IEEE Transactions on Parallel and Distributed Systems最新文献

{"title":"Chorus: Robust Multitasking Local Client-Server Collaborative Inference With Wi-Fi 6 for AIoT Against Stochastic Congestion Delay","authors":"Yuzhe Luo;Ji Qi;Ling Li;Ruizhi Chen;Xiaoyu Wu;Limin Cheng;Chen Zhao","doi":"10.1109/TPDS.2025.3619775","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3619775","url":null,"abstract":"The rapid growth of AIoT devices brings huge demands for DNNs deployed on resource-constrained devices. However, the intensive computation and high memory footprint of DNN inference make it difficult for the AIoT devices to execute the inference tasks efficiently. In many widely deployed AIoT use cases, multiple local AIoT devices launch DNN inference tasks randomly. Although local collaborative inference has been proposed to accelerate DNN inference on local devices with limited resources, multitasking local collaborative inference, which is common in AIoT scenarios, has not been fully studied in previous works. We consider multitasking local client-server collaborative inference (MLCCI), which achieves efficient DNN inference by offloading the inference tasks from multiple AIoT devices to a more powerful local server with parallel pipelined execution streams through Wi-Fi 6. Our optimization goal is to minimize the mean end-to-end latency of MLCCI. Based on the experiment results, we identify three key challenges: high communication costs, high model initialization latency, and congestion delay brought by task interference. We analyze congestion delay in MLCCI and its stochastic fluctuations with queuing theory and propose Chorus, a high-performance adaptive MLCCI framework for AIoT devices, to minimize the mean end-to-end latency of MLCCI against stochastic congestion delay. Chorus generates communication-efficient model partitions with heuristic search, uses a prefetch-enabled two-level LRU cache to accelerate model initialization on the server, reduces congestion delay and its short-term fluctuations with execution stream allocation based on the cross-entropy method, and finally achieves efficient computation offloading with reinforcement learning. We established a system prototype, which statistically simulated many virtual clients with limited physical client devices to conduct performance evaluations, for Chorus with real devices. The evaluation results for various workload levels show that Chorus achieved an average of <inline-formula><tex-math>$1.4times$</tex-math></inline-formula>, <inline-formula><tex-math>$1.3times$</tex-math></inline-formula>, and <inline-formula><tex-math>$2times$</tex-math></inline-formula> speedup over client-only inference, and server-only inference with LRU and MLSH, respectively.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2706-2723"},"PeriodicalIF":6.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cache Partition Management for Improving Fairness and I/O Responsiveness in NVMe SSDs","authors":"Jiaojiao Wu;Fan Yang;Zhibing Sha;Li Cai;Zhigang Cai;Balazs Gerofi;Yuanquan Shi;Jianwei Liao","doi":"10.1109/TPDS.2025.3619866","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3619866","url":null,"abstract":"NVMe SSDs have become mainstream storage devices thanks to their compact size and ultra-low latency. It has been observed that the impact of interference among all concurrently running streams (i.e., I/O workloads) on their overall responsiveness differs significantly, thus leading to unfairness. The intensity and access locality of streams are the primary factors contributing to interference. A small-sized data cache is commonly equipped in the front-end of SSDs to improve I/O performance and extend the device’s lifetime. The degree of parallelism at this level, however, is limited compared to that of the SSD back end, which consists of multiple channels, chips, and planes. Therefore, the impact of interference can be more significant at the data cache level. In this paper, we propose a cache division management scheme that not only contributes to fairness but also boosts I/O responsiveness across all workloads in NVMe SSDs. Specifically, our proposal supports long-term data cache partitioning and short-term cache adjustment with global sharing, ensuring better fairness and further enhancing cache utilization efficiency in multi-stream scenarios. Trace-driven simulation experiments show that our proposal improves fairness by an average of <monospace>66.0</monospace>% and reduces overall I/O response time by between <monospace>3.8</monospace>% and <monospace>18.0</monospace>%, compared to existing cache management schemes for NVMe SSDs.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"37 1","pages":"122-136"},"PeriodicalIF":6.