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DP in ice environments-development of a dynamic positioning in ice validation platform (DPIVP)

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2023年第6期99卷 621-641页

作者： Mills, Jason Islam, Mohammed Pearson, Wayne Gash, Bob Natl Res Council Canada NRC 1 Arctic AvePOB 12093 St John NL A1B 3T5 Canada

This paper presents the development of a dynamic positioning in ice validation platform (DPIVP) which is part of a larger research project aimed at developing dynamic positioning (DP) system technologies for ice-rich environments. One outcome is simulation software to aid research in this area. The DPIVP software was designed to realistically simulate the dynamics of ice-structure interactions for real-time applications and to validate components common to DP in ice simulations. The software consists of many components which the DPIVP ties together as a unified system. All components have well-defined interfaces. Many of them are also distributed, allowing execution on separate computers and/or CPUs which helps ensure real-time operation. These two characteristics also decreases coupling and encourages a more modular design with the benefit of easily substituting alternative component implementations without reprogramming the DPIVP. Alternate implementations are useful for conducting research in specific DP in ice areas without substantially changing the system, such as alternative ice force models, DP control algorithms, vessel models, 3D and 2D visualization, environment models, and data acquisition systems. The integrated system was tested and evaluated using unit testing, integration testing, and system testing. The completed system was also validated using test cases that match physical model tests;the results compared favorably. Although the software has some limitations, for example, validated ice-force models being limited to two vessels, and thus lacks the generality we wish, the end result is a working prototype that satisfies the research requirements and provides an architecture and framework for future development.

关键词： DP in ice dynamic positioning simulation numerical modeling software software development programming DP training ice force modeling Python distributed computing

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Performance Analysis of Work Stealing Strategies in Large-Scale Multithreaded computing

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2023年第4期33卷 1-23页

作者： Kielanski, Grzegorz Van Houdt, Benny Univ Antwerp Middelheimlaan1 B-2020 Antwerp Belgium

distributed systems use randomized work stealing to improve performance and resource utilization. In most prior analytical studies of randomized work stealing, jobs are considered to be sequential and are executed as a whole on a single server. In this article, we consider a homogeneous system of servers where parent jobs spawn child jobs that can feasibly be executed in parallel. When an idle server probes a busy server in an attempt to steal work, it may either steal a parent job or multiple child jobs. To approximate the performance of this system, we introduce a Quasi-Birth-Death Markov chain and express the performance measures of interest via its unique steady state. We perform simulation experiments that suggest that the approximation error tends to zero as the number of servers in the system becomes large. To further support this observation, we introduce a mean field model and show that its unique fixed point corresponds to the steady state of the Quasi-Birth-Death Markov chain. Using numerical experiments, we compare the performance of various simple stealing strategies as well as optimized strategies.

关键词： Matrix analytic methods distributed computing

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MHND: Multi-Homing Network Design Model for Delay Sensitive Applications

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IEICE TRANSACTIONS ON COMMUNICATIONS 2023年第11期E106B卷 1143-1153页

作者： Kawabata, Akio Chatterjee, Bijoy Chand Oki, Eiji Toyohashi Univ Technol Toyohashi 4418580 Japan South Asian Univ New Delhi India Kyoto Univ Kyoto 6068501 Japan

When mission -critical applications are provided over a network, high availability is required in addition to a low delay. This paper proposes a multi -homing network design model, named MHND, that achieves low delay, high availability, and the order guarantee of events. MHND maintains the event occurrence order with a multi -homing configuration using conservative synchronization. We formulate MHND as an integer linear programming problem to minimize the delay. We prove that the distributed server allocation problem with MHND is NP -complete. Numerical results indicate that, as a multi -homing number, which is the number of servers to which each user belongs, increases, the availability increases while increasing the delay. Noteworthy, two or more multi -homing can achieve approximately an order of magnitude higher availability compared to that of conventional single -homing at the expense of a delay increase up to two times. By using MHND, flexible network design is achieved based on the acceptable delay in service and the required availability.

