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    • SDAA: Runtime System for Shenwei AI Acceleration Card

      Online: March 27,2024 DOI: 10.13328/j.cnki.jos.007084

      Abstract (6) HTML (0) PDF 9.10 M (23) Comment (0) Favorites

      Abstract:The homegrown Shenwei AI acceleration card is equipped with the Shenwei many-core processor based on systolic array enhancement, and although its intelligent computing power can be comparable to the mainstream GPU, there is still a lack of basic software support. To lower the utilization threshold of the Shenwei AI acceleration card and effectively support the development of AI applications, this study designs a runtime system SDAA for the Shenwei AI acceleration card, whose semantics is consistent with the mainstream CUDA. For key paths such as memory management, data transmission, and kernel function launch, the software and hardware co-design method is adopted to realize the multi-level memory allocation algorithm with segment and paged memory combined on the card, pageable memory transmission model of multiple threads and channels, adaptive data transmission algorithm with multi-heterogeneous components, and fast kernel function launch method based on on-chip array communication. As a result, the runtime performance of SDAA is better than that of the mainstream GPU. The experimental results indicate that the memory allocation speed of SDAA is 120 times the corresponding interface of NVIDIA V100, the memory transmission overhead is 1/2 of the corresponding interface, and the data transmission bandwidth is 1.7 times the corresponding interface. Additionally, the launch time of the kernel function is equivalent to the corresponding interface, and thus the SDAA runtime system can support the efficient operation of mainstream frameworks and actual model training on the Shenwei AI acceleration card.

    • Projection-based Requirements Analysis Approach for Embedded Systems

      Online: March 27,2024 DOI: 10.13328/j.cnki.jos.007081

      Abstract (9) HTML (0) PDF 7.44 M (33) Comment (0) Favorites

      Abstract:Embedded systems are becoming increasingly complex, and the requirements analysis of their software systems has become a bottleneck in embedded system development. Device dependency and interleaving execution logic are typical characteristics of embedded software systems, necessitating effective requirement analysis methods to decouple the requirements based on device dependencies. Starting from the idea of environment-based modeling in requirement engineering, this study proposes a projection-based requirement analysis approach from system requirements to software requirements for embedded software systems, helping requirement engineers to effectively decouple the requirements. The study first summarizes the system requirement and software requirement descriptions of embedded software systems, defines the requirement decoupling strategies of embedded software systems based on interactive environment characteristics, and designs the specification process from system requirements to software requirements. A real case study is carried out in the spacecraft sun search system, and five representative case scenarios are quantitatively evaluated through two metrics of coupling and cohesion, which demonstrate the effectiveness of the proposed approach.

    • RobustSketch: Elastic Method for Elephant Flow Identification Supporting Network Traffic Jitters

      Online: March 27,2024 DOI: 10.13328/j.cnki.jos.007090

      Abstract (7) HTML (0) PDF 7.02 M (27) Comment (0) Favorites

      Abstract:Elephant flow identification is a fundamental task in network measurements. Currently, the mainstream methods generally employ sketch data structure Sketch to quickly count network traffic and efficiently find elephant flows. However, the rapid influx of numerous packets will significantly decrease the identification accuracy of elephant flows under network traffic jitters. To this end, this study proposes an elastic identification method for elephant flows supporting network traffic jitters, which is named RobustSketch. This method first designs a stretchable mice flow filter based on the cyclic Sketch chain, and adaptively increases and reduces the number of Sketch in real-time packet arrival rates. As a result, it always completely records all arrived packets within the current period to ensure accurate mice flow filtering even under network traffic jitters. Subsequently, this study designs a scalable elephant flow record table based on dynamic segmented hashing, which adaptively increases and reduces segments according to the number of candidate elephant flows filtered out by the mice flow filter. Finally, this can fully record all candidate elephant flows and keep high storage space utilization. Furthermore, the error bounds of the proposed mice flow filter and elephant flow recording table are provided by theoretical analysis. Finally, experimental evaluation is conducted on the proposed elephant flow identification method RobustSketch with real network traffic samples. Experimental results indicate that the identification accuracy of elephant flows of the proposed method is significantly higher than that of the existing methods, and can stably keep high accuracy of over 99% even under network traffic jitters. Meanwhile, its average relative error is reduced by more than 2.7 times, which enhances the accuracy and robustness of elephant flow identification.

