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Proximity-based service discovery for distributed digital twin systems

Discover Internet of Things 2025年第1期5卷 1-24页

作者： Rothermel, Kurt Herzog, Otthein Zhiqiang, Siegfried Wu Institute Of Parallel and Distributed Systems University of Stuttgart Universitaetsstr. 38 Stuttgart70569 Germany College of Architecture and Urban Planning Tongji University 1239 Siping Road Shanghai200092 China Department of Mathematics and Computer Science University of Bremen Bibliothekstrasse 5 Bremen23884 Germany

Over the past decade, there has been a significant increase in interest in digital twin (DT) technology in a variety of domains. While research on DTs of single assets was initially prevalent, there has been a notable shift towards distributed systems of DTs, which connect to each other to collaborate. Typically, collaboration is enabled by DTs providing services that can be consumed by other DTs. In service-oriented systems, a service is typically identified by type information. However, this is not sufficient in distributed DT systems, where DTs associated with different physical entities may provide the same type of service. Consequently, selecting the appropriate service depends not only on the service type, but also on the associated physical entity. However, requiring DTs to know the mapping of services to their physical environment is not feasible for large dynamic systems. This paper presents a novel proximity-based service discovery method that allows DTs to select services based on service type and their proximity to other objects. That is, service specifications are fully abstracted from the mapping of services to physical objects, relieving DTs from maintaining information about this mapping. Furthermore, service discovery is robust to changes in the physical environment and service population. The proposed service discovery method has been implemented on top of a spatial DBMS. We argue that this implementation is optimal in terms of network utilization and latency, and perform comprehensive evaluations to show the performance of discovery queries as a function of their complexity. © The Author(s) 2025.

关键词： Location based services

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VersaSlot: Efficient Fine-grained FPGA Sharing with *** Slots and Live Migration in FPGA Cluster

arXiv

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arXiv 2025年

作者： Gu, Jianfeng Wang, Hao Guo, Xiaorang Schulz, Martin Gerndt, Michael Chair of Computer Architecture and Parallel Systems Technical University of Munich Munich Germany

As FPGAs gain popularity for on-demand application acceleration in data center computing, dynamic partial reconfiguration (DPR) has become an effective fine-grained sharing technique for FPGA multiplexing. However, current FPGA sharing encounters partial reconfiguration contention and task execution blocking problems introduced by the DPR, which significantly degrade application performance. In this paper, we propose VersaSlot, an efficient spatio-temporal FPGA sharing system with novel *** slot architecture that can effectively resolve the contention and task blocking while improving resource utilization. For the heterogeneous *** architecture, we introduce an efficient slot allocation and scheduling algorithm, along with a seamless cross-board switching and live migration mechanism, to maximize FPGA multiplexing across the cluster. We evaluate the VersaSlot system on an FPGA cluster composed of the latest Xilinx UltraScale+ FPGAs (ZCU216) and compare its performance against four existing scheduling algorithms. The results demonstrate that VersaSlot achieves up to 13.66x lower average response time than the traditional temporal FPGA multiplexing, and up to 2.19x average response time improvement over the state-of-the-art spatio-temporal sharing systems. Furthermore, VersaSlot enhances the LUT and FF resource utilization by 35% and 29% on average, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Design of an FPGA-Based Neutral Atom Rearrangement Accelerator for Quantum Computing

Design of an FPGA-Based Neutral Atom Rearrangement Accelerat...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Xiaorang Guo Jonas Winklmann Dirk Stober Amr Elsharkawy Martin Schulz Chair of Computer Architecture and Parallel Systems Technical University of Munich Garching Germany

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Neutral atoms have emerged as a promising technology for implementing quantum computers due to their scalability and long coherence times. However, the execution frequency of neutral atom quantum computers is constrained by image processing procedures, particularly the assembly of defect-free atom arrays, which is a crucial step in preparing qubits (atoms) for execution. To optimize this assembly process, we propose a novel quadrant-based rearrangement algorithm that employs a divide-and-conquer strategy and also enables the simultaneous movement of multiple atoms, even across different columns and rows. We implement the algorithm on Field Programmable Gate Arrays (FPGAs) to handle each quadrant independently (hardware-level optimization) while maximizing parallelization. To the best of our knowledge, this is the first hardware acceleration work for atom rearrangement, and it significantly reduces the processing time. This achievement also contributes to the ongoing efforts of tightly integrating quantum accelerators into High-Performance Computing (HPC) systems. Tested on a Zynq RFSoC FPGA at 250 MHz, our hardware implementation is able to complete the rearrangement process of a 30 × 30 compact target array, derived from a 50 × 50 initial loaded array, in approximately 1.0 μs. Compared to a comparable CPU implementation and to state-of-the-art FPGA work, we achieved about 54 x and 300 x speedups in the rearrangement analysis time, respectively. Additionally, the FPGA-based acceleration demonstrates good scalability, allowing for seamless adaptation to varying sizes of the atom array, which makes this algorithm a promising solution for large-scale quantum systems.

