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Accelerating distributed Training through In-network Aggregation with Idle Resources in Data Centers 29

Accelerating Distributed Training through In-network Aggrega...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Wang, Ge Tu, Huaqing Zhao, Gongming Xu, Hongli University of Science and Technology of China China Zhejiang Lab China

ISBN: (纸本)9798350330717

As the parameter scale of large-scale models continues to increase, distributed model training imposes significant communication overhead in data centers, resulting in reduced training efficiency. To address this challenge, a promising solution, called in-network aggregation, is proposed to mitigate the bandwidth bottleneck in data centers by aggregating gradients within network. However, existing works mainly rely on programmable switches to implement in-network aggregation, while programmable switches have limited memory resources, which makes it hard to storage parameter with large size. Moreover, programmable switches are not yet widely deployed in current data centers, resulting in poor availability. To overcome this limitation, we leverage servers with idle resources in data centers for in-network aggregation, since servers have more powerful storage capabilities compared with programmable switches. Specifically, we formally formulate the problem of server-based in-network aggregation. An efficient approximate algorithm with bounded approximation factor is proposed to select servers with idle resources and paths for model aggregation. Our extensive simulations show that our proposed method can reduce communication time by 38.4%-60.1% compared to state-of-the-art solutions. © 2023 IEEE.

关键词： Data Center Network distributed Model Training In-network Aggregation

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A distributed Technique of Optimization Problems Solving Based on Efficient Workload Assignment 27th

A Distributed Technique of Optimization Problems Solving Ba...

引用

27th international conference on distributed Computer and Communication Networks, DCCN 2024

作者： Klimenko, Anna Institute of IT and Security Technologies Russian State University for Humanities Moscow Russia

ISBN: (纸本)9783031808524

Currently, the efficiency improvement of complex technical systems is an urgent scientific task, which in numerous cases is formalized via optimization problem and solved by some well-known optimization methods. Metaheuristics are used intensively in this field due to their possibility to generate acceptable solutions in restricted time periods. However, it is known that with the decrease of performing time the solution accuracy of metaheuristics degrades, as well as algorithm’s convergence depends on its exploitation/exploration features and preliminary set parameters. parallel metaheuristics implementations are used to improve algorithms performance. In this paper a distributed technique of computationally-hard optimization problems solving based on efficient workload assignment is presented and described. The novelty of the technique proposed is that the additional procedure of metaheuristics instances forming and distribution is added, which creates metaheuristic instances of a various computational complexity and assigns them to the most suited computing resources to perform parallel independent runs within the operation time restriction. The workload generated by metaheuristics blocks is distributed efficiently by means of metaheuristics portfolio usage. The latter makes it possible to improve the metaheuristics instances distribution and computational complexities forming so as to put the largest metaheuristic block to the node with the highest performance with the iterative improvement of the distribution, considering the full set of computing resources constraints and criteria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Scalable High-Performance Community Detection Using Label Propagation in Massive Networks 16th

Scalable High-Performance Community Detection Using Label Pr...

引用

16th international conference on Social Networks Analysis and Mining

作者： Boddu, Sharon Khan, Maleq Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

ISBN: (纸本)9783031785405;9783031785412

Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze complex systems in the real world and has applications in many areas, including network science, data mining, and computational biology. Label propagation is a community detection method that is simpler and faster than other methods such as Louvain, InfoMap, and spectral-based approaches. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable for dealing with such large networks. This paper presents distributed-memory and hybrid parallel community detection algorithms based on the label propagation method. We incorporated novel optimizations and communication schemes, leading to very efficient and scalable algorithms. We also discuss various load-balancing schemes and present their comparative performances. These algorithms have been implemented and evaluated using large high-performance computing systems. Our hybrid algorithm is scalable to thousands of processors and has the capability to process massive networks. This algorithm was able to detect communities in the Metaclust50 network, a massive network with 282 million nodes and 42 billion edges, in 654 s using 4096 processors.

