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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chadha, Mohak Wieland, Paul Gerndt, Michael Technische Universität München Computer Architecture and Parallel Systems Garching Germany

ISBN: (纸本)9798350330663

With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS enables an application to be decomposed into fine-grained functions that are executed on a FaaS platform. It offers several advantages such as no infrastructure management, a pay-per-use billing policy, and on-demand fine-grained autoscaling. However, despite its advantages, developers today encounter various challenges while adopting FaaS solutions that reduce productivity. These include FaaS platform lock-in, support for diverse function deployment parameters, and diverse interfaces for interacting with FaaS platforms. To address these challenges, we present gFaaS, a novel framework that facilitates the holistic development and management of functions across diverse FaaS platforms. Our framework enables the development of generic functions in multiple programming languages that can be seamlessly deployed across different platforms without modifications. Results from our experiments demonstrate that gFaaS functions perform similarly to native platform-specific functions across various scenarios. A video demonstrating the functioning of gFaaS is available from https://***/STbb6ykJFf0. © 2024 IEEE.

关键词： Interoperability

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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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What’s Missing in Agile Hardware Design? Verification!

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Journal of computer Science & Technology 2023年第4期38卷 735-736页

作者： Babak Falsafi Parallel Systems Architecture Laboratory Institute of Computer and Communication SciencesSchool of Computer andCommunication SciencesEcole Polytechnique Fédérale de LausanneLausanneCH-1015Switzerland

Agile hardware design is an approach to developing hardware systems that draws inspiration from the principles and practices of agile software *** emphasizes collaboration,flexibility,iterative development,and quick adaptation to changing *** agile hardware design,the focus is on delivering functionalhardware systems in shorter development cycles while maintaining high-quality and customer *** particular,agile hardware design is of great interest in the open-source hardware ***-sourcehardware development—such as RISC-V—is at the forefront of initiatives to democratize hardware and drive innovation in chip design *** design is instrumental for the RISC-V community because it supportsrapid iteration,accommodates the evolving RISC-V standard and the addition of custom extensions,improvescommunity collaboration and time-to-market,and addresses the design challenges associated with complex architectural features.

关键词： hardware agile architectural

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Analyzing Digital Services Across the Compute Continuum Using iFogSim 29

Analyzing Digital Services Across the Compute Continuum Usin...

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29th IEEE International Conference on Embedded and Real-Time Computing systems and Applications, RTCSA 2023

作者： Baneshi, Saeedeh Varbanescu, Ana-Lucia Pathania, Anuj Akesson, Benny Pimentel, Andy University of Amsterdam Parallel Computer Systems SNE Lab Netherlands TNO-ESI Eindhoven Netherlands

ISBN: (纸本)9798350337860

Digital services enable users to interact with a broad range of applications and, as such, have become an essential part of our daily lives. Although convenient, their ubiquity comes at a significant cost in energy, raising sustainability concerns. We access these services by triggering a computing continuum, spanning from the device to the edge, fog, and cloud. Scheduling decisions made at each layer impact the overall quality of service (QoS) and energy consumption of digital services. © 2023 IEEE.

关键词： Quality of service

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Securing Digital Integrity: Proposed Comprehensive Framework for Deepfake Detection and Blockchain Validation 5th

Securing Digital Integrity: Proposed Comprehensive Framework...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Jain, Anant Gaur, Adamya Gupta, Gauranshi Mishra, Shubhangi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819773701

This paper proposes a comprehensive solution to combat the growing threat of Deepfake technology, employing Convolutional Neural Networks (CNNs) and Blockchain. CNNs analyze video frames for anomalies indicative of Deepfake manipulation, while Blockchain ensures content integrity through timestamping and IPFS hashes. Leveraging transfer learning with EfficientNet-B1 architecture and dropout layers to prevent overfitting, the CNN model attains a 98.53% accuracy on testing data. Smart contracts are utilized to store social media content and AI verification results. The methodology integrates private storage on social media platforms, AI-based CNN verification, Blockchain timestamping, smart contract verification, and continuous improvement. By synergizing AI and Blockchain technologies, our approach aims to bolster defenses against deceptive content proliferation, enhancing the reliability of online media ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Empowering Health Insurance with Personalization: A Comprehensive Architecture Leveraging Federated Learning and BlockChain

Empowering Health Insurance with Personalization: A Comprehe...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jain, Anant Gupta, Gauranshi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819795062

