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Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices 24

Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for...

引用

30th international conference on Mobile computing and networking (ACM MobiCom)

作者： Ye, Shengyuan Zeng, Liekang Chu, Xiaowen Xing, Guoliang Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China HKUST GZ Data Sci & Analyt Thrust Guangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798400704895

On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-preserving machine learning at the edge. However, the intensive training workload and limited onboard computing resources pose significant challenges to the availability and efficiency of model training. While existing works address these challenges through native resource management optimization, we instead leverage our observation that edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources beyond a single terminal. We propose Asteroid, a distributed edge training system that breaks the resource walls across heterogeneous edge devices for efficient model training acceleration. Asteroid adopts a hybrid pipeline parallelism to orchestrate distributed training, along with a judicious parallelism planning for maximizing throughput under certain resource constraints. Furthermore, a fault-tolerant yet lightweight pipeline replay mechanism is developed to tame the device-level dynamics for training robustness and performance stability. We implement Asteroid on heterogeneous edge devices with both vision and language models, demonstrating up to 12.2x faster training than conventional parallelism methods and 2.1x faster than state-of-the-art hybrid parallelism methods through evaluations. Furthermore, Asteroid can recover training pipeline 14x faster than baseline methods while preserving comparable throughput despite unexpected device exiting and failure.

关键词： Edge intelligence distributed machine learning data parallelism pipeline parallelism hybrid parallelism

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A Task-Resource Joint Optimization Model in distributed computing 1st

A Task-Resource Joint Optimization Model in Distributed Comp...

引用

1st international conference on Emerging networking Architecture and Technologies (ICENAT)

作者： Su, Shi Hui, Hongwen Wei, Wenjie Beijing Normal Univ Expt High Sch 14 XiCheng Dist Beijing 100032 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn 30 Xueyuan Dist Beijing 100083 Peoples R China

ISBN: (纸本)9789811996962;9789811996979

distributed computing decomposes the user tasks into many small tasks and distributes them to multiple servers for processing to save the overall computing time. There is an important challenge that how to efficiently divide the tasks to achieve the goal. In this paper, aiming at solving the complex relationship between multiple tasks and multiple servers, we propose a resource-task joint optimization model in a distributed computing environment. The optimization goal of this model is to minimize the total computation time, while considering the transmission time and execution time of task data. Based on the task size, intensity, and computing power of the server, we propose a task-resource joint optimization algorithm to solve the proposed model. Numerical simulation verified the feasibility of the model and the correctness of the algorithm.

关键词： distributed computing Resource-task joint optimization Computation time

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Towards an Optimized Heterogeneous distributed Task Scheduler in OpenMP Cluster

Towards an Optimized Heterogeneous Distributed Task Schedule...

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2024 Workshops of the international conference for High Performance computing, networking, Storage and Analysis, SC Workshops 2024

作者： Neveu, Remy Ceccato, Rodrigo Leite, Gustavo Araujo, Guido Diaz, Jose M. Monsalve Yviquel, Herve Instituto de Computação Campinas Brazil

ISBN: (纸本)9798350355543

This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication *** work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts;second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments;and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods. © 2024 IEEE.

关键词： distributed architectures Parallel systems Scheduling and task partitioning

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Proceedings of QCS 2021: 2nd international Workshop on Quantum computing Software, Held in conjunction with SC 2021: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of QCS 2021: 2nd International Workshop on Quant...

引用

2nd international Workshop on Quantum computing Software, QCS 2021

ISBN: (纸本)9781728186740

The proceedings contain 13 papers. The topics discussed include: quantum algorithms and simulation for parallel and distributed quantum computing;tensor network circuit simulation at exascale;Illinois express quantum network for distributing and controlling entanglement on metro-scale;exploring affine abstractions for qubit mapping;scalable programming workflows for validation of quantum computers;and mapping constraint problems onto quantum gate and annealing devices.