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145546986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Popularity-Aware Data Placement in Erasure Coding-Based Edge Storage Systems","authors":"Ruikun Luo;Jiadong Zhao;Qiang He;Feifei Chen;Song Wu;Hai Jin;Yun Yang","doi":"10.1109/TPDS.2025.3619273","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3619273","url":null,"abstract":"Edge computing enables low-latency data access by caching popular content on edge servers. However, server unavailability at runtime can increase retrieval latency when requests are redirected to the cloud. To enhance availability, <i>erasure coding</i> (EC) has been employed to ensure full data access for all users in an edge storage system (ESS). Existing approaches for edge data placement place coded blocks across the entire system without considering data popularity. As a result, they often suffer from high data retrieval latency. In addition, they are designed to process data items individually. Data placed earlier will limit the placement options for subsequent files because edge servers with the most neighbors in the system can be easily exhausted. Some files cannot be placed properly to accommodate user demands. This increases users’ data retrieval latency further. This paper investigates the <i>edge data placement</i> (EDP) problem with popularity awareness. We formulate EDP as a mixed-integer programming problem and prove its <inline-formula><tex-math>$mathcal{NP}$</tex-math></inline-formula>-hardness. We then design an exact algorithm (EDP-O) that decomposes the problem into three convex subproblems and solves it iteratively, and an approximation algorithm (EDP-A) with a guaranteed <inline-formula><tex-math>$ln N$</tex-math></inline-formula> approximation ratio for large-scale systems. Experiments on real-world datasets show that EDP-O and EDP-A reduce average retrieval latency by 18.4% and 15.6% in small-scale settings, while EDP-A achieves 54.7% latency reduction and 34.9% lower discard rate in large-scale scenarios compared to four baselines.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2733-2746"},"PeriodicalIF":6.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11197023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSpMM: Efficiently Scalable SpMM Kernels Across Multiple Generations of Tensor Cores","authors":"Zeyu Xue;Mei Wen;Jianchao Yang;Minjin Tang;Zhongdi Luo;Jing Feng;Yang Shi;Zhaoyun Chen;Junzhong Shen;Johannes Langguth","doi":"10.1109/TPDS.2025.3616981","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3616981","url":null,"abstract":"Sparse-Dense Matrix-Matrix Multiplication (SpMM) has emerged as a foundational primitive in HPC and AI. Recent advancements have aimed to accelerate SpMM by harnessing the powerful Tensor Cores found in modern GPUs. However, despite these efforts, existing methods frequently encounter performance degradation when ported across different Tensor Core architectures. Recognizing that scalable SpMM across multiple generations of Tensor Cores relies on the effective use of general-purpose instructions, we have meticulously developed a SpMM library named <italic>SSpMM</i>. However, a significant conflict exists between granularity and performance in current Tensor Core instructions. To resolve this, we introduce the innovative <italic>Transpose Mapping Scheme</i>, which elegantly implements fine-grained kernels using coarse-grained instructions. Additionally, we propose the <italic>Register Shuffle Method</i> to further enhance performance. Finally, we introduce <italic>Sparse Vector Compression</i>, a technique that ensures our kernels are scalable with both structured and unstructured sparsity. Our experimental results, conducted on four generations of Tensor Core GPUs using over 3,000 sparse matrices from well-established matrix collections, demonstrate that <italic>SSpMM</i> achieves an average speedup of 2.04 ×, 2.81 ×, 2.07 ×, and 1.87 ×, respectively, over the state-of-the-art SpMM solution. Furthermore, we have integrated <italic>SSpMM</i> into PyTorch, achieving a 1.81 × speedup in end-to-end Transformer inference compared to <italic>cuDNN</i>.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2652-2667"},"PeriodicalIF":6.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GAP-DCCS: A Generic Acceleration Paradigm for Data-Intensive Applications With Efficient Data Compression and Caching Strategy Over CPU-GPU Clusters","authors":"Jiangwei Xiao;Yingzhe Bai;Hanfei Diao;Guofeng Liu;Yuzhu Wang","doi":"10.1109/TPDS.2025.3615283","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3615283","url":null,"abstract":"Seismic exploration is a geophysical method used for imaging subsurface structures, capable of providing high-resolution images of the underground. In seismic data processing, Kirchhoff Pre-Stack Depth Migration (KPSDM) serves as one of the key techniques, playing a critical role in significantly enhancing the lateral resolution of imaging and providing accurate characterization of subsurface media. However, with the continuous growth in high-density seismic data volumes, the computational efficiency of KPSDM is primarily constrained by substantial computational loads, end-to-end I/O bottlenecks, and data storage pressures. To address the performance optimization challenges of computation-intensive applications that require frequent large-scale data transfers between the host and accelerator devices, this paper proposes GAP-DCCS, a GPU-based Generic Acceleration Paradigm with efficient Data Compression and Caching Strategy, which includes the following core strategies: (1) For compute-intensive modules, a GPU-based three-dimensional parallel acceleration is implemented, combined with memory access optimization techniques and overlapping strategies for data transfer and computation, to improve GPU resource utilization; (2) To alleviate the storage pressure of large-scale datasets, the BitComp compression algorithm is introduced to efficiently compress task data while maintaining output stability, significantly reducing storage requirements and end-to-end data transfer volume; (3) To tackle the I/O bottleneck caused by frequent large-scale data transfers between the host and devices, an adaptive dynamic caching data management mechanism is designed to effectively increase data reuse rates and markedly reduce end-to-end transfer frequency. Experimental results demonstrate that the proposed optimization method significantly enhances the computational performance of KPSDM, achieving a speedup of 123.51× on a single NVIDIA Tesla A800 GPU compared to a 16-core CPU. This optimization paradigm has not only been effectively validated in KPSDM but also offers a referable high-performance computing solution for other large-scale data processing tasks.