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检索条件"任意字段=24th Symposium on Principles of Distributed Computing"

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distributed computing Systems and Approaches for Polar Science Research: An Interdisciplinary Systematic Review 24

Distributed Computing Systems and Approaches for Polar Scien...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Kathiravelu, Pradeeban Birmingham, Ryan Univ Alaska Anchorage Anchorage AK 99508 USA Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9798350377521;9798350377514

Polar science is an umbrella term for several research disciplines practiced in Earth's polar regions and other planets. Polar science research includes studies performed on land (such as geology and archeology on the circumpolar tundra), air (atmospheric research), sky (planetary observations due to dark, clear, and clean skies during the polar nights), and ocean (marine studies in the Arctic and the Southern Oceans). Many polar science research works look into overlapping complementary studies. distributed computing is a discipline of computer science that involves computations across multiple distinct physically separated computing resources, such as computer clusters, clouds, and the edge. distributed computing has enabled efficient, scalable, and elastic execution of complex computational problems on utility hardware without expecting access to expensive infrastructure or supercomputers. In this paper, we screen 770 for the interdisciplinary research of distributed computing used in or developed for polar science. After systematically removing the irrelevant studies, we assess the full text of 72 papers to understand the distributed systems research landscape for polar science. We then specifically study 22 polar science research works that develop or heavily utilize distributed computing frameworks or principles in detail. Our study finds distributed execution frameworks instrumental for polar science due to the complex and real-time computational needs, coupled with the remote location requiring efficient network bandwidth availability and usage to access remote resources.

关键词： Computer Networks distributed computing distributed Systems Environmental Systems Polar Science

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Proceedings - 2021 IEEE 24th International symposium on Real-Time distributed computing, ISORC 2021

Proceedings - 2021 IEEE 24th International Symposium on Real...

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24th IEEE International symposium on Real-Time distributed computing, ISORC 2021

ISBN: (纸本)9781665404143

the proceedings contain 16 papers. the topics discussed include: multi-link failure effects on MPLS resilient fast-reroute network architectures;RT-cloud: virtualization technologies and cloud computing for railway use-case;energy-efficient adaptive system reconfiguration for dynamic deadlines in autonomous driving;synchronizing real-time tasks in time-triggered networks;experiences from adjusting industrial software for worst-case execution time analysis;a reliable job allocation scheduler for time-triggered wireless networks;T-pack: timed network security for real time systems;multi-link failure effects on MPLS resilient fast-reroute network architectures;and a processor extension for time-predictable code execution.

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Elasticity in a Task-based Dataflow Runtime through Inter-node GPU Work Stealing 24

Elasticity in a Task-based Dataflow Runtime Through Inter-no...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： John, Joseph Milthorpe, Josh Natl Computat Infrastruct Canberra ACT Australia Australian Natl Univ Sch Comp Canberra ACT Australia Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9798350395679;9798350395662

Most contemporary HPC programming models assume an inelastic runtime in which the resources allocated to an application remain fixed throughout its execution. Conversely, elastic runtimes can expand and shrink resources based on availability and/or dynamic application requirements. In this paper, we implement elasticity for PaRSEC, a task-based dataflow runtime, using inter-node GPU work stealing. In addition to supporting elasticity, we demonstrate that inter-node GPU work stealing can enhance the performance of imbalanced applications by up to 45%.

关键词： distributed Work Stealing Elastic computing Malleable computing Task-based programming Dataflow PaRSEC

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IDIOMS: Index-powered distributed Object-centric Metadata Search for Scientific Data Management 24

IDIOMS: Index-powered Distributed Object-centric Metadata Se...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Zhang, Wei Tang, Houjun Byna, Suren Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Ohio State Univ Columbus OH USA

ISBN: (纸本)9798350395679;9798350395662

Affix-oriented metadata search is one of the essential fuzzy search capabilities that allow users to find data of interest in their voluminous data set with incomplete query conditions. With the recent transition towards object-centric data management systems in the science community, there is a paramount need for the support of such features in distributed settings. However, existing metadata search solutions either do not support efficient affix-oriented metadata search or do not suit well in a distributed setting of object-centric data management systems. To bridge this gap, we introduce IDIOMS, a metadata search solution underpinned by a distributed metadata index, meticulously designed to enable high-performance affix-oriented metadata search for parallel object-centric storage. One of the standout features of IDIOMS is its efficiency in supporting four distinct types of highly demanded metadata queries. Furthermore, IDIOMS is flexibly catering to both independent and collective metadata search operations. Our experimental comparisons with SoMeta, a state-of-the-art metadata query method, demonstrate more than 400x performance boost for independent queries and up to 300x performance improvements for collective queries, while keeping a small index management overhead.