关键词： Community detection parallel graph algorithms network analysis graph mining

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Online reward adaptation for MDP-based distributed missions

Online reward adaptation for MDP-based distributed missions

引用

international conference on Unmanned Aircraft systems (ICUAS)

作者： Hamadouche, Mohand Dezan, Catherine Espes, David Branco, Kalinka Univ Bretagne Occidentale CNRS UMR 6285 Lab STICC F-29200 Brest France Univ Sao Paulo Sao Carlos SP Brazil

ISBN: (纸本)9798350310375

Unmanned aerial vehicles are increasingly used in environments where human intervention is difficult, repetitive, and dangerous. They greatly improve mission quality, productivity, and safety. Mission management of these increasingly complex autonomous vehicles requires independent and online decisions. Markov decision processes (MDPs) are the most widely used probabilistic decision models for describing, modeling, and solving decision-making problems under uncertainty. In order to take into account the physical constraints and safety requirements of the mission, parallel decision models are required with an increase in mission complexity. However, the parallel execution of several MDPs can lead to conflicts. This paper describes a self-adaptation method for resolving conflicts that arise during the mission of a UAV swarm modeled with Markov decision processes (MDPs). The decisions must be taken in priority by the UAV itself but in some cases, it does not have the global view to choose the most adapted to the mission. The proposed method is able to detect and resolve conflicts based on two main phases. The first is the detection of conflicting UAV members by the embedded edge devices. Second, each UAV adjusts its mission plan to avoid conflicts in the swarm. To illustrate the methodology, experimental results obtained with a UAV swarm system performing a target search and tracking mission are presented. Our solution has low overhead and significantly improves the swarm's lifetime, safety, and mission efficiency.

关键词： Self adaptation Reward adaptation conflict resolution Decision-Making Markov Decision Process

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Discovery of breakout patterns in financial tick data via parallel stream processing with in-order guarantees 23

Discovery of breakout patterns in financial tick data via pa...

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17th ACM international conference on distributed and Event-based systems, DEBS 2023

作者： Kalogerakis, Stefanos Magoutis, Kostas Computer Science Department University of Crete Heraklion Greece

ISBN: (纸本)9798400701221

In this paper we describe a parallel solution to the problem of discovering breakout patterns in continuously evaluated exponential-moving averages (EMA) of financial tick data streams over tumbling windows, achieving in-order processing of stock quotes. Our solution extends a first system we developed for this problem that used sequential ingestion of tick data to correctly preserve the per symbol ordering while placing them to their corresponding time window. The solution described in this paper allows for parallel ingestion of tick data streams to increase throughput, while ensuring order via a broadcast mechanism that propagates necessary ordering metadata to downstream operators. Evaluation of our prototype on a 32-core cluster of high-performance servers shows that our parallel prototype scales well (although sublinearly, given the constraint of maintaining in-order guarantees), and yields up to ∼1.73x faster processing compared to the sequential-ingest version when each is deployed on 32 cores over 4 servers. © 2023 ACM.

关键词： Finance

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Job Scheduling for HPC Clusters: Constraint Programming vs. Backfilling Approaches 18

Job Scheduling for HPC Clusters: Constraint Programming vs. ...

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18th international conference on distributed and Event-Based systems (DEBS)

作者： Goponenko, Alexander, V Lamar, Kenneth Allan, Benjamin A. Brandt, James M. Dechev, Damian Univ Cent Florida Orlando FL 32816 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9798400704437

Scheduling problems are naturally formulated in terms of Constraint Programming (CP), yet the application of CP-based approaches for job scheduling on High-Performance Computing (HPC) clusters remains underexplored. This study aims to bridge this gap by analyzing the scheduling of diverse real workload traces using IBM ILOG CP Optimizer. The analysis considers not just the basic metrics Average Bounded Slowdown and Average Response Time, but also Area-Weighted Average Response Time and Level-2 Priority-Weighted Specific Time, which measure packing efficiency and fairness. For each workload trace and metric combination, schedules produced through optimizing the metric with CP Optimizer are compared against schedules generated by the variants of the list scheduling with backfilling that are most suitable for the metric. The CP-based scheduling improves scheduling quality in most of the examined cases. The analysis of the metrics that represent different scheduling goals uncovers several non-trivial insights. Presently, CP Optimizer still encounters scalability issues with large wait queues and non-linear objective functions. Nonetheless, it often demonstrates improvements in packing efficiency, which make CP techniques attractive, particularly in scenarios where high-maintenance clusters run a moderate number of large, rigid, well-characterized jobs.

关键词： high-performance computing parallel job scheduling schedule quality constraint programming CP Optimizer packing efficiency fairness weighted average response time

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2024 3rd international conference on New Energy, Energy Storage and Power Engineering, NESP 2024

2024 3rd International Conference on New Energy, Energy Stor...

