The Maximum Minimal Cut Problem (MMCP), a NP-hard combinatorial optimization (CO) problem, has not received much attention due to the demanding and challenging bi-connectivity constraint. Moreover, as a CO problem, it is also a daunting task for machine learning, especially without labeled instances. To deal with these problems, this work proposes an unsupervised learning framework combined with heuristics for MMCP that can provide valid and high-quality solutions. As far as we know, this is the first work that explores machine learning and heuristics to solve MMCP. The unsupervised solver is inspired by a relaxation-plus-rounding approach, the relaxed solution is parameterized by graph neural networks, and the cost and penalty of MMCP are explicitly written out, which can train the model end-to-end. A crucial observation is that each solution corresponds to at least one spanning tree. Based on this finding, a heuristic solver that implements tree transformations by adding vertices is utilized to repair and improve the solution quality of the unsupervised solver. Alternatively, the graph is simplified while guaranteeing solution consistency, which reduces the running time. We conduct extensive experiments to evaluate our framework and give a specific application. The results demonstrate the superiority of our method against two techniques designed.

Quantum perturbation theory is pivotal in determining the critical physical properties of materials. The first-principles computations of these properties have yielded profound and quantitative insights in diverse domains of chemistry and physics. In this work, we propose a portable and scalable OpenCL implementation for quantum perturbation theory, which can be generalized across various high-performance computing (HPC) systems. Optimal portability is realized through the utilization of a cross-platform unified interface and a collection of performance-portable heterogeneous optimizations. Exceptional scalability is attained by addressing major constraints on memory and communication, employing a locality-enhancing task mapping strategy and a packed hierarchical collective communication scheme. Experiments on two advanced supercomputers demonstrate that our implementation exhibits remarkably performance on various material systems, scaling the system to 200,000 atoms with all-electron precision. This research enables all-electron quantum perturbation simulations on substantially larger molecular scales, with a potentially significant impact on progress in material sciences.

Maintaining the high availability of online systems requires reliable and fast online anomaly detection and fault localization. However, existing anomaly detection methods either suffer high training costs and low generalization capabilities or are designed and evaluated using offline data with limited efficacy in online usage. Furthermore, these methods' fault localization capabilities are often inadequate due to external observability constraints. Therefore, designing a new approach to address these limitations effectively is essential. To address the aforementioned limitations, this paper proposes a novel non-intrusive in situ anomaly detection and fault lo-calization framework, Detective-Dee. The proposed framework leverages a compressed sensing method for anomaly detection, which exhibits strong generalization capabilities and eliminates extensive training. Detective-Dee further improves its performance by incorporating three optimization techniques: concurrent sub-stitution sampling, Look-Up-Table-based similarity calculation, and substitution window-based threshold selection to improve parallelism and reduce computational and comparison overheads. Additionally, the framework adopts an innovative non-intrusive fault localization strategy based on anomaly detection triggering. This approach utilizes the dynamic instrumentation capabilities of eBPF, combined with extracting vulnerable function and function call chains through source code analysis, to improve the online anomaly detection capability and achieve robust fault localization with low overhead. To validate the effectiveness of Detective-Dee, we developed a prototype system and conducted a comprehensive evaluation. The results demonstrate that, compared to the state-of-the-art anomaly detection method, Detective-Dee exhibits a 4x improve-ment in anomaly detection speed while maintaining higher online and comparable offline detection ability. Furthermore, under 33 real-world fault cases across eight popular distributed systems, Detective-Dee successfully detects 31 cases and accurately locates 26 cases with less than 1% overhead, outperforming the state-of-the-art method.

In recent years, the demand for computing resources of AI industry is urgent because of the data explosion, which promoted the construction of computing power networks in the new era for operators. From the cloud network era to today's computing power network, stricter requirements are proposed to ensure the efficiency and security of computing services. Despite computing power scheduling technologies such as on-demand edge computing and efficient compute first network, knowledge graph techniques for graphs are less explored. As a new technology that can express the relationship between nodes in the graph extremely easily, knowledge graph has a natural advantage in expressing feature information of computing nodes in computing power network. Therefore, a novel knowledge graph representation for the architecture of computing power networks is proposed. And the knowledge graph of the computing power network is constructed by using the knowledge representation method. The scheduling tasks of computing power network is automatically executed by the proposed knowledge driven method based on the constructed knowledge graph. Different with the current scheduling technology of computing power network, the model will theoretically become more and more efficient and accurate with continuously addition of knowledge.