Historically, the health insurance domain has adhered to uniform premium pricing structures, often sidelining the distinct health nuances of each policyholder. Such broad-brush approaches can result in misaligned premiums, not truly reflective of individual health dynamics. Venturing into uncharted territories, this study proposes an innovative fusion of Federated Machine Learning (FML) and BlockChain methodologies to recalibrate health insurance pricing. FML, a cutting-edge evolution in the machine learning sphere, emphasizes decentralized data processing. By harnessing FML, timely insights can be gathered that mirror an individual’s health trajectory. This continuous insight generation facilitates agile adjustments to insurance premiums, ensuring they resonate with real-time health conditions. BlockChain’s decentralized ledger system acts as the bedrock of this model. Each premium modification, steered by FML-derived insights, is indelibly etched onto the BlockChain. This architecture not only masters transparency but also instills a heightened sense of trust, given the unalterable nature of BlockChain entries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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

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

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Zawalska, Justyna Chung, Minh Rycerz, Katarzyna Schulz, Laura Schulz, Martin Kranzlmuller, Dieter AGH University of Krakow Institute of Computer Science Krakow Poland CYFRONET AGH Academic Computer Center Krakow Poland Garching bei München Germany Chair for Computer Architecture and Parallel Systems Germany MNM-Team 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. © 2024 IEEE.

关键词： CQM HPC HPCQC integration Load Rebalancing Quantum Computing task migration

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Fine-Grained Re-Execution for Efficient Batched Commit of Distributed Transactions 49th

Fine-Grained Re-Execution for Efficient Batched Commit of Di...

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49th International Conference on Very Large Data Bases, VLDB 2023

作者： Dong, Zhiyuan Wang, Zhaoguo Zhang, Xiaodong Xu, Xian Zhao, Changgeng Chen, Haibo Panda, Aurojit Li, Jinyang Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Department of Computer Science New York University United States

Distributed transaction systems incur extensive cross-node communication to execute and commit serializable OLTP transactions. As a result, their performance greatly suffers. Caching data at nodes that execute transactions can cut down remote reads. Batching transactions for validation and persistence can amortize the communication cost during committing. However, caching and batching can significantly increase the likelihood of conflicts, causing expensive aborts. In this paper, we develop Hackwrench to address the challenge of caching and batching. Instead of aborting conflicted transactions, Hackwrench tries to repair them using fine-grained re-execution by tracking the dependencies of operations among a batch of transactions. Tracked dependencies allowHackwrench to selectively invalidate and re-execute only those operations necessary to "fix" the conflict, which is cheaper than aborting and executing an entire batch of transactions. Evaluations using TPC-C and other micro-benchmarks show that Hackwrench can outperform existing commercial and research systems including FoundationDB, Calvin,COCO, and Sundial under comparable settings. © 2023, VLDB Endowment. All rights reserved.

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Realistic Neutral Atom Image Simulation 4

Realistic Neutral Atom Image Simulation

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Winklmann, Jonas Tsevas, Dimitrios Schulz, Martin Computer Architecture and Parallel Systems Technical University of Munich Munich Germany Max Planck Institute of Quantum Optics Quantum Many-Body Systems Division Garching Germany

ISBN: (纸本)9798350343236

Neutral atom quantum computers require accurate single atom detection for the preparation and readout of their qubits. This is usually done using fluorescence imaging. The occupancy of an atom site in these images is often somewhat ambiguous due to the stochastic nature of the imaging process. Further, the lack of ground truth makes it difficult to rate the accuracy of reconstruction algorithms. We introduce a bottom-up simulator that is capable of generating sample images of neutral atom experiments from a description of the actual state in the simulated system. Possible use cases include the creation of exemplary images for demonstration purposes, fast training iterations for deconvolution algorithms, and generation of labeled data for machine-learning-based atom detection approaches. The implementation is available through our GitHub as a C library or wrapped Python package. We show the modeled effects and implementation of the simulations at different stages of the imaging process. Not all real-world phenomena can be reproduced perfectly. The main discrepancies are that the simulator allows for only one characterization of optical aberrations across the whole image, supports only discrete atom locations, and does not model all effects of complementary metal-oxide-semiconductor (CMOS) cameras perfectly. Nevertheless, our experiments show that the generated images closely match real-world pictures to the point that they are practically indistinguishable and can be used as labeled data for training the next generation of detection algorithms. © 2023 IEEE.

关键词： Atoms

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