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14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Du, Ying Tan, Siqi Han, Kaifeng Jiang, Jiamo Wang, Zhiqin Chen, Li China Acad Informat & Commun Technol Beijing Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Anhui Peoples R China

ISBN: (纸本)9781665450850

Semi-supervised learning (SSL) has been applied to many practical applications over the past few years. Recently, distributed graph-based semi-supervised learning (DGSSL) has shown to have good performance. Traditional DGSSL algorithms usually have the problem of the straggler effect that algorithm execution time is limited by the slowest node. To solve this problem, a novel coded DGSSL(CDGSSL) algorithm based on the Maximum Distance Separable (MDS) code is proposed in this paper. Specifically, the proposed algorithm is based on the Maximum Distance Separable (MDS) code. In general, the proposed coded distributed algorithm is straggler-tolerant. Moreover, we provide optimal parameters design for the proposed algorithm. The superiority of the proposed algorithm has been confirmed via experiments on Alibaba Cloud Elastic Compute Service.

关键词： Coded computation distributed computing Maximum Distance Separable (MDS) code semi-supervised learning (SSL)

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A key performance indicator to analyze swarm learning performances with EHR 12

A key performance indicator to analyze swarm learning perfor...

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12th IEEE International Conference on Healthcare Informatics (IEEE-ICHI)

作者： Mantovani, Matteo Schede, Riccardo Combi, Carlo Diciotti, Stefano Univ Verona Dept Comp Sci Verona Italy Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (纸本)9798350383744;9798350383737

Swarm Learning (SL) has been recently proposed for distributed learning, where a group of individual centers perform a synchronized training. Unlike traditional machine learning models that rely on a central server, swarm learning distributes the learning process across multiple nodes. Each node independently processes data and contributes to the overall learning task. This collaboration allows the swarm to benefit from individual nodes' different data. Unlike federated learning, here model parameters are not handled by a central server but are randomly handled across each individual node. The intrinsic attention of swarm learning to data privacy makes it suitable for distributed healthcare analysis, where a clinical center wants to benefit from all the other ones in the swarm network. However, the benefit for a single center or for the whole network could vary depending on data distribution. In this paper, we want to analyze the performance of the swarm learning in a network with multiple nodes, where different data distribution scenarios are taken into account. This analysis will show the gain of the whole swarm network and a specific (reference) node, focusing on scenarios where this node has a different amount of data with respect to the other nodes. To perform a more analytical analysis, we introduce a new Key Performance Indicator (KPI) to measure such gain. We then applied this method using ICU data extracted from the MIMIC EHR database and discussed the results obtained by analyzing 5 nodes with different data distribution scenarios.

关键词： swarm learning performance analysis KPI EHR distributed computing

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Mining Profitability in Bitcoin: Calculations of User-Miner Equilibria and Cost of Mining 1

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24th International Conference on distributed Applications and Interoperable Systems (DAIS)

作者： Tedeschi, Enrico Nohr, Oyvind Arne Moen Dagenborg, Havard Johansen, Dag UiT Arctic Univ Norway N-9019 Tromso Norway

ISBN: (数字)9783031626388

ISBN: (纸本)9783031626371;9783031626388

This paper examines the equilibrium between user transaction fees and miner profitability within proof-of-work-based blockchains, specifically focusing on Bitcoin. We analyze the dependency of mining profit on factors such as transaction fee adjustments and operational costs, particularly electricity. By applying a multidimensional profitability model and performing a sensitivity analysis, we evaluate the potential for profit maximization through operational cost reduction versus fee increases. Our model integrates variable electricity costs, market-driven Bitcoin prices, mining hardware efficiency, network hash rate, and transaction fee elasticity. We show that mining strategies aimed at reducing electricity expenses are far more profitable than pursuing transactions with higher fees.

关键词： Blockchain Bitcoin Proof-of-Work Mining Strategies Profitability Electricity Cost distributed computing

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Parking Problem by Oblivious Mobile Robots in Infinite Grids 1

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20th International Conference on distributed computing and Intelligent Technology (ICDCIT)

作者： Chakraborty, Abhinav Mukhopadhyaya, Krishnendu Siksha O Anusandhan Univ Inst Tech Educ & Res Dept Comp Sci & Engn Bhubaneswar Odisha India Indian Stat Inst Adv Comp & Microelect Unit Kolkata India