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Legate Sparse: distributed Sparse computing in Python 23

Legate Sparse: Distributed Sparse Computing in Python

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2023 international conference for High Performance computing, networking, Storage and Analysis, SC 2023

作者： Yadav, Rohan Lee, Wonchan Elibol, Melih Papadakis, Manolis Lee-Patti, Taylor Garland, Michael Aiken, Alex Kjolstad, Fredrik Bauer, Michael Stanford University Stanford United States NVIDIA Santa Clara United States

ISBN: (纸本)9798400701092

The sparse module of the popular SciPy Python library is widely used across applications in scientific computing, data analysis and machine learning. The standard implementation of SciPy is restricted to a single CPU and cannot take advantage of modern distributed and accelerated computing resources. We introduce Legate Sparse, a system that transparently distributes and accelerates unmodified sparse matrix-based SciPy programs across clusters of CPUs and GPUs, and composes with cuNumeric, a distributed NumPy library. Legate Sparse uses a combination of static and dynamic techniques to efficiently compose independently written sparse and dense array programming libraries, providing a unified Python interface for distributed sparse and dense array computations. We show that Legate Sparse is competitive with single-GPU libraries like CuPy and achieves 65% of the performance of PETSc on up to 1280 CPU cores and 192 GPUs of the Summit supercomputer, while offering the productivity benefits of idiomatic SciPy and NumPy. © 2023 Owner/Author(s).

关键词： Libraries

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distributed Self-Orchestration System based on Home 5G Concept 12

Distributed Self-Orchestration System based on Home 5G Conce...

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12th international conference on Cloud networking (CloudNet)

作者： Babou, Cheikh Saliou Mbacke Owada, Yasunori Inoue, Masugi Takizawa, Kenichi Kuri, Toshiaki Natl Inst Informat & Commun Technol 4-2-1 Nukui Kitamachi Koganei Tokyo 1848795 Japan Natl Inst Informat & Commun Technol 2-1-3 Katahira Aoba Sendai Miyagi 9800812 Japan

ISBN: (纸本)9798350313062;9798350313079

Autonomous orchestration in distributed systems is a solution that attracts attention in 5G and beyond. Indeed, proposing a distributed orchestrator system with an Edge Cloud network can ensure autonomous management and service continuity, even when remote resources are not reachable. This feature is one of the key advantages of fifth-generation (5G and beyond) cellular networks, as stated by 3GPP (release 18), particularly regarding Proximity-based Service (ProSe), service management, and so on. Therefore, there is an urgent need to address the current gap in the edge cloud system to achieve greater proximity and distributed self-management, thereby providing services closer to the users. This is crucial for meeting the requirements of real-time applications such as gaming, Virtual Reality, augmented reality, vehicle-based services, etc. This paper proposes a self-orchestrator system within the concept of Home 5G architecture, based on 3-tier edge cloud networks, known as Home Edge computing (HEC). We validate our proposal using a Vehicle-to-vehicle-to-Home Server (V2V2H) use case to evaluate the latency of content sharing between end-devices and the HEC server.

关键词： Home 5G HEC Autonomous Edge Cloud ProSe Self-orchestrator V2V2I D2D HgNB

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BalanceNet Orchestrator: A KQV-based Dynamic Task Allocation for distributed Deep Learning 38

BalanceNet Orchestrator: A KQV-based Dynamic Task Allocation...

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38th international conference on Information networking (ICOIN)

作者： Sun, Teh-Jen Ngo, Thien-Thu Huh, Eui-Nam Kyung Hee Univ Dept Artificial Intelligence Yongin South Korea Kyung Hee Univ Dept Comp Sci & Engn Yongin South Korea

ISBN: (纸本)9798350330946;9798350330953

In Artificial Intelligence(AI), training expansive models with billions of parameters necessitates substantial computational resources. This requirement has led to the adoption of parallel computing frameworks. However, these frameworks often confront node performance imbalances due to disparities in computational capabilities and network conditions. To address this issue, we introduce the BalanceNet Orchestrator(BNO), a dynamic task allocation algorithm designed to equilibrate workloads in parallel training environments. BalanceNet Orchestrator assesses and adjusts to node-specific performance in real time, facilitating optimal workload distribution and resource utilization. This method significantly enhances training efficiency and accelerates model convergence, presenting an efficient approach for training large-scale AI models within parallel training architecture.

