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In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP jobs. In this paper, we propose GraphCP, a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. Moreover, GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future iterations. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 20.5x and 8.9x faster than two out-of-core graph processing systems GridGraph and GraphZ, and 3.5x and 1.7x faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.
Keyword ：
concurrent jobs concurrent jobs disk I/O disk I/O graph processing graph processing
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| GB/T 7714 | Xu, Xianghao , Wang, Fang , Jiang, Hong et al. A disk I/O optimized system for concurrent graph processing jobs [J]. | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (3) . |
| MLA | Xu, Xianghao et al. "A disk I/O optimized system for concurrent graph processing jobs" . | FRONTIERS OF COMPUTER SCIENCE 18 . 3 (2024) . |
| APA | Xu, Xianghao , Wang, Fang , Jiang, Hong , Cheng, Yongli , Feng, Dan , Fang, Peng . A disk I/O optimized system for concurrent graph processing jobs . | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (3) . |
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Existing graph systems focus mainly on the execution efficiency of the graph analysis tasks, often ignoring the importance and efficiency of time-evolving graph storage. However, to effectively mine the potential application values, an efficient storage system is important for time-evolving graphs whose storage requirement scales with the increasing number of snapshots. Storage cost and snapshot access speed are the two most important performance indicators for a time-evolving graph storage system, which are challenging for designers of such systems because they are conflicting goals. In this article, we address these challenges by proposing an efficient storage scheme for the large time-evolving graphs. We first design a Snapshot-level Data Deduplication (SLDD) strategy to eliminate the large number of repeated vertices and edges among the snapshots, and then a Structure-Changing Graph Representation (SCGR) to significantly improve the snapshot access speed. We implement an efficient time-evolving graph storage system, TgStore, based on this scheme to effectively store large-scale time-evolving graphs, aiming to efficiently support the time-evolving graph analysis tasks. Experimental results show that TgStore can obtain a high compression ratio of 43.03:1 when storing 100 snapshots of Twitter, while with an average snapshot access speedup of 16x. Efficient storage scheme enables TgStore to efficiently support time-evolving graph algorithms. For example, when executing the Pagerank algorithm on the time-evolving graph of Twitter, TgStore outperforms Graphone, a state-of-the-art time-evolving graph storage system, by 15.9x in algorithm execution speed and 1.45x in memory usage.
Keyword ：
Big Data Big Data Blogs Blogs Costs Costs data deduplication data deduplication data representation data representation Market research Market research Pandemics Pandemics Social networking (online) Social networking (online) storage system storage system Task analysis Task analysis Time-evolving graph Time-evolving graph
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| GB/T 7714 | Cheng, Yongli , Ma, Yan , Jiang, Hong et al. TgStore: An Efficient Storage System for Large Time-Evolving Graphs [J]. | IEEE TRANSACTIONS ON BIG DATA , 2024 , 10 (2) : 158-173 . |
| MLA | Cheng, Yongli et al. "TgStore: An Efficient Storage System for Large Time-Evolving Graphs" . | IEEE TRANSACTIONS ON BIG DATA 10 . 2 (2024) : 158-173 . |
| APA | Cheng, Yongli , Ma, Yan , Jiang, Hong , Zeng, Lingfang , Wang, Fang , Xu, Xianghao et al. TgStore: An Efficient Storage System for Large Time-Evolving Graphs . | IEEE TRANSACTIONS ON BIG DATA , 2024 , 10 (2) , 158-173 . |
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Many applications need to execute Single-Source Shortest Paths (SSSP) algorithm on each snapshot of a time evolving graph, leading to long waiting times experienced by the users of such applications. However, these applications are often time-sensitive, the delayed computation results can lead to the loss of best decision-making opportunities. To address this problem, in this paper we propose an efficient SSSP algorithm for time-evolving graphs, called V-Grouper. The main idea of V-Grouper is to avoid the redundant computations of the same vertex in different snapshots. Our experimental results over real-world time-evolving graphs show that, due to the high similarity of consecutive snapshots, the computation results of one vertex in neighboring snapshots are equal with a high probability. At the beginning of computation, V-Grouper first divides all the versions of a given vertex in different snapshots into vertex groups, where the computation result of each version is predicted based on the aforementioned insight of neighboring snapshots having equal results. The versions of the vertex in each group have the same predicted computation result. During the computation process for each vertex group, only one version needs to participate in computation, avoiding a large number of redundant computations. Experimental results show that V-Grouper is up to 64.31x faster than the state-of-the-art SSSP algorithm.