关键词： delay sensitive service network design availability distributed computing conservative synchronization

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A Survey on Collaborative DNN Inference for Edge Intelligence

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Machine Intelligence Research 2023年第3期20卷 370-395页

作者： Wei-Qing Ren Yu-Ben Qu Chao Dong Yu-Qian Jing Hao Sun Qi-Hui Wu Song Guo College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing211100China Department of Computing Hong Kong Polytechnic UniversityHong Kong999077China

With the vigorous development of artificial intelligence(AI),intelligence applications based on deep neural networks(DNNs)have changed people’s lifestyles and production ***,the large amount of computation and data generated from the network edge becomes the major bottleneck,and the traditional cloud-based computing mode has been unable to meet the requirements of realtime processing *** solve the above problems,by embedding AI model training and inference capabilities into the network edge,edge intelligence(EI)becomes a cutting-edge direction in the field of ***,collaborative DNN inference among the cloud,edge,and end devices provides a promising way to boost ***,at present,EI oriented collaborative DNN inference is still in its early stage,lacking systematic classification and discussion of existing research *** by it,we have comprehensively investigated recent studies on EI-oriented collaborative DNN *** this paper,we first review the background and motivation of ***,we classify four typical collaborative DNN inference paradigms for EI,and analyse their characteristics and key ***,we summarize the current challenges of collaborative DNN inference,discuss future development trends and provide future research directions.

关键词： Artificial intelligence(AI) edge intelligence(EI) distributed computing deep neural network(DNN) collaborative inference

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A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第6期27卷 3014-3025页

作者： Cohen, Raphael Y. Y. Kovacheva, Vesela P. P. Harvard Med Sch Brigham & Womens Hosp Dept Anesthesiol Perioperat & Pain Med Boston MA 02115 USA PathAI Boston MA 02215 USA

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.

关键词： Artificial intelligence Medical services Data models Bioinformatics Python Microservice architectures Brain modeling distributed computing electronic healthcare records medical information systems

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Self-stabilizing c-wave algorithms for arbitrary networks

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computing 2023年第1期105卷 53-88页

作者： Karaata, Mehmet Hakan Alrashed, Ebrahim Allaho, Mohammad Kuwait Univ Dept Comp Engn 13060 POB 5969 Safat Kuwait

In this paper, we propose the first self-stabilizing c-wave algorithm for broadcasting large size messages from multiple initiator processes such that every process receives at least one of these messages broadcast by the initiators. The proposed algorithm first splits each message into a sequence of message shares and then broadcasts the message shares in a pipelined manner in order. We then present an improved maximally concurrent self-stabilizing c-wave algorithm called dynamic clusteringc-wavealgorithm that constructs dynamic broadcast trees by allowing a process to dynamically change its parent to a neighbouring process with the most message shares to implement a maximally concurrent broadcasting of large size messages. The improved algorithm is maximally concurrent in the sense that a process is allowed to receive a message share from any neighbouring process with a larger sequence of message shares than its current parent from which it receives message shares in the broadcast trees constructed by the initiators when available. We implemented and experimentally evaluated the c-wave algorithm to compare its message propagation delay to those of the m and the k-wave algorithms. Simulation results show that the proposed algorithm significantly reduces the broadcast propagation delays compared to that of the m and the k-wave algorithms. The improvement is more evident over the m-wave algorithm. Solutions to global-snapshots, distributed broadcast and various synchronization problems can be solved efficiently using c-wave algorithms. In addition, the proposed algorithms have applications in blockchain, mobile wireless sensor networks, VANETs and military communication networks.

关键词： c-wave distributed computing Multi-node broadcast m-wave Self-stabilization Wave algorithms

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Toward runtime support for unstructured and dynamic exascale-era applications

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JOURNAL OF SUPERcomputing 2023年第8期79卷 9245-9272页

作者： Thomadakis, Polykarpos Chrisochoides, Nikos Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

This paper presents an effort to mitigate overheads, latencies, and limitations observed in message-driven runtime frameworks, by utilizing lightweight threads tightly integrated with message passing. It also introduces new abstractions and features for group communication as well as fine-grained concurrency on top of remote method invocations to improve workload balancing in shared and distributed memory. We observe up to 100% difference in performance behavior for task creation and handling message passing. Evaluations on 1000 cores (25 nodes) of a distributed memory machine showed that the integration of fine-grained concurrency with the runtime achieves performance improvements of 12% on a seismic wave simulation benchmark, as opposed to 50% degradation with OpenMP. Moreover, a 3D mesh refinement application showed 50% improvement, exploiting multi-grain parallelism at data and task levels.