ISBN: (数字)9783031505836

ISBN: (纸本)9783031505829;9783031505836

In this paper, the parking problem of a swarm of mobile robots has been studied. The robots are deployed at the nodes of an infinite grid, which has a subset of prefixed nodes marked as parking nodes. Each parking node p(i) has a capacity of k(i) which is given as input and equals the maximum number of robots a parking node can accommodate. As a solution to the parking problem, robots need to partition themselves into groups so that each parking node contains a number of robots that are equal to the capacity of the node in the final configuration. It is assumed that the number of robots in the initial configuration represents the sum of the capacities of the parking nodes. The robots are assumed to be autonomous, anonymous, homogeneous, identical and oblivious. They operate under an asynchronous scheduler. They neither have any agreement on the coordinate axes nor do they agree on a common chirality. All the initial configurations for which the problem is unsolvable have been identified. A deterministic distributed algorithm has been proposed for the remaining configurations, ensuring the solvability of the problem.

关键词： distributed computing Mobile Robots Look-Compute-Move Cycle Asynchronous Infinite Grid Parking Nodes

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JARVIS: Disjoint Large Language Models on Radio VLANs for Intelligent Services

JARVIS: Disjoint Large Language Models on Radio VLANs for In...

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2024 Military Communications Conference

作者： Perello, Miguel Sirera Groen, Joshua Liu, Wan Ioannidis, Straus Chowdhury, Kaushik Northeastern Univ Inst Wireless Internet Things Boston MA 02115 USA

ISBN: (纸本)9798350374247;9798350374230

Large Language Models (LLMs) have changed the way we access and interpret information, communicate with each other and even operate computer systems through autonomous code generation. Typically, these billion-parameter models rely on cloud storage and execution due to their computational demands. In this paper, we challenge this status quo by proposing JARVIS, a distributed LLM framework that splits model layers across edge devices with limited compute resources, trading off computation for increased peer-level communication. JARVIS is robust to individual node failures, including recovery methods for lost layers via peer-level duplication. We evaluate JARVIS using Google's open-source Gemma LLM (2B parameters) deployed over 18 software-defined radios in the NSF Colosseum RF emulator. Our evaluation explores LLM performance degradation from node losses, providing insights into node prioritization in tactical environments. The JARVIS software code is released for community exploration and adoption.

关键词： Artificial Intelligence Generative AI Large Language Models Network Slicing distributed computing

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The Event Horizon Science Gateway

The Event Horizon Science Gateway

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Practice and Experience in Advanced Research computing (PEARC)

作者： Quick, Rob Shelton, Esen Gokpinar Wang, Jun West, Andrew Thomas Indiana Univ Pervas Technol Inst Indianapolis IN 46204 USA Indiana Univ Pervas Technol Inst Blomington IN USA Univ Arizona Steward Observ Tucson AZ USA

ISBN: (纸本)9798400704192

The Event Horizon Telescope (EHT) recently used 10 petabyte-scale observation data to construct the first images of black holes and 100 terabyte-scale simulation data to constrain the plasma properties around supermassive black holes. This work leveraged the Open Science Grid (OSG) high throughput resources provided by the Partnership to Advance Throughput computing (PATh). While EHT has successfully utilized PATh to create the most extensive black hole simulation library to date, the broad adoption of this resource for data processing has been slower. The sophisticated command-line-driven HTCondor environment creates barriers for less technical researchers, limiting PATh's reach and impact on the broader astronomy and science communities. In May of 2023, the Cyberinfrastructure Integration Research Center (CIRC) at Indiana University was awarded an NSF EAGER award to collaborate with EHT and PATh in implementing a targeted science gateway instance that integrates critical EHT application functionality to leverage OSG within the Apache Airavata framework. The project leverages modern state-of-the-art User Experience (UX) techniques and participatory design methods to lower the barrier to adopting OSG resources for researchers trying to discover the properties of black holes.

关键词： distributed computing Science Gateway User Interface Design High Throughput computing Apache Airavata

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A Platform for Large Scale Statistical Modelling in R

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Journal of Data Science 2024年第2期22卷 208-220页

作者： Cairns, Jason Urbanek, Simon Murrell, Paul Department of Statistics University of Auckland Auckland New Zealand

With the growing scale of big datasets, fitting novel statistical models on larger-than-memory datasets becomes correspondingly challenging. This document outlines the development and use of an API for large scale modelling, with a demonstration given by the proof of concept platform largescaler, developed specifically for the development of statistical models for big datasets. © 2024 The Author(s).

关键词： big data distributed computing modelling

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NetCL: A Unified Programming Framework for In-Network computing 24

NetCL: A Unified Programming Framework for In-Network Comput...