Keyword ：
Grouper Grouper Predicted computation results Predicted computation results SSSP SSSP Time-evolving graph Time-evolving graph
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| GB/T 7714 | Cheng, Yongli , Huang, Chuanjie , Jiang, Hong et al. An efficient SSSP algorithm on time-evolving graphs with prediction of computation results [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 186 . |
| MLA | Cheng, Yongli et al. "An efficient SSSP algorithm on time-evolving graphs with prediction of computation results" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 186 (2023) . |
| APA | Cheng, Yongli , Huang, Chuanjie , Jiang, Hong , Xu, Xianghao , Wang, Fang . An efficient SSSP algorithm on time-evolving graphs with prediction of computation results . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 186 . |
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Big data applications increasingly rely on the analysis of large graphs. In recent years, a number of out-of-core graph processing systems have been proposed to process graphs with billions of edges on just one commodity computer, by efficiently using the secondary storage (e.g., hard disk, SSD). Unfortunately, these graph processing systems continue to suffer from poor performance, despite of many solutions proposed to address the disk I/O bottleneck problem, a commonly recognized root cause. However, our experimental results show that another root cause of the poor performance is the subgraph construction phase of graph processing, which induces a large number of random memory accesses that substantially weaken cache access locality and thus greatly degrade the performance. In this paper, we propose an efficient out-of-core graph processing system, LOSC, to substantially reduce the overheads of subgraph construction. LOSC proposes a locality-optimized subgraph construction scheme that significantly improves the in-memory data access locality of the subgraph construction phase. Furthermore, LOSC adopts a compact edge storage format and a lightweight replication of vertices to reduce I/O traffic and improve computation efficiency. Extensive evaluation results show that LOSC is respectively 9.4x and 5.1x faster than GraphChi and GridGraph, two representative out-of-core systems. In addition, LOSC outperforms other state-of-art out-of-core graph processing systems including FlashGraph, GraphZ, G-Store and NXGraph. For example, LOSC can be up to 6.9x faster than FlashGraph.(c) 2022 Elsevier Inc. All rights reserved.
Keyword ：
Graph processing Graph processing Out-of-core Out-of-core Subgraph construction Subgraph construction
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| GB/T 7714 | Xu, Xianghao , Wang, Fang , Jiang, Hong et al. LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 172 : 51-68 . |
| MLA | Xu, Xianghao et al. "LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 172 (2023) : 51-68 . |
| APA | Xu, Xianghao , Wang, Fang , Jiang, Hong , Cheng, Yongli , Hua, Yu , Feng, Dan et al. LOSC: A locality-optimized subgraph construction scheme for out-of-core graph processing . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2023 , 172 , 51-68 . |
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Abstract ：
时变图连通分量已经被广泛应用到不同场景,?如交通路网建设、推荐系统的信息推送等.?然而当前多数连通分量求解方法忽视了NUMA体系结构对计算效率产生的影响,?即过高的远程内存访问延迟导致低下的算法执行效率.?本文针对时变图的弱连通分量求解问题,?提出一种基于NUMA延迟发送的时变图弱连通分量求解方法,?它通过合理的数据内存布局,?合理控制NUMA节点间的信息交换次数,?最大限度减少远程内存访问数量,?显著提高了算法执行效率.?实验结果表明,?该方法的性能明显优于当前流行的图处理系统Ligra和Polymer提供的方法.
Keyword ：
NUMA NUMA 图计算 图计算 延迟发送 延迟发送 弱连通分量 弱连通分量 时变图 时变图
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| GB/T 7714 | 梁锐杰 , 程永利 . 基于NUMA延迟发送的时变图弱连通分量求解 [J]. | 计算机系统应用 , 2023 , 32 (3) : 322-329 . |
| MLA | 梁锐杰 et al. "基于NUMA延迟发送的时变图弱连通分量求解" . | 计算机系统应用 32 . 3 (2023) : 322-329 . |
| APA | 梁锐杰 , 程永利 . 基于NUMA延迟发送的时变图弱连通分量求解 . | 计算机系统应用 , 2023 , 32 (3) , 322-329 . |
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为了解决高校存在大数据高层次创新人才培养与产业需求脱节的问题,通过对大数据产业专班研究生联合培养示范基地建设进行探索,完善现有的基地建设方案、持续稳定扩大专班规模、深化培养模式改革,为培育符合大数据产业需求的创新型人才提供思路.福州大学通过多年的探索与实践,为企业培养了一大批具有大数据产业创新能力的高层次人才,实践经验可为高校开设相关产业研究生联合培养基地建设提供参考.