关键词： computers Schedules Quantum system Quantum computing Scalability Qubit Atoms Central Processing Unit Field programmable gate arrays Assembly

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KLiNQ: Knowledge Distillation-Assisted Lightweight Neural Network for Qubit Readout on FPGA

arXiv

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arXiv 2025年

作者： Guo, Xiaorang Bunarjyan, Tigran Liu, Dai Lienhard, Benjamin Schulz, Martin Chair of Computer Architecture and Parallel Systems Technical University of Munich Garching Germany Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Superconducting qubits are among the most promising candidates for building quantum information processors. Yet, they are often limited by slow and error-prone qubit readout—a critical factor in achieving high-fidelity operations. While current methods, including deep neural networks, enhance readout accuracy, they typically lack support for mid-circuit measurements essential for quantum error correction, and they usually rely on large, resource-intensive network models. This paper presents KLiNQ, a novel qubit readout architecture leveraging lightweight neural networks optimized via knowledge distillation. Our approach achieves around a 99% reduction in model size compared to the baseline while maintaining a qubit-state discrimination accuracy of 91%. KLiNQ facilitates rapid, independent qubit-state readouts that enable mid-circuit measurements by assigning a dedicated, compact neural network for each qubit. Implemented on the Xilinx UltraScale+ FPGA, our design can perform the discrimination within 32 ns. The results demonstrate that compressed neural networks can maintain high-fidelity independent readout while enabling efficient hardware implementation, advancing practical quantum computing. Copyright © 2025, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Telemetry for Quantum systems in HPC Centers

Telemetry for Quantum Systems in HPC Centers

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ISC High Performance 2025 Research Paper Proceedings (40th International Conference)

作者： Hossam Ahmed Burak Mete Helmut Heller Matthew Tovey Xiaolong Deng Asim Zulfiqar Muhammad Nufail Farooqi Mahmoud Abuzayed Martin Schulz Laura Schulz QCT Department Leibniz Supercomputing Centre of the Bavarian Academy of Science and Humanities (LRZ) Garching b. München Germany Chair for Computer Architecture and Parallel Systems (CAPS) Technical University of Munich (TUM) Garching b. München Germany

ISBN: (数字)9783982633619

Quantum systems have traditionally operated within highly controlled laboratory environments to minimize internal noise and maximize stability. However, when these systems are integrated into High Performance Computing (HPC) environments and setup for continuous operation, they encounter significantly higher levels of external noise and instability coupled with higher demands. This paper introduces a comprehensive framework to counter the challenges of maintaining the stability of operational quantum computers in such noisy HPC settings. We propose a novel system architecture that monitors and manages the environmental conditions of quantum computing systems using a broad set of sensors, enhancing their robustness and reliability. Our architecture is open and can be extended to other setups. It can serve as a blueprint for other sites as it spans quantum technologies and offers broad coverage. Ultimately, this data will be essential to facilitate advancements in machine learning to drive real-time error mitigation.

关键词： computers Quantum system Trapped ions Quantum computing Working environment noise systems architecture Machine learning Stability analysis Telemetry Monitoring

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City models: past, present and future prospects

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Frontiers of Urban and Rural Planning 2025年第1期3卷 1-24页

作者： Ritter, Helge Herzog, Otthein Rothermel, Kurt Cohn, Anthony G. Wu, Zhiqiang Faculty of Technology Bielefeld University Bielefeld Germany College of Architecture and Urban Planning Tongji University Shanghai People’s Republic of China FB 03 University of Bremen Bremen Germany Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart Germany School of Computer Science University of Leeds Leeds UK The Alan Turing Institute London UK

This paper attempts to take a comprehensive look at the challenges of representing the spatio-temporal structures and dynamic processes that define a city’s overall characteristics. For the task of urban planning and urban operation, we take the stance that even if the necessary representations of these structures and processes can be achieved, the most important representation of the relevant mindsets of the citizens are, unfortunately, mostly neglected. After a review of major “traditional” urban models of structures behind urban scale, form, and dynamics, we turn to major recent modeling approaches triggered by recent advances in AI that enable multimodal generative models. Some of these models can create representations of geometries, networks and images, and reason flexibly at a human-compatible semantic level. They provide huge amounts of knowledge extracted from huge collections of text and image documents and cover the required rich representation spectrum including geographic knowledge by different knowledge sources, degrees of granularity and scales. We then discuss what these new opportunities mean for coping with the modeling challenges posed by cities, in particular with regard to the role and impact of citizens and their interactions within the city infrastructure. We propose to integrate these possibilities with existing approaches, such as agent-based models, which opens up new modeling spaces including rich citizen models which are able to also represent social interactions. Finally, we put forward some thoughts about a vision of a “social AI in a city ecosystem” that adds relevant citizen models to state-of-the-art structural and process models. This extended city representation will enable urban planners to establish citizen-oriented planning of city infrastructures, to make them into inviting environments that reconcile and foster human culture, city resilience and sustainability.