关键词： AI Parallel training distributed heterogeneous environment

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Architectural Elements of Decentralized Process Management Systems 22nd

Architectural Elements of Decentralized Process Management S...

引用

22nd international conference on Service Oriented computing

作者： Grunert, Kai Shoferi, Janis Joderi Gold, Lucas Rieder, Wolf Kuepper, Axel Tech Univ Berlin Serv Ctr Networking Berlin Germany

ISBN: (纸本)9789819608041;9789819608058

In a decentralized Process Management System, several process engines cooperate to execute a single process instance by using direct Machine-to-Machine communication and local coordination of the process flow. In this paper, we analyze the software architecture elements of a decentralized Process Management System. We explain the involved components, connectors, data, and the relationships between them. We also describe the state transitions of decentralized processes during execution.

关键词： decentralized Process Management System distributed Process Engines distributed and Heterogeneous Workflow Enactment Service Business Process Management System Process Automation Process Execution System Process Engine

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Enhanced Slicing based Deep Learning architecture for 5G networks in distributed Learning 15

Enhanced Slicing based Deep Learning architecture for 5G net...

引用

15th international conference on computing Communication and networking Technologies, ICCCNT 2024

作者： Dadhich, Atul Shankar, Amar Kumari, Aarsi Department of Electrical Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science & IT ARKA JAIN University Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper gives a distributed deep getting-to-know (DDL) architecture for 5G community cutting. DDL is an allotted method which has been a success in schooling massive-scale deep mastering fashions. It applies the ideas of disbursed computing to reduce the dimensions of the statistics being trained at the same time as education samples in parallel on different computing assets. The proposed solution in this paper exploits the improvements in 5G networks' virtualization skills, which include community cutting, to provide a green architecture which allows DDL fashions to be taught with dispensed resources to limit the training time. The proposed structure consists of three predominant modules: a useful resource-cutting manager, a dispensed training cluster, and a dispensed inference cluster. The resource-reducing manager allows dynamic aid cutting throughout numerous assets (e.g. GPU, CPU). The distributed schooling cluster includes workers distributing the training duties across the sources. The allotted inference cluster is used to perform inference on the trained fashions throughout more than one allotted computing source. The advantages of the proposed structure are that it allows green disbursed schooling, green disbursed inference, and value-green education of complex deep gaining knowledge of models. As such, it may be used to educate huge and complex models with decreased prices and also boost the schooling throughput. Additionally, 5G community cutting presents flexibility in useful resource provision. ©2024 IEEE.

关键词： 5G mobile communication systems

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Implementation of a Decentralised Platform for Digital File Verification and Sharing using Solana Blockchain and IPFS 4

Implementation of a Decentralised Platform for Digital File ...

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4th international conference on Pervasive computing and Social networking (ICPCSN)

作者： Goel, Ayush Jain, Madhur Kayalvizhi, R. SRM Inst Sci & Technol Dept Networking & Commun Chennai Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

In recent years, the usage of digital files has increased and therefore data theft has also increased. Traditional centralized platforms often suffer from vulnerabilities such as single points of failure and lack of transparency. In response, decentralized technologies such as Interplanetary File System (IPFS) and blockchain have emerged as promising solutions. This research study presents a novel decentralized platform built upon the Solana blockchain and IPFS, designed to address the shortcomings of centralized systems. The proposed platform uses the transparency and immutability of the Solana blockchain network to provide a secure ledger for file verification. In addition to verification, the platform facilitates seamless sharing of digital files among users through IPFS, a distributed file system. By utilizing IPFS's decentralized architecture, data is stored across a chain of nodes, eliminating a single centralized server. It not only enhances data resilience and availability but also mitigates the risk of censorship and data manipulation. This paper proposes using Solana blockchain instead of traditional Ethereum blockchain for better scalability because Solana supports more than 2000 TPS (transaction per second) and reduced network fee.

关键词： Blockchain File Sharing Solana Smart Contracts Inter Planetary File Storage

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