Keyword ：
大数据 大数据 研究生培养 研究生培养 示范基地 示范基地
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| GB/T 7714 | 郭文忠 , 陈星 , 程永利 . 大数据产业专班研究生联合培养示范基地建设探索与实践 ——以福州大学为例 [J]. | 科技创业月刊 , 2023 , 36 (4) : 153-156 . |
| MLA | 郭文忠 et al. "大数据产业专班研究生联合培养示范基地建设探索与实践 ——以福州大学为例" . | 科技创业月刊 36 . 4 (2023) : 153-156 . |
| APA | 郭文忠 , 陈星 , 程永利 . 大数据产业专班研究生联合培养示范基地建设探索与实践 ——以福州大学为例 . | 科技创业月刊 , 2023 , 36 (4) , 153-156 . |
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With the rapid development of manipulation technologies, the generation of Deep Fake videos is more accessible than ever. As a result, face forgery detection becomes a challenging task, attracting a significant amount of attention from researchers worldwide. However, most previous work, consisting of convolutional neural networks (CNN), is not sufficiently discriminative and cannot fully utilise subtle clues and similar textures during the process of facial forgery detection. Moreover, these methods cannot simultaneously consider accuracy and time efficiency. To address such problems, we propose a novel framework named FPC-Net to extract some meaningful and unnatural expressions in local regions. This framework utilises CNN, long short-term memory (LSTM), channel groups loss (CG-Loss) and adaptive feature fusion to detect face forgery videos. First, the proposed method exploits spatial features by CNN, and a channel-wise attention mechanism is employed to separate channels. Specifically, with the help of channel groups loss, the channels are divided into two groups, each representing a specific class. Second, LSTM is applied to learn the correlation of spatial features. Finally, the correlation of features is mapped into other latent spaces. Through a lot of experiments, the results are that the detection speed of the proposed method reaches 420 FPS and the auc scores achieve best performance of 99.7%, 99.9%, 94.7%, and 82.0% on Raw Celeb-DF, Raw Face Forensics++, F2F and NT datasets respectively. The experimental results demonstrate that the proposed framework has great time efficiency performance while improving the detection performance compared with other frame-level methods in most cases.
Keyword ：
adaptive feature fusion adaptive feature fusion facial forgery detection facial forgery detection
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| GB/T 7714 | Wu, Bin , Su, Lichao , Chen, Dan et al. FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss [J]. | IET COMPUTER VISION , 2022 , 17 (3) : 330-340 . |
| MLA | Wu, Bin et al. "FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss" . | IET COMPUTER VISION 17 . 3 (2022) : 330-340 . |
| APA | Wu, Bin , Su, Lichao , Chen, Dan , Cheng, Yongli . FPC-Net: Learning to detect face forgery by adaptive feature fusion of patch correlation with CG-Loss . | IET COMPUTER VISION , 2022 , 17 (3) , 330-340 . |
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In recent years, system researchers have proposed many out-of-core graph processing systems to efficiently handle graphs that exceed the memory capacity of a single machine. Through disk-friendly graph data organizations and well-designed execution engines, existing out-of-core graph processing systems can maintain sequential locality on disk access and greatly reduce disk I/Os during processing. However, they have not fully explored the characteristics of graph data and algorithm execution to further reduce disk I/Os, leaving significant room for performance improvement. In this paper, we present a novel out-of-core graph processing system called GraphSD, which optimizes the I/O traffic by simultaneously capturing the state and dependency of graph data during computation. At the heart of GraphSD is a state- and dependency-aware update strategy that includes two adaptive update models, selective cross-iteration update (SCIU) and full cross-iteration update (FCIU). These two update models are dynamically triggered at runtime to enable active-vertex aware processing and cross-iteration vertex value computation, which avoid loading inactive edges and reduce disk I/Os in the future iterations. Moreover, an efficient sub-block based buffering scheme is proposed to further minimize I/O overheads. Our evaluation results show that GraphSD outperforms two state-of-the-art out-of-core graph processing systems HUS-Graph and Lumos by up to 2.7 × and 3.9 × respectively. © 2022 ACM.
