randomness in random walks and the skewed nature of graphs render most memory accesses random. We demonstrate that there is actually plenty of spatial and temporal locality to harvest, by careful partitioning, rearranging, and batching of operations. The resulting system, FlashMob, improves both cache and memory bandwidth utilization by making memory accesses more sequential and regular. We also found that a classical combinatorial optimization problem (and its exact pseudo-polynomial solution) can be applied to complex decision making, for accurate yet efficient data/task partitioning. Our comprehensive experiments over diverse graphs show that our system achieves an order of magnitude performance improvement over the fastest existing system. It processes a 58GB real graph at higher per-step speed than the existing system on a 600KB toy graph fitting in the L2 cache.

关键词： graph computing random walk memory cache

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A Novel graph-Based Energy Management System

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IEEE TRANSACTIONS ON SMART GRID 2020年第3期11卷 1845-1853页

作者： Dai, Renchang Liu, Guangyi Wang, Zhiwei Kan, Bowen Yuan, Chen Global Energy Interconnect Res Inst North Amer Graph Comp & Grid Modernizat Grp San Jose CA 95134 USA Global Energy Interconnect Res Inst North Amer San Jose CA 95134 USA

The typical five-minute computational latency of current Energy Management Systems (EMSs) can leave and has left system operators blind to the system state created by events that lead to cascading outages, system separation and/or system collapse. In this paper, we present a graph-computing based EMS that allows the EMS computational cycle time to be reduced to the SCADA sampling cycle, about five seconds. This is achieved in part by taking advantage of both nodal and hierarchical parallelism, to achieve high-speed graph-based state estimation, power flow, and contingency analysis applications. A production-mode case study on an existing large-scale system verifies the advantages and performance of graph computing, demonstrating that the graph-based EMS is more than 20 times faster than a contemporary commercial EMS.

关键词： Energy management Power systems Relational databases Parallel processing Data models Data structures Contingency analysis energy management system graph database graph computing power flow state estimation

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H²Pregel : A partition-based hybrid hierarchical graph computation approach

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 104卷 15-31页

作者： Liu, Qiang Dong, XiaoShe Chen, Heng Zhang, Xingjun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Shaanxi Peoples R China

A partition-based hybrid hierarchical graph computation approach, called H(2)Pregel is proposed to address the redundant supersteps and inefficient computation problems due to low access locality. The H(2)Pregel preprocesses the input graph through a distributed recode algorithm to ensure the continuity and sequence of vertex ids, then employs a hybrid approach to combine the advantages of both synchronous and asynchronous models, and hierarchically computes the high proportion of interior messages generated by high quality partition algorithms. Moreover, H(2)Pregel leverages configurable parallel threads to accelerate local computation by "sub-supersteps", and employs an exterior messages stealing optimization to avoid extra communication overheads between tasks. We implemented H(2)Pregel on Giraph, a classic open source system based on Pregel. The evaluation results on large-scale graphs show that, compared with Pregel in three partition algorithms, H(2)Pregel can achieve average speedups by 1.12-4.52 times and decrease average communication messages by 23.5%-55.5%, and average supersteps by 15.8%-82.0%. (C) 2019 Elsevier B.V. All rights reserved.

关键词： graph computing Hybrid computation BSP model Hierarchical

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A Multi-Objective Genetic Algorithm for detecting dynamic communities using a local search driven immigrant's scheme

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 110卷 960-975页

作者： Panizo-LLedot, Angel Bello-Orgaz, Gema Camacho, David Univ Rey Juan Carlos Comp Sci Dept Madrid Spain Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

The interest in Community Detection Problems on networks that evolves over time has experienced an increasing attention over the last years. Multi-Objective Genetic Algorithms and other bio-inspired methods have been successfully applied to tackle the community finding problem in static networks. Although, there are a large number of evolutionary and bio-inspired approaches that combine Local Search Strategies and other techniques from graph theory to handle the community detection problems in static networks, few research has been done related to the application of these algorithms over temporal, or dynamic, networks. This work is focused on the design, implementation, and the empirical analysis of a new Multi-Objective Genetic Algorithm that combines an Immigrant's scheme with local search strategies for dynamic community detection. The main contribution of this new algorithm is to address the adaptation of these strategies to dynamic networks. On the one hand, the Immigrant's scheme motif is to reuse previously acquired information to reduce computational time. On the other hand, in a dynamic environment is possible that a valid solution became invalid due to some changes in the environment, for example, if some nodes or edges have been removed or added to the network. Therefore, the aim of the local search operator used in the new algorithm is to transform an invalid solution, due to a change happened on the network, into a valid one maintaining the highest possible quality. Finally, the proposed algorithm has been tested using several synthetic and real-world networks, and compared against several algorithms (DYNMOGA, ALPA, Infomap) from the state of the art. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Dynamic community finding Multi-Objective Genetic algorithms graph computing Network analysis

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