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2024 International Conference for High Performance computing, Networking, Storage and Analysis

作者： Karlos, George Bal, Henri Wang, Lin Vrije Univ Amsterdam Amsterdam Netherlands Paderborn Univ Paderborn Germany

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

The emergence of programmable data planes (PDPs) has paved the way for in-network computing (INC), a paradigm wherein networking devices actively participate in distributed computations. However, PDPs are still a niche technology, mostly available to network operators, and rely on packet-processing DSLs like P4. This necessitates great networking expertise from INC programmers to articulate computational tasks in networking terms and reason about their code. To lift this barrier to INC we propose a unified compute interface for the data plane. We introduce C/C++ extensions that allow INC to be expressed as kernel functions processing in-flight messages, and APIs for establishing INC-aware communication. We develop a compiler that translates kernels into P4, and thin runtimes that handle the required network plumbing, shielding INC programmers from low-level networking details. We evaluate our system using common INC applications from the literature.

关键词： distributed computing In-network computing Programmable data plane Programming languages Compilers

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hZCCL: Accelerating Collective Communication with Co-Designed Homomorphic Compression 24

hZCCL: Accelerating Collective Communication with Co-Designe...

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2024 International Conference for High Performance computing, Networking, Storage and Analysis

作者： Huang, Jiajun Di, Sheng Yu, Xiaodong Zhai, Yujia Liu, Jinyang Jian, Zizhe Liang, Xin Zhao, Kai Lu, Xiaoyi Chen, Zizhong Cappello, Franck Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA Argonne Natl Lab Lemont IL 60439 USA Stevens Inst Technol Hoboken NJ 07030 USA Univ Kentucky Lexington KY USA Florida State Univ Tallahassee FL 32306 USA Univ Calif Merced Merced CA USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

As network bandwidth struggles to keep up with rapidly growing computing capabilities, the efficiency of collective communication has become a critical challenge for exa-scale distributed and parallel applications. Traditional approaches directly utilize error-bounded lossy compression to accelerate collective computation operations, exposing unsatisfying performance due to the expensive decompression-operation-compression (DOC) workflow. To address this issue, we present a first-ever homomorphic compression-communication co-design, hZCCL, which enables operations to be performed directly on compressed data, saving the cost of time-consuming decompression and recompression. In addition to the co-design framework, we build a light-weight compressor, optimized specifically for multi-core CPU platforms. We also present a homomorphic compressor with a run-time heuristic to dynamically select efficient compression pipelines for reducing the cost of DOC handling. We evaluate hZCCL with up to 512 nodes and across five application datasets. The experimental results demonstrate that our homomorphic compressor achieves a CPU throughput of up to 379.08 GB/s, surpassing the conventional DOC workflow by up to 36.53x. Moreover, our hZCCL-accelerated collectives outperform two state-of-the-art baselines, delivering speedups of up to 2.12x and 6.77x compared to original MPI collectives in single-thread and multi-thread modes, respectively, while maintaining data accuracy.

关键词： Collective Communication Homomorphic Compression distributed computing Parallel Algorithm

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SMASK: Parallel Probabilistic Privacy-Preserving Frequent Pattern Mining Technique for Big Data 3rd

SMASK: Parallel Probabilistic Privacy-Preserving Frequent Pa...

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3rd International Conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Sharma, Shivani Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala India

ISBN: (纸本)9783031640759;9783031640766

This paper introduces a spark-based fast solution for privacy-preserving frequent pattern mining problems for big data. Spark Resilient distributed Dataset (RDD) framework has been used to implement the Mask approach, which uses the probabilistic distortion method for maintaining data privacy while mining frequent patterns. The masking technique shows very promising results in terms of privacy and utility both. However, due to sequential nature limits the application to small or medium size data. The spark-based proposed technique introduces two-level parallelization i.e. data and algorithmic level which in turn paves a way to gain faster analytical results in a bounded amount of time while dealing with a large volume of datasets. This makes the application feasible for the current growth of data size. A number of experiments have been conducted to compare the performance of the proposed scheme with benchmark parallel approaches in terms of privacy, utility, and time complexity over real and simulated data sets. It has been observed that the proposed scheme preserves the privacy of sensitive data while maintaining utility within a real bound of time. Experiments show that the proposed Spark-based scheme i.e. S-Mask gains 16 times speedup on average over different benchmark data sets and maintains a desired ratio between privacy and utility of the data.

关键词： Spark distributed computing Parallelization Constraint-based data mining Sensitive Pattern Hiding Privacy Preservation

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