关键词： Metadata Search Metadata Management Parallel Data Processing distributed Data Management Scientific Data Management

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Building Trust in Data for IoT Systems 24

Building Trust in Data for IoT Systems

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Margaria, Davide Carelli, Alberto Vesco, Andrea LINKS Fdn Cvbersecur Res Grp I-10138 Turin Italy

ISBN: (纸本)9798350377521;9798350377514

Nowadays, Internet of things platforms are being deployed in a wide range of application domains. Some of these include use cases with security requirements, where the data generated by an IoT node is the basis for making safety-critical or liability-critical decisions at system level. the challenge is to develop a solution for data exchange while proving and verifying the authenticity of the data from end-to-end. In line with this objective, this paper proposes a novel solution with the proper protocols to provide Trust in Data, making use of two Roots of Trust that are the IOTA distributed Ledger Technology and the Trusted Platform Module. the paper presents the design of the proposed solution and discusses the key design aspects and relevant trade-offs. the paper concludes with a Proof-of-Concept implementation and an experimental evaluation to confirm its feasibility and to assess the achievable performance.

关键词： Internet of things distributed Ledger Technology IOTA Tangle Trusted computing Remote Attestation

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A distributed, Asynchronous Algorithm for Large-Scale Internet Network Topology Analysis 24

A Distributed, Asynchronous Algorithm for Large-Scale Intern...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Elmougy, Youssef Hayashi, Akihiro Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350395679;9798350395662

With the growing complexity of modern internet networks, we introduce a distributed, asynchronous, and scalable algorithm tailored for determining the centrality and importance of various levels of the global internet network topology at a massive scale. By utilizing triangle formations and neighborhood densities in these complex networks, our algorithm provides valuable insights into the structural importance of individual nodes and the intricate relationships among interconnected entities. Focusing on the global internet's prime components routers, IP addresses, and Autonomous Systems - the algorithm provides a scalable solution extending to the global internet infrastructure, datacenters, cloud networks, and private networks. By taking advantage of the parallelism, distributed processing, efficient communication, and fine-grained asynchronous execution through an Actor-based programming system, our algorithm is capable of efficiently processing and performing rapid computations on large-scale internet networks. We perform scalability studies on the NERSC Perlmutter supercomputer and the Georgia Tech HPC PACE cluster using three large-scale real-world network datasets and one large-scale synthetic network dataset, achieving up to 91.7% parallel efficiency scaling out to 2K cores and reducing execution time from 5.7 hours to 18.3 seconds, while performing 105.4x better on average compared to related approaches. Our research contributes significantly to facilitating enhanced network management, fault tolerance and resilience, and security, but as well to improving overall network stability, reducing latency, and optimizing energy consumption across the vast internet network devices environment.

关键词： Network Topology Internet Analysis Actors Selectors distributed Systems Scalable computing Centrality Shared Memory PGAS OpenSHMEM

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Preserving Near-Optimal Gradient Sparsification Cost for Scalable distributed Deep Learning 24

Preserving Near-Optimal Gradient Sparsification Cost for Sca...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Yoon, Daegun Oh, Sangyoon ETRI Daejeon South Korea Ajou Univ Suwon South Korea