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2747-2762"},"PeriodicalIF":6.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Art of the Fugue: Minimizing Interleaving in Collaborative Text Editing","authors":"Matthew Weidner;Martin Kleppmann","doi":"10.1109/TPDS.2025.3611880","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3611880","url":null,"abstract":"Most existing algorithms for replicated lists, which are widely used in collaborative text editors, suffer from a problem: when two users concurrently insert text at the same position in the document, the merged outcome may interleave the inserted text passages, resulting in corrupted and potentially unreadable text. The problem has gone unnoticed for decades, and it affects both CRDTs and Operational Transformation. This paper defines maximal non-interleaving, our new correctness property for replicated lists. We introduce two related CRDT algorithms, Fugue and FugueMax, and prove that FugueMax satisfies maximal non-interleaving. We also implement our algorithms and demonstrate that Fugue offers performance comparable to state-of-the-art CRDT libraries for text editing.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 11","pages":"2425-2437"},"PeriodicalIF":6.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atomic Smart Contract Interoperability With High Efficiency via Cross-Chain Integrated Execution","authors":"Chaoyue Yin;Mingzhe Li;Jin Zhang;You Lin;Qingsong Wei;Siow Mong Rick Goh","doi":"10.1109/TPDS.2025.3614374","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3614374","url":null,"abstract":"With the development of Ethereum, numerous blockchains compatible with Ethereum’s execution environment (i.e., Ethereum Virtual Machine, EVM) have emerged. Developers can leverage smart contracts to run various complex decentralized applications on top of blockchains. However, the increasing number of EVM-compatible blockchains has introduced significant challenges in cross-chain interoperability, particularly in ensuring efficiency and atomicity for the whole cross-chain application. Existing solutions are <italic>either limited in guaranteeing overall atomicity for the cross-chain application, or inefficient due to the need for multiple rounds of cross-chain smart contract execution.</i> To address this gap, we propose <monospace>IntegrateX</monospace>, an efficient cross-chain interoperability system that ensures the overall atomicity of cross-chain smart contract invocations. The core idea is to <italic>deploy the logic required for cross-chain execution onto a single blockchain, where it can be executed in an integrated manner.</i> This allows cross-chain applications to perform all cross-chain logic efficiently within the same blockchain. <monospace>IntegrateX</monospace> consists of a <italic>cross-chain smart contract deployment protocol</i> and a <italic>cross-chain smart contract integrated execution protocol.</i> The former achieves efficient and secure cross-chain deployment by decoupling smart contract logic from state, and employing an off-chain cross-chain deployment mechanism combined with on-chain cross-chain verification. The latter ensures atomicity of cross-chain invocations through a 2PC-based mechanism, and enhances performance through transaction aggregation and fine-grained state lock. We implement a prototype of <monospace>IntegrateX</monospace>. Extensive experiments demonstrate that it reduces up to 61.2% latency compared to the state-of-the-art baseline while maintaining low gas consumption.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2635-2651"},"PeriodicalIF":6.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Megapixel Approach for Efficient Execution of Irregular Wavefront Algorithms on GPUs","authors":"Mathias Oliveira;Willian Barreiros;Renato Ferreira;Alba C. M. A. Melo;George Teodoro","doi":"10.1109/TPDS.2025.3612696","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3612696","url":null,"abstract":"Morphological operations are critical in high-resolution biomedical image processing. Their efficient execution relies on an irregular flood-filling strategy consolidated in the Irregular Wavefront Propagation Pattern (IWPP). IWPP was designed for GPUs and achieved significant gains compared to previous work. Here, however, we have revisited IWPP to identify the key limitations of its GPU implementation and proposed a novel more efficient strategy. In particular, the IWPP most demanding phase consists of tracking active pixels, those contributing to the output, that are the ones processed during the execution. This computational strategy leads to irregular memory access, divergent execution, and high storage (queue) management costs. To address these aspects, we have proposed the novel execution strategy called