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HeatSense: Intelligent Thermal Anomaly Detection for Securing NoC-Enabled MPSoCs

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arXiv 2025年

作者： Hasanzadeh, Mahdi Khalil, Kasem Sturton, Cynthia Patooghy, Ahmad Department of Computer Systems Technology North Carolina A&T State University GreensboroNC United States Electrical and Computer Engineering Department University of Mississippi OxfordMS United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC United States

Multi-Processor System-on-Chips (MPSoCs) are highly vulnerable to thermal attacks that manipulate dynamic thermal management systems. To counter this, we propose an adaptive real-time monitoring mechanism that detects abnormal thermal patterns in chip tiles. Our design space exploration helped identify key thermal features for an efficient anomaly detection module to be implemented at routers of network-enabled MPSoCs. To minimize hardware overhead, we employ weighted moving average (WMA) calculations and bit-shift operations, ensuring a lightweight yet effective implementation. By defining a spectrum of abnormal behaviors, our system successfully detects and mitigates malicious temperature fluctuations, reducing severe cases from 3.00°C to 1.9°C. The anomaly detection module achieves up to 82% of accuracy in detecting thermal attacks, which is only 10-15% less than top performing machine learning (ML) models like Random Forest. However, our approach reduces hardware usage by up to 75% for logic resources and 100% for specialized resources, making it significantly more efficient than ML-based solutions. This method provides a practical, low-cost solution for resource-constrained environments, ensuring resilience against thermal attacks while maintaining system performance. Copyright © 2025, The Authors. All rights reserved.

关键词： parallel processing systems

High-Level Message Sequence Charts: Satisfiability and Realizability Revisited

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arXiv 2025年

作者： Bollig, Benedikt Fortin, Marie Gastin, Paul Université Paris-Saclay CNRS ENS Paris-Saclay LMF Gif-sur-Yvette France Université Paris Cité CNRS IRIF Paris France

Message sequence charts (MSCs) visually represent interactions in distributed systems that communicate through FIFO channels. High-level MSCs (HMSCs) extend MSCs with choice, concatenation, and iteration, allowing for the specification of complex behaviors. This paper revisits two classical problems for HMSCs: satisfiability and realizability. Satisfiability (also known as reachability or nonemptiness) asks whether there exists a path in the HMSC that gives rise to a valid behavior. Realizability concerns translating HMSCs into communicating finite-state machines to ensure correct system implementations. While most positive results assume bounded channels, we introduce a class of HMSCs that allows for unbounded channels while maintaining effective implementations. On the other hand, we show that the corresponding satisfiability problem is still undecidable. © 2025, CC BY.

关键词： parallel processing systems

GraphCom: Communication Hierarchy-aware Graph Engine for Distributed Model Training 25

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GraphCom: Communication Hierarchy-aware Graph Engine for Dis...

34th ACM Web Conference, WWW 2025

作者： Gan, Xinbiao Li, Tiejun Wu, Liang Zhang, Qiang Song, Lingyun Yang, Bo Liu, Jie Lu, Kai School of Computer National University of Defense Technology Changsha China National University of Defense Technology Changsha China School of Computer Science Northwest Polytechnical University Chanxi China

ISBN: (纸本)9798400712746

Efficient processing of large-scale graphs with billions to trillions of edges is essential for training graph-based large language models (LLMs) in web-scale systems. The increasing complexity and size of these models create significant communication challenges due to the extensive message exchanges required across distributed nodes. Current graph engines struggle to effectively scale across hundreds of computing nodes because they often overlook variations in communication costs within the interconnection hierarchy. This paper presents GraphCom, a communication-efficient message graph engine for graph processing on supercomputers. Our key idea is to leverage the network topology information to perform communication hierarchy-aware message aggregation, where messages are (i) gathered to the responsible nodes (referred to as monitors) in the source domains, (ii) transferred between monitors, and (iii) scattered to the target nodes in the target domains. GraphCom’s aggregation is more aggressive in that each source domain (instead of the source node). We have implemented GraphCom on top of MPI. We demonstrate GraphCom’s effectiveness with synthetic benchmarks and real-world graphs, utilizing up to 79,024 nodes and over 1.2 million processor cores, demonstrating that GraphCom surpasses leading graph-parallel systems and state-of-the-art counterparts in both throughput and scalability. Moreover, we have deployed GraphCom on a production supercomputer, where it consistently outperforms the top solutions on the Graph500 list. These results highlight the potential GraphCom has to significantly improve the efficiency of distributed large-scale graph-based LLM training by optimizing communication between distributed systems, making it an invaluable graph engine for distributed training tasks on web-scale graphs. © 2025 Copyright held by the owner/author(s).