    • Intra-domain Routing Protection Algorithm Based on Shortest Path Serialization Graph

      Online: March 27,2024 DOI: 10.13328/j.cnki.jos.007091

      Abstract (17) HTML (0) PDF 2.22 M (26) Comment (0) Favorites

      Abstract:Internet service providers employ routing protection algorithms to meet real-time, low-latency, and high-availability application needs. However, existing routing protection algorithms have the following three problems. (1) The failure protection ratio is generally low under the premise of not changing the traditional routing protocol forwarding mechanism. (2) The traditional routing protocol forwarding mechanism should be changed to pursue a high failure protection ratio, which is difficult to deploy in practice. (3) The optimal next hop and backup next hop cannot be utilized simultaneously, which causes poor network load balancing capability. For the three problems, this study proposes a routing protection algorithm based on the shortest path serialization graph, which does not need to change the forwarding mechanism, supports incremental deployment and adopts both optimal next hop and backup next hop without routing loops, with a high failure protection ratio. The proposed algorithm mainly includes the following two steps. (1) A sequence number for each node is calculated, and the shortest path sequencing graph is generated. (2) The shortest path serialization graph is generated based on the node sequence number and reverse order search rules, and the next hop set between node pairs is calculated according to the backup next hop calculation rules. Tests on real and simulated network topologies show that the proposed scheme has significant advantages over other routing protection schemes in the average number of backup next hops, failure protection ratio, and path stretch.

    • Client Selection Algorithm in Cross-device Federated Learning

      Online: March 20,2024 DOI: 10.13328/j.cnki.jos.007085

      Abstract (31) HTML (0) PDF 3.88 M (68) Comment (0) Favorites

      Abstract:As a new type of distributed machine learning paradigm, federated learning makes full use of the computing power of many distributed clients and their local data to jointly train a machine learning model under the premise of meeting user privacy and data confidentiality requirements. In cross-device federated learning scenarios, the client usually consists of thousands or even tens of thousands of mobile devices or terminal devices. Due to the limitations of communication and computing costs, the aggregation server only selects few clients for the training during each round of training. Meanwhile, several widely employed federated optimization algorithms adopt a completely random client selection algorithm, which has been proven to have a huge optimization space. In recent years, how to efficiently and reliably select a suitable set from massive heterogeneous clients to participate in training and thus optimize the resource consumption and model performance of federated learning protocols has been extensively studied, but there is still no comprehensive investigation on the key issue. Therefore, this study conducts a comprehensive survey of client selection algorithms for cross-device federated learning. Specifically, it provides a formal description of the client selection problem, then gives the classification of selection algorithms, and discusses and analyzes the algorithms one by one. Finally, some future research directions for client selection algorithms are explored.

    • Review on Temporal Graph Neural Networks for Financial Risk Prediction

      Online: March 20,2024 DOI: 10.13328/j.cnki.jos.007087

      Abstract (18) HTML (0) PDF 7.80 M (69) Comment (0) Favorites

      Abstract:Financial risk prediction plays an important role in financial market regulation and financial investment, and has become a research hotspot in artificial intelligence and financial technology in recent years. Due to the complex investment, supply and other relationships among financial event entities, existing research on financial risk prediction often employs various static and dynamic graph structures to model the relationship among financial entities. Meanwhile, convolutional graph neural networks and other methods are adopted to embed relevant graph structure information into the feature representation of financial entities, which enables the representation of both semantic and structural information related to financial risks. However, previous reviews of financial risk prediction only focus on studies based on static graph structures, but ignore the characteristics that the relationship among entities in financial events will change dynamically over time, which reduces the accuracy of risk prediction results. With the development of temporal graph neural networks, increasingly more studies have begun to pay attention to financial risk prediction based on dynamic graph structures, and a systematic and comprehensive review of these studies will help learners foster a complete understanding of financial risk prediction research. According to different methods to extract temporal information from dynamic graphs, this study first reviews three different neural network models for temporal graphs. Then, based on different graph learning tasks, it introduces the research on financial risk prediction in four areas, including stock price trend risk prediction, loan default risk prediction, fraud transaction risk prediction, and money laundering and tax evasion risk prediction. Finally, the difficulties and challenges facing the existing temporal graph neural network models in financial risk prediction are summarized, and potential directions for future research are prospected.

    • Survey on Key Techniques of Encrypted Computing in Fully Encrypted Databases

      Online: March 20,2024 DOI: 10.13328/j.cnki.jos.007095

      Abstract (21) HTML (0) PDF 5.24 M (79) Comment (0) Favorites

      Abstract:In recent years, with the popularity of cloud services, increasingly more enterprises and individuals have stored their data in cloud databases. However, enjoying the convenience of cloud services also brings about data security issues. One of the crucial problems is data confidentiality protection, which is to safeguard the sensitive data of users from being spied on or leaked. Fully encrypted databases have emerged to face this challenge. Compared with traditional databases, fully encrypted databases can encrypt data in the entire lifecycle of data transmission, storage, and computation, thereby ensuring data confidentiality. Currently, there are still many challenges in encrypting data while supporting all SQL functionalities and maintaining high performance. This study comprehensively investigates the key techniques of encrypted computing in fully encrypted databases, summarizes the techniques according to the types, and compares and sums up them based on functionality, security, and performance. Firstly, it introduces the architecture of fully encrypted databases, including crypto-based architecture, trusted execution environment (TEE)-based architecture, and hybrid architecture. Then, the key techniques of each architecture are summarized. Finally, the challenges and opportunities of current research are discussed, with some open problems provided for future research.