Irregular Wavefront Megapixel Propagation Pattern (IWMPP). IWMPP introduces a coarse-grained execution approach based on fixed-size square regions (instead of pixels in IWPP), referred to as megapixels (MPs). This design reduces the number of elements tracked and enables a regular processing within MPs that, in turn, improves thread divergence and memory accesses. IWMPP introduces optimizations, such as Duplicate Megapixel Removal (DMR) to avoid MPs recomputation and Tiled-Ordered (TO) execution that enforces a semistructured MPs execution sequence to improve data propagation efficiency. Experimental results using large tissue cancer images demonstrated that the IWMPP GPU attains significant gains over the state-of-the-art (IWPP). For morphological reconstruction, fill holes, and h-maxima operations, on the RTX 4090, the IWMPP GPU is up to 17.9×, 45.6×, and 14.9× faster than IWPP GPU, respectively, while at the same time reducing memory demands. IWMPP is an important step to enable quick processing of large imaging datasets.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 11","pages":"2399-2411"},"PeriodicalIF":6.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176841","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Data Locality by Integrating Intermediate Data Partitioning and Reduce Task Scheduling in Spark Framework","authors":"Mengsi He;Zhongming Fu;Zhuo Tang","doi":"10.1109/TPDS.2025.3611388","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3611388","url":null,"abstract":"Data locality is crucial for distributed computing systems (e.g., Spark and Hadoop), which is the main factor considered in the task scheduling. Simultaneously, the effects of data locality on reduce tasks are determined by the intermediate data partitioning. While suffering from the problem of data skew, the existing intermediate data partitioning methods only achieves load balancing for reduce tasks. To address the problem, this paper optimizes the data locality for reduce tasks by integrating intermediate data partitioning and task scheduling in Spark framework. First, it presents a distribution skew model to divide the key clusters into skewed and non-skewed distribution. Then, a data locality and load balancing-aware intermediate data partitioning method is proposed, where a priority allocation strategy for the key clusters with skewed distribution is presented, and a balanced allocation strategy for the key clusters with non-skewed distribution is presented. Finally, it proposes a data locality-aware reduce task scheduling algorithm, where an online self-adaptive NARX (nonlinear autoregressive with external input) model is developed to predict the idle time of node. It can ensure that the delayed scheduling decision made can complete the data transmission of reduce tasks earlier. We implement our proposals in Spark-3.5.1 and evaluate the performance using several representative benchmarks. Experimental results indicate that the proposed method and algorithm can reduce the job/application running time by approximately 4% to 46% and decrease the total volume of data transmission by approximately 8% to 54%.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 11","pages":"2383-2398"},"PeriodicalIF":6.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FedSR: A Semi-Decentralized Federated Learning Framework for Non-IID Data Based on Incremental Subgradient Optimization","authors":"Jianjun Huang;Hao Huang;Li Kang;Lixin Ye","doi":"10.1109/TPDS.2025.3611304","DOIUrl":"https://doi.org/10.1109/TPDS.2025.3611304","url":null,"abstract":"In the Industrial Internet of Things (IoT), data heterogeneity across different devices poses a huge challenge to federated learning techniques, significantly reducing the performance of federated learning models. Additionally, the large number of devices participating in IoT federated learning and training imposes a substantial computational burden on cloud servers. Current federated learning research primarily adopts centralized or discentralized learning architectures, which cannot fundamentally solve these issues. To address this, we propose a novel semi-centralized cloud-edge-device hierarchical federate learning framework that integrated both centralized and decentralized federated learning approaches. Specifically, only a subset of adjacent devices forms small-scale ring clusters, and the cloud server aggregates the ring models to construct a global model. To mitigate the impact of data heterogeneity across devices, we use an incremental subgradient optimization algorithm within each ring cluster to enhance the generalization ability of the ring cluster models. Extensive experiments demonstrate that our approach effectively reduces the impact of data heterogeneity, improves model performance, and significantly alleviates the communication burden on cloud servers compared to centralized and discentralized federated learning frameworks. Indeed, the framework proposed in this paper aims to balance the strengths of centralized federated learning and ring federated learning. It achieves superior performance in addressing the data non-IID problem compared to centralized federated learning architectures while also mitigating issues associated with excessively large rings in ring architectures.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"36 12","pages":"2693-2705"},"PeriodicalIF":6.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}