ISBN: (纸本)9798350395679;9798350395662

Communication overhead is a major obstacle to scaling distributed training systems. Gradient sparsification is a potential optimization approach to reduce the communication volume without significant loss of model fidelity. However, existing gradient sparsification methods have low scalability owing to inefficient design of their algorithms, which raises the communication overhead significantly. In particular, gradient build-up and inadequate sparsity control methods degrade the sparsification performance considerably. Moreover, communication traffic increases drastically owing to workload imbalance of gradient selection between workers. To address these challenges, we propose a novel gradient sparsification scheme called ExDyna. In ExDyna, the gradient tensor of the model comprises fined-grained blocks, and contiguous blocks are grouped into non-overlapping partitions. Each worker selects gradients in its exclusively allocated partition so that gradient build-up never occurs. To balance the workload of gradient selection between workers, ExDyna adjusts the topology of partitions by comparing the workloads of adjacent partitions. In addition, ExDyna supports online threshold scaling, which estimates the accurate threshold of gradient selection on-the-fly. Accordingly, ExDyna can satisfy the user-required sparsity level during a training period regardless of models and datasets. therefore, ExDyna can enhance the scalability of distributed training systems by preserving near-optimal gradient sparsification cost. In experiments, ExDyna outperformed state-of-the-art sparsifiers in terms of training speed and sparsification performance while achieving high accuracy.

关键词： distributed deep learning gradient sparsification scalability

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Intelligent Data Source Emission Rate Control for Optimising the Performance of Streaming Applications 24

Intelligent Data Source Emission Rate Control for Optimising...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Xiao, Ziren Leckie, Christopher Rodriguez, Maria A. Univ Melbourne Melbourne Vic Australia

ISBN: (纸本)9798350395679;9798350395662

Streaming applications are expected to process an ever-increasing amount of data with high throughput and stringent latency requirements. Flooding these applications with incoming data may overload the stream processing engine, leading to a system with unstable queues and infinitely growing latencies. Existing stream processing systems are equipped to deal with such overload scenarios reactively, either through back pressure or load shedding mechanisms. these mechanisms, however, have considerable drawbacks as they consume additional system resources, incur in non-negligible performance overheads, and may compromise the quality of application-level results. To address this gap, we propose a strategy based on reinforcement learning to throttle the input rate of data sources in streaming applications. the proposed strategy mitigates overload scenarios by addressing the source of the problem, thus allowing resources to be better utilized by application and system components and mitigating the performance overhead of system-level reactive mechanisms. through our experiments with two different applications, we demonstrate that our proposed approach reduces end-to-end latencies by up to 82% and increases throughput by up to 10% compared to back pressure mechanisms implemented in state-of-the-art stream processing engines.

关键词： distributed stream processing rate control reinforcement learning deep learning

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Efficient Data-Parallel Continual Learning with Asynchronous distributed Rehearsal Buffers 24

Efficient Data-Parallel Continual Learning with Asynchronous...

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24th IEEE/ACM International symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Bouvier, thomas Nicolae, Bogdan Chaugier, Hugo Costan, Alexandru Foster, Ian Antoniu, Gabriel Univ Rennes IRISA CNRS Inria Rennes France Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9798350395679;9798350395662

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs, allowing us to achieve short runtime and scalability while retaining high accuracy. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input mini-batches (groups of training samples fed to the model) using unbiased, global sampling. In this paper we explore the benefits of this approach for classification models. We run extensive experiments on up to 128 GPUs of the thetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 classification accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

关键词： continual learning data-parallel training experience replay distributed rehearsal buffers asynchronous data management scalability

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Proceedings - 2024 IEEE 24th International symposium on Cluster, Cloud and Internet computing, CCGrid 2024

Proceedings - 2024 IEEE 24th International Symposium on Clus...

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24th IEEE/ACM International symposium on Cluster, Cloud and Internet computing, CCGrid 2024

ISBN: (纸本)9798350395662

the proceedings contain 75 papers. the topics discussed include: accelerating large language model training with hybrid GPU-based compression;demystifying swarm learning: an emerging decentralized federated learning system;IDIOMS: index-powered distributed object-centric metadata search for scientific data management;Brug: an adaptive memory (re-)allocator;MDSTGCN : multi-scale dynamic spatial-temporal graph convolution network with edge feature embedding for traffic forecasting;fair, efficient multi-resource scheduling for stateless serverless functions with Anubis;STRonG: system topology risk analysis on graphs;federated semi-supervised learning with local and global updating frequency optimization;and towards better QoS and lower costs of P4 EIP gateway at the edge.

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