    • Survey on Temporal Knowledge Graph Representation and Reasoning

      Online: March 20,2024 DOI: 10.13328/j.cnki.jos.007093

      Abstract (23) HTML (0) PDF 7.17 M (83) Comment (0) Favorites

      Abstract:As a research hotspot in artificial intelligence in recent years, knowledge graphs have been applied to many fields in reality. However, with the increasingly diversified application scenarios of knowledge graphs, people gradually find that static knowledge graphs which do not change with time cannot fully adapt to the scenarios of high-frequency knowledge update. To this end, researchers propose the concept of temporal knowledge graphs containing temporal information. This study organizes all existing temporal knowledge graph representation and reasoning models and summarizes and constructs a theoretical framework for these models. Then, on this basis, it briefly introduces and analyzes the current research progress of temporal representation reasoning, and carries out the future trend prediction to help researchers develop and design better models.

    • Compositional Signal Temporal Logic for Runtime Quantitative Monitoring of Composite Services

      Online: March 13,2024 DOI: 10.13328/j.cnki.jos.007082

      Abstract (26) HTML (0) PDF 10.42 M (85) Comment (0) Favorites

      Abstract:In recent years, service-oriented IoT architectures have received a lot of attention from academia and industry. By encapsulating IoT resources into intelligent IoT services, interconnecting and collaborating these resource-constrained and capacity-evolving IoT services to facilitate IoT applications has become a widely adopted and flexible mechanism. Upon capacity-fluctuating and resource-varying edge devices, IoT services may experience QoS degradations or resource mismatches during their execution, making it difficult for IoT applications to continue and possibly inducing failures. Therefore, quantitative monitoring of IoT services at runtime has become the key to guaranteeing the robustness of IoT applications. Different monitoring mechanisms have been proposed in recent literature, but they are inadequate in formal interpretation with strong domain relevance and empirical subjectivity. Based on formal methods, such as signal temporal logic (STL), the problem of IoT service monitoring can be formulated as a temporal logic task to achieve runtime quantitative monitoring. However, STL and its extensions suffer from issues of non-differentiability, loss of soundness, and inapplicability in dynamic environments. Moreover, existing works are inadequate for the monitoring of composite services, with a lack of integrity, linkage, and dynamics. To solve these problems, this study proposes a compositional signal temporal logic (CSTL) to achieve quantitative monitoring of different QoS constraints and time constraints upon intra-, inter-, and composite services. Specifically, CSTL extends an accumulative operator based on positively and negatively biased Riemann sums to emphasize the robust satisfaction of all sub-formulae over their entire time domains and to evaluate qualitative and quantitative constraint satisfaction for IoT service monitoring. Besides, CSTL extends a compositional operator based on constraint types and composite structures, as well as dynamic variables that can vary with the dynamic environment, to effectively monitor QoS variations and temporal violations of composite services. As a result, temporal and QoS constraints upon intra-, inter-, and composite services, can be specified by CSTL formulae, and formally interpreted with qualitative and quantitative satisfaction at runtime. Extensive evaluations show that the proposed CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

    • Research Progress in High-performance Cryptographic Computing Technology Based on Heterogeneous Multicore GPUs

      Online: March 13,2024 DOI: 10.13328/j.cnki.jos.007089

      Abstract (27) HTML (0) PDF 8.52 M (78) Comment (0) Favorites

      Abstract:As the core foundation for ensuring network security, cryptography plays a crucial role in data protection, identity verification, encrypted communication, and other aspects. With the rapid popularization of 5G and the Internet of Things technology, network security is facing unprecedented challenges, and the demand for cryptographic performance is showing explosive growth. GPU can utilize thousands of parallel computing cores to accelerate complex computing problems, which is very suitable for the computationally intensive nature of cryptographic algorithms. Therefore, researchers have extensively explored methods to accelerate various cryptographic algorithms on GPU platforms. Compared with platforms such as CPU and FPGA, GPU has significant performance advantages. This study discusses the classification of various cryptographic algorithms and GPU platform architecture, and provides a detailed analysis of current research on various ciphers on GPU heterogeneous platforms. Additionally, it summarizes the current technical challenges confronted by high-performance cryptography based on GPU platforms and provides prospects for future technological development. Finally, comprehensive references can be provided for practitioners in cryptography engineering research on the latest research progress and application practices of high-performance cryptography based on GPU by in-depth studies and summaries.

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