Keyword ：
Iterative methods Iterative methods Scheduling algorithms Scheduling algorithms
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| GB/T 7714 | Xu, Xianghao , Jiang, Hong , Wang, Fang et al. GraphSD: A State and Dependency aware Out-of-Core Graph Processing System [C] . 2022 . |
| MLA | Xu, Xianghao et al. "GraphSD: A State and Dependency aware Out-of-Core Graph Processing System" . (2022) . |
| APA | Xu, Xianghao , Jiang, Hong , Wang, Fang , Cheng, Yongli , Fang, Peng . GraphSD: A State and Dependency aware Out-of-Core Graph Processing System . (2022) . |
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Shared-memory graph processing is usually more efficient than in a cluster in terms of cost effectiveness, ease of programming and runtime. However, the limited memory capacity of a single machine and the huge sizes of graphs restrains its applicability. Hence, it is imperative to reduce memory footprint. We observe that index compression holds promise and propose CIC-PIM, a lightweight encoding with chunked index compression, to reduce the memory footprint and the runtime of graph algorithms. CIC-PIM aims for significant space saving, real random-access support and high cache efficiency by exploiting the ubiquitous power-law and sparseness features of large scale graphs. The basic idea is to divide index structures into chunks of appropriate size and compress the chunks with our lightweight fixed-length byte-aligned encoding. After CIC-PIM compression, two-fold larger graphs are processed with all data fit in memory, resulting in speedups or fast in-memory processing unattainable previously. (C) 2020 Elsevier Inc. All rights reserved.
Keyword ：
Graph processing Graph processing Index compression Index compression Parallel processing Parallel processing Shared-memory Shared-memory
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| GB/T 7714 | Zhang, Yongxuan , Jiang, Hong , Wang, Fang et al. CIC-PIM: Trading spare computing power for memory space in graph processing [J]. | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2021 , 147 : 152-165 . |
| MLA | Zhang, Yongxuan et al. "CIC-PIM: Trading spare computing power for memory space in graph processing" . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 147 (2021) : 152-165 . |
| APA | Zhang, Yongxuan , Jiang, Hong , Wang, Fang , Hua, Yu , Feng, Dan , Cheng, Yongli et al. CIC-PIM: Trading spare computing power for memory space in graph processing . | JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING , 2021 , 147 , 152-165 . |
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Big data applications increasingly rely on the analysis of large graphs. In order to analyze and process the large graphs with high cost efficiency, researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity computer. On the other hand, with the rapidly growing need of analyzing graphs in the real-world, graph processing systems have to efficiently handle massive concurrent graph processing (CGP) jobs. Unfortunately, due to the inherent design for single graph processing job, existing out-of-core graph processing systems usually incur redundant data accesses and storage and severe competition of I/O bandwidth when handling the CGP jobs, thus leading to very long waiting time experienced by users for the computing results. In this paper, we propose an I/O-efficient out-of-core graph processing system, GraphCP, to support the processing of CGP jobs. GraphCP proposes a benefit-aware sharing execution model that shares the I/O access and processing of graph data among the CGP jobs and adaptively schedules the loading of graph data, which efficiently overcomes above challenges faced by existing out-of-core graph processing systems. In addition, GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access locality. Extensive evaluation results show that GraphCP is 10.3x and 4.6x faster than two state-of-the-art out-of-core graph processing systems GridGraph and GraphZ respectively, and 2.1x faster than a CGP-oriented graph processing system Seraph.
Keyword ：
concurrent processing concurrent processing graph processing graph processing I/O I/O
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| GB/T 7714 | Xu, Xianghao , Wang, Fang , Jiang, Hong et al. GraphCP: An I/O-Efficient Concurrent Graph Processing Framework [C] . 2021 . |
| MLA | Xu, Xianghao et al. "GraphCP: An I/O-Efficient Concurrent Graph Processing Framework" . (2021) . |
| APA | Xu, Xianghao , Wang, Fang , Jiang, Hong , Cheng, Yongli , Feng, Dan , Zhang, Yongxuan et al. GraphCP: An I/O-Efficient Concurrent Graph Processing Framework . (2021) . |
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