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Leveraging Hybrid Classical-Quantum Methods for Efficient Load Rebalancing in HPC 24

Leveraging Hybrid Classical-Quantum Methods for Efficient Lo...

引用

Proceedings of the SC '24 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis

作者： Justyna Zawalska Minh Chung Katarzyna Rycerz Laura Schulz Martin Schulz Dieter Kranzlmüller AGH University of Krakow Institute of Computer Science Krakow Poland and Academic Computer Center CYFRONET AGH Krakow Poland Leibniz Supercomputing Centre (LRZ) Garching bei München Germany and MNM-Team Ludwig-Maximilians-Universität München (LMU) Germany Leibniz Supercomputing Centre (LRZ) Garching bei München Germany Leibniz Supercomputing Centre (LRZ) Garching bei München Germany and Chair for Computer Architecture and Parallel Systems Technical University of Munich (TUM) Germany

ISBN: (纸本)9798350355543

Load imbalance is a challenge for parallel applications in High Performance Computing (HPC). It is caused by processes having different execution times or load values, leading to idle or wait times at synchronization points, where faster processes must wait for the slowest process to catch up. To mitigate this issue, applications can employ load balancing (LB) strategies, which migrate load between processes to even out load. This is often referred to as the Load Rebalancing Problem (LRP). While many approaches solving the LRP exist, they can only be heuristics and hence further optimization potential exists. In our work, we turn to a novel approach by using hybrid classical-quantum approaches and present two versions of the constrained quadratic model for solving the LRP; the two differ in how they balance the number of qubits required with the types of applied constraints. We compare the quantum-based methods with classical methods using heuristic algorithms Greedy, Karmarkar--Karp, and ProactLB. We evaluate our approaches using imbalance ratio and speedup as metrics, as well as the number of migrated tasks to indicate overhead caused by migrations. Our results show that the quantum-based methods outperform the classic methods. For example, we need only 1/4 of the number of migrated tasks in a realistic use case compared with classical methods, particularly Greedy and KK, to balance the load.

关键词： CQM

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FedLesScan: Mitigating Stragglers in Serverless Federated Learning

FedLesScan: Mitigating Stragglers in Serverless Federated Le...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Elzohairy, Mohamed Chadha, Mohak Jindal, Anshul Grafberger, Andreas Gu, Jianfeng Gerndt, Michael Abboud, Osama Computer Architecture and Parallel Systems Germany Huawei Technologies Munich Germany

ISBN: (纸本)9781665480451

Federated Learning (FL) is a machine learning paradigm that enables the training of a shared global model across distributed clients while keeping the training data local. While most prior work on designing systems for FL has focused on using stateful always running components, recent work has shown that components in an FL system can greatly benefit from the usage of serverless computing and Function-as-a-Service technologies. To this end, distributed training of models with severless FL systems can be more resource-efficient and cheaper than conventional FL systems. However, serverless FL systems still suffer from the presence of stragglers, i.e., slow clients due to their resource and statistical heterogeneity. While several strategies have been proposed for mitigating stragglers in FL, most methodologies do not account for the particular characteristics of serverless environments, i.e., cold-starts, performance variations, and the ephemeral stateless nature of the function instances. Towards this, we propose FedLesScan, a novel clustering-based semi-asynchronous training strategy, specifically tailored for serverless F L. FedLesScan dynamically adapts to the behavior of clients and minimizes the effect of stragglers on the overall system. We implement our strategy by extending an open-source serverless FL system called FedLess. Moreover, we comprehensively evaluate our strategy using the 2nd generation Google Cloud Functions with four datasets and varying percentages of stragglers. Results from our experiments show that compared to other approaches FedLesScan reduces training time and cost by an average of 8% and 20% respectively while utilizing clients better with an average increase in the effective update ratio of 17.75%. © 2022 IEEE.

关键词： Deep learning

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Real-Time Capability of Dlr's Beamforming Synthetic Aperture Radar Processing architecture

Real-Time Capability of Dlr's Beamforming Synthetic Aperture...

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2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023

作者： Schlemon, Maron Schulz, Martin Scheiber, Rolf Jasger, Marc Oliva, Joel Amao Germany Technical University of Munich Computer Architecture and Parallel Systems Germany

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft's limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability. © 2023 IEEE.

关键词： Synthetic aperture radar

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e-science and the VL-e approach

引用

1st Converging Science conference, 2004

作者： Bob Hertzberger, L.O. Computer Architecture and Parallel Systems Group Department of Computer Science Universiteit van Amsterdam Netherlands

ISBN: (纸本)9783540332459

Now that the Internet and Web are abundantly present in every days live and net-working and computing speed are improving at an impressive rate, more and more attempts become visible to apply these developments towards science, in general often called: e-Science. In order for e-Science to work, an environment has to be created that should support the requirements of different scientific domains such as life sciences or high energy physics research. © Springer-Verlag Berlin Heidelberg 2006.

关键词： e-Science

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