关键词： Runtime support software Lightweight threads distributed computing Dynamic applications Tasking

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Federated-Learning-Based Synchrotron X-Ray Microdiffraction Image Screening for Industry Materials

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第2期19卷 2228-2237页

作者： Chen, Bo Xu, Kang Zhu, Yongxin Tian, Li Chang, Victor Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Aston Univ Aston Business Sch Dept Operat & InformationManagement Birmingham B47ET England

Synchrotron X-ray microdiffraction (mu XRD) services are conducted for industrial minerals to identify their crystal impurities in terms of crystallinity and potential impurities. mu XRD services generate huge loads of images that have to be screened before further processing and storage. However, there are insufficient effective labeled samples to train a screening model since service consumers are unwilling to share their original experimental images. In this article, we propose a physics law-informed federated learning (FL) based mu XRD image screening method to improve the screening while protecting data privacy. In our method, we handle the unbalanced data distribution challenge incurred by service consumers with different categories and amounts of samples with novel client sampling algorithms. We also propose hybrid training schemes to handle asynchronous data communications between FL clients and servers. The experiments show that our method can ensure effective screening for industrial users conducting industrial material testing while keeping commercially confidential information.

关键词： Client sampling distributed computing federated learning (FL) industrial inspection machine learning synchrotron radiation X-ray

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RSP-gcForest: A distributed Deep Forest via Random Sample Partition

RSP-gcForest: A Distributed Deep Forest via Random Sample Pa...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Li, Mark Junjie Cai, Wenzhu Lin, Yigang Huang, Sunjie Huang, Joshua Zhexue Peng, Patrick Xiaogang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen Academy of Inspection and Quarantine China

ISBN: (纸本)9798350324457

Deep Forest, a powerful alternative to deep neural networks, has gained much attention due to its advantages, such as low complexity, minimal hyperparameter requirements, and strong application performance. In the current big data environment, where data volumes and model complexities are growing rapidly, distributed computing is needed to increase computational efficiency. Recently, a distributed deep forest approach, called BLB-gcForest (Bag of Little Bootstraps-gcForest), has been successful in reducing training instances within cascade forests, combining BLB and granularity segmentation, thus improving the computational efficiency and scalability of distributed deep forests. However, it still transmits the entire data set between layers and requires double sampling with BLB, limiting the amount of data and the scalability of resource utilization. This paper introduces a novel algorithm, RSP-gcForest, based on Random Sample Partition (RSP) to improve distributed deep forests computational efficiency and scalability. RSP-gcForest uses block-level samples that replace the full dataset, significantly reducing interlayer instance transmission and prediction within cascade forests. Additionally, RSP blocks are integrated with the segmentation granularity of cascade forests for ensemble learning, effectively addressing computational efficiency and resource constraints. We conducted experiments on four extensive datasets using Spark and evaluated performance across five key metrics. The results clearly show that RSP-gcForest, while maintaining high classification quality, surpasses state-of-the-art methods in terms of computational efficiency and resource utilization. Furthermore, it achieves superior load balancing, demonstrating its potential as a powerful tool in big data and distributed computing. © 2023 IEEE.

关键词： Deep forest distributed computing Ensemble Learning Random Sample Partition

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Privacy-Preserving Machine Learning on Apache Spark

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IEEE ACCESS 2023年 11卷 127907-127930页

作者： Brito, Claudia V. Ferreira, Pedro G. Portela, Bernardo L. Oliveira, Rui C. Paulo, Joao T. INESC TEC P-4200465 Porto Portugal Univ Minho Dept Informat P-4710057 Braga Portugal Univ Porto Fac Sci P-4099002 Porto Portugal

The adoption of third-party machine learning (ML) cloud services is highly dependent on the security guarantees and the performance penalty they incur on workloads for model training and inference. This paper explores security/performance trade-offs for the distributed Apache Spark framework and its ML library. Concretely, we build upon a key insight: in specific deployment settings, one can reveal carefully chosen non-sensitive operations (e.g. statistical calculations). This allows us to considerably improve the performance of privacy-preserving solutions without exposing the protocol to pervasive ML attacks. In more detail, we propose Soteria, a system for distributed privacy-preserving ML that leverages Trusted Execution Environments (e.g. Intel SGX) to run computations over sensitive information in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41% when compared to previous related work. Our protocol is accompanied by a security proof and a discussion regarding resilience against a wide spectrum of ML attacks.

关键词： Cluster computing Training Machine learning Hardware Task analysis Homomorphic encryption distributed computing Trusted computing Privacy-preserving machine learning distributed systems apache spark trusted execution environments Intel SGX

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