关键词： parallel processing systems

A parallel implementation of reduced-order modeling of large-scale systems

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arXiv 2025年

作者： Farcas, Ionut-Gabriel Gundevia, Rayomand P. Munipalli, Ramakanth Willcox, Karen E. Virginia Tech BlacksburgVA24061 United States Amentum Edwards AFB CA93524 United States Air Force Research Laboratory Edwards AFBCA93524 United States The University of Texas at Austin AustinTX78712 United States Department of Mathematics Uruguay Oden Institute for Computational Engineering and Sciences United States

Motivated by the large-scale nature of modern aerospace engineering simulations, this paper presents a detailed description of distributed Operator Inference (dOpInf), a recently developed parallel algorithm designed to efficiently construct physics-based reduced-order models (ROMs) for problems with large state dimensions. One such example is the simulation of rotating detonation rocket engines, where snapshot data generated by high-fidelity large-eddy simulations have many millions of degrees of freedom. dOpInf enables, via distributed computing, the efficient processing of datasets with state dimensions that are too large to process on a single computer, and the learning of structured physics-based ROMs that approximate the dynamical systems underlying those datasets. All elements of dOpInf are scalable, leading to a fully parallelized reduced modeling approach that can scale to the thousands of processors available on leadership high-performance computing platforms. The resulting ROMs are computationally cheap, making them ideal for key engineering tasks such as design space exploration, risk assessment, and uncertainty quantification. To illustrate the practical application of dOpInf, we provide a step-by-step tutorial using a 2D Navier-Stokes flow over a step scenario as a case study. This tutorial guides users through the implementation process, making dOpInf accessible for integration into complex aerospace engineering simulations. © 2025, CC BY.

关键词： parallel processing systems

Adaptive fully implicit and thermodynamically consistent modeling of multiphase flow in porous media on three dimensional grids

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Computers and Fluids 2025年 298卷

作者： Sheng, Qiuyu Yang, Haijian Chen, Huangxin Cheng, Tianpei Sun, Shuyu School of Mathematics Hunan University Changsha Hunan410082 China School of Mathematical Sciences and Fujian Provincial Key Laboratory on Mathematical Modeling and High Performance Scientific Computing Xiamen University Fujian361005 China School of Mathematical Sciences Peking University Beijing100871 China School of Mathematical Sciences Tongji University Shanghai200092 China

The development of innovative mathematical models and state-of-the-art simulators for multiphase flow in porous media is a key focus in hydrogeology. However, many traditional models for multiphase flow in porous media lack complete thermodynamic consistency, as the energy reconstructed from the capillary pressure fails to yield a time-dependent system with a dissipated free energy. In this paper, we utilize a thermodynamically consistent model for multiphase flow in porous media, which satisfies the second law of thermodynamics. For large-scale numerical simulation of the resultant flow model, we introduce and investigate a robust and scalable fully implicit simulator with a suitable time adaptivity strategy designed for distributed memory parallel computers. In particular, our approach enhances the numerical formulation by proposing a family of inexact Newton–Krylov methods for efficient computation and several types of field-split algorithms for large-scale preconditioning. The numerical experiments indicate that the proposed fully implicit simulator accurately predicts the highly complex physical processes of thermodynamically consistent problems, particularly those characterized by high heterogeneity with complex reservoir topography on 3D structured or unstructured grids. © 2025 Elsevier Ltd

关键词： parallel processing systems

Identification of Active Subfunctions in Finite-Max Minimisation via a Smooth Reformulation

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arXiv 2024年

作者： Ras, Charl J. Tam, Matthew K. Uteda, Daniel J. School of Mathematics & Statistics The University of Melbourne ParkvilleVIC3010 Australia

In this work, we consider a nonsmooth minimisation problem in which the objective function can be represented as the maximum of finitely many smooth "subfunctions". First, we study a smooth min-max reformulation of the problem. Due to this smoothness, the model provides enhanced capability of exploiting the structure of the problem, when compared to methods that attempt to tackle the nonsmooth problem directly. Then, we present several approaches to identify the set of active subfunctions at a minimiser, all within finitely many iterations of a first order method for solving the smooth model. As is well known, the problem can be equivalently rewritten in terms of these subfunctions, but a key challenge is to identify this set a priori. Such an identification is clearly beneficial in an algorithmic sense, since one can apply this knowledge to create an equivalent problem with lower complexity, thus facilitating generally faster convergence. Finally, numerical results comparing the accuracy of each of these approaches are presented, along with the effect they have on reducing the complexity of the original *** Codes 49K35, 65B99, 65K15, 65Y20, 90C25, 90C33, 90C47 Copyright © 2024, The Authors. All rights reserved.

关键词： parallel processing systems

Computing Performance Analysis of IEC 61499 Distributed Automation systems 26

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Computing Performance Analysis of IEC 61499 Distributed Auto...

26th International Conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 IEEE.

关键词： parallel processing systems

Optimizing Multi-DNN Inference on Mobile Devices through Heterogeneous Processor Co-Execution

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arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： parallel processing systems

COMPLEXITY OF SOME ALGORITHMIC PROBLEMS IN GROUPS: A SURVEY

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arXiv 2024年

作者： Shpilrain, Vladimir Department of Mathematics The City College of New York New YorkNY10031 United States

In this survey, we address the worst-case, average-case, and generic-case time complexity of the word problem and some other algorithmic problems in several classes of groups and show that it is often the case that the average-case complexity of the word problem is linear with respect to the length of an input word, which is as good as it gets if one considers groups given by generators and defining relations. At the same time, there are other natural algorithmic problems, for instance, the geodesic (decision) problem or Whitehead's automorphism problem, where the average-case time complexity can be sublinear, even constant. © 2024, CC BY.

关键词： parallel processing systems