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2024 3rd international conference on New Energy, Energy Storage and Power Engineering, NESP 2024

The proceedings contain 68 papers. The topics discussed include: effect of the downstream blockage induced under-rib convection on oxygen feeding in a PEMFC with parallel flow field;simulation and analysis of the detection effect of seafloor shallow thermal origin gas release by marine resistivity method;dual-objective optimal scheduling of grid-connected distributed photovoltaic systems considering life loss cost of energy storage;research on security protection strategies for household distributed photovoltaic clusters;short-term wind speed prediction based on time series trends and periodic characteristics;experimental research on heating performance of solar energy coupled with water source heat pump system;research on variable pressure oil supply servo system based on accumulator;and optimal scheduling of building integrated energy system considering the interaction of electric energy and heat energy.

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A Multi-objective Approach for Solving distributed Job Shop Scheduling Problems 1

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4th international conference on Optimization Learning Algorithms and Applications

作者： dos Santos, Francisco Costa, Lino Varela, Leonilde Univ Minho ALGORITMI Res Ctr LASI Braga Portugal Kimpa Vita Univ Polytech Inst Uige Angola Univ Minho Dept Prod & Syst Braga Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

Nowadays, the industrial market is characterised by high levels of competition, with customers increasingly demanding in terms of quality, delivery times, costs, etc.. However, with increasing demand and the need to increase productivity, many companies in recent years have dedicated themselves to decentralising their factories, thus moving to distributed production. Today's manufacturing systems are distributed in the sense that there are several jobs that have to be carry out on machines located in different factories. This paper proposes a multi-objective distributed job shop scheduling model with unrelated parallel machines and sequence-dependent setup times. The transport time of raw materials to carry out a given job to a factory is also taken into account. Small instances of the problem were solved using NSGA-III with the aim of simultaneously minimising two objectives: the makespan and average completion time. Preliminary results show the validity of this approach.

关键词： distributed job shop scheduling problem multi-objective optimization evolutionary algorithms

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Exploiting Spatial and Temporal Features for Deep Learning Based Human Activity Recognition 29

Exploiting Spatial and Temporal Features for Deep Learning B...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Cao, Wenying Shi, Gaotao Zhang, Tieguan Tianjin University International Engineering Institute Tianjin China Tianjin University College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350330717

Human activity recognition using inertial sensors has gained significant popularity and widespread adoption in various fields, while deep learning has emerged as the dominant approach, playing a pivotal role in enhancing performance. Nevertheless, existing algorithms often neglect the heterogeneity of sensors and fail to effectively extract contextual features from long-term time series data, resulting in low accuracy in activity recognition, especially for complex activities. This paper proposes a novel method called FUsion Transformer hUman activity REcognition(FUTURE). FUTURE exploits the feature fusion mechanism and designs a multi-scaled DenseNet to extract the spatial features so that the relationship between heterogeneous sensors can be captured. Furthermore, a customized multi-head attention model with less computational complexity is employed in FUTURE to capture global dependencies within the sensor data. Experimental results on multiple datasets validate that FUTURE achieves an average recognition accuracy of 95% for complex activities and the evaluation against the state-of-the-art demonstrates the superior performance of the FUTURE model in accurately classifying complex activities. © 2023 IEEE.

关键词： Attention mechanism Feature fusion Human Activity Recognition Inertial sensor

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Optimized Deep Learning Models For Edge Computing: A Comparative Study on Raspberry PI4 For Real-Time Plant Disease Detection 4

Optimized Deep Learning Models For Edge Computing: A Compara...

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4th international conference on Embedded and distributed systems, EDiS 2024

作者： Saad, Abdelmadjid Aidi, Abdeldjallil Benyamina, Abou El Hassan University of Oran 1 Ahmed Ben Bella Laboratory of Parallel Embedded Architectures and Intensive Computing Computer Science Department Oran Algeria University of Tiaret Ibn Khaldoun Laboratory of Mathematics and Computer Science Computer Science Department Tiaret Algeria

ISBN: (纸本)9798331519872

In the pursuit of precision agriculture, the integration of artificial intelligence (AI) for real-time plant disease detection holds a significant promise. This study investigates the application of edge computing on a Raspberry Pi 4 platform to keep the performance of three optimized deep learning models: GoogLeNet, ResNet and XceptionNet, in detecting plant diseases using the new plant diseases dataset. The goal seeks to bridge the gap between theoretical advancements in deep learning and practical applications in precision agriculture, shedding light on the trade-offs between model complexity, computational demands, and diagnostic accuracy. Additionally, we provide a lightweight web application that interfaces with the trained models, exemplifying how edge computing can democratize access to AI tools, making them accessible to farmers and researchers. Our findings show that converting the models to TensorFlow Lite significantly reduces model size and inference time while maintaining similar performance, crucial for applications on resource-limited devices like the Raspberry Pi 4. © 2024 IEEE.

关键词： Smart agriculture

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