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A graph, made up of vertices and edges, is a natural representation for many real-world applications. Graph artificial intelligence (AI) techniques, especially graph neural networks (GNNs), are becoming increasingly important in modern machine learning and data analysis, as they can accurately represent high- dimensional features of vertices, edges, and structure information into low-dimensional embeddings. They have become a valuable area of study for students in fields like computer science, data science, and AI. However, the students are facing two challenges to grasp the knowledge of GNNs, including (i) learning GNNs often requires multidiscipline knowledge, and (ii) resources for learning GNNs are often fragmented. Motivated by that, we designed a self-contained course module on high-performance computing for graph AI: from a top-down perspective based on our study in this area for the past years. In particular, we divide them into four levels from the top to the bottom, including (i) level 1: graph theory basics, (ii) level 2: fundamental theories of GNNs, (iii) level 3: efficient graph AI computation framework, and (iv) level 4: GPU architecture and programming. In addition, we have disseminated part of this module into different educational activities, such as courses and tutorials. This paper is submitted for the Research to Education track of EduPar-25. 

Smart contracts underpin decentralized applications but face significant security risks from vulnerabilities, while traditional analysis methods have limitations. Large Language Models (LLMs) offer promise for vulnerability detection, yet adapting these powerful models efficiently, particularly generative ones, remains challenging. This paper investigates two key strategies for the efficient adaptation of LLMs for Solidity smart contract vulnerability detection: (1) replacing token-level generation with a dedicated classification head during fine-tuning, and (2) selectively freezing lower transformer layers using Low-Rank Adaptation (LoRA). Our empirical evaluation demonstrates that the classification head approach enables models like Llama 3.2 3B to achieve high accuracy (77.5%), rivaling the performance of significantly larger models such as the fine-tuned GPT-3.5. Furthermore, we show that selectively freezing bottom layers reduces training time and memory usage by approximately 10-20% with minimal impact on accuracy. Notably, larger models (3B vs. 1B parameters) exhibit greater resilience to layer freezing, maintaining high accuracy even with a large proportion of layers frozen, suggesting a localization of general code understanding in lower layers versus task-specific vulnerability patterns in upper layers. These findings present practical insights for developing and deploying performant LLM-based vulnerability detection systems efficiently, particularly in resource-constrained settings. 

Breadth-First Search (BFS) is a fundamental graph traversal algorithm in a level-by-level pattern. It has been widely used in real-world applications, such as social network analysis, scientific computing, and web crawling. However, achieving high performance for BFS on large-scale graphs remains a challenging task due to irregular memory access patterns, diverse graph structures, and the necessity for efficient parallelization. This paper addresses these challenges by designing a highly optimized parallel BFS implementation based on the top-down and bottom-up traversal strategies. It further integrates several key innovations, including graph typea-ware computation strategy selection, graph pruning, twolevel bottom-up, and efficient parallel implementation. We evaluate our method on 11 diverse graphs in terms of size, diameter, and density. On a CPU server with 48 threads, our method achieves an average speedup of 9.5x over the serial BFS implementation. Also, on a synthetic dense graph, our method processes 9.3 billion edges per second, showing its efficiency in dense graph traversal. 

The single-source shortest path (SSSP) problem is essential in graph theory with applications in navigation, biology, social networks, and traffic analysis. The  -Stepping algorithm enhances parallelism by grouping vertices into "buckets" based on their tentative distances. However, its performance depends on values and graph properties. This paper introduces an adaptive parallel Delta-Stepping implementation with three innovations: neighbor reordering, bucket fusion, and graph type-aware selection. Tested on 11 diverse graphs, it achieves an average 7.1× speedup over serial Dijkstra’s algorithm on a 48-thread CPU server. 

Microservices have emerged as a strong architecture for large-scale, distributed systems in the context of cloud computing and containerization. However, the size and complexity of microservice systems have strained current access control mechanisms. Intricate dependency structures, such as multi-hop dependency chains, go uncaptured by existing access control mechanisms and leave microservice deployments open to adversarial actions and influence. This work introduces CloudCover, an access control mechanism and enforcement framework for microservices. CloudCover provides holistic, deployment-wide analysis of microservice operations and behaviors. It implements a verificationin-the-loop access control approach, mitigating multi-hop microservice threats through control-flow integrity checks. We evaluate these domain-relevant multi-hop threats and CloudCover under existing, real-world scenarios such as Istio’s opensource microservice example and under theoretic and synthetic network loads of 10,000 requests per second. Our results show that CloudCover is appropriate for use in real deployments, requiring no microservice code changes by administrators 

Geo-obfuscation is a location privacy protection mechanism used by mobile users to conceal their precise locations when reporting location data, and it has been widely used to protect the location privacy of workers in spatial crowdsourcing (SC). However, this technique introduces inaccuracies in the reported locations, raising the question of how to control the quality loss that results from obfuscation in SC services. Prior studies have addressed this issue in time-insensitive SC settings, where some degree of quality degradation can be accepted and the locations can be expressed with less precision, which, however, is inadequate for time-sensitive SC. In this paper, we aim to minimize the quality loss caused by geo-obfuscation in time-sensitive SC applications. To this end, we model workers’ mobility on a fine-grained location field and constrain each worker’s obfuscation range to a set of peer locations, which have similar traveling costs to the destination as the actual location. We apply a linear programming (LP) framework to minimize the quality loss while satisfying both peer location constraints and geo-indistinguishability, a location privacy criterion extended from differential privacy. By leveraging the constraint features of the formulated LP, we enhance the time efficiency of solving LP through the geo-indistinguishability constraint reduction and the column generation algorithm. Using both simulation and real-world experiments, we demonstrate that our approach can reduce the quality loss of SC applications while protecting workers’ location privacy.