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检索条件"任意字段=14th International Conference on Parallel and Distributed Computing Systems"

共 8132 条记录，以下是71-80 订阅

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25th international European conference on parallel and distributed computing, EuroPar 2019

25th International European Conference on Parallel and Distr...

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25th international European conference on parallel and distributed computing, EuroPar 2019

ISBN: (纸本)9783030483395

the proceedings contain 63 papers. the special focus in this conference is on parallel and distributed computing. the topics include: mF2C: the Evolution of Cloud computing Towards an Open and Coordinated Ecosystem of Fogs and Clouds;academic Achievement Recognition and Verification Using Blockchain;crypto-Trading. Rechargeable Token-Based Smart Energy Market Enabled by Blockchain and IoT Technology;ethereum Transaction Performance Evaluation Using Test-Nets;blockchain-Based Trusted Cross-organizational Deliveries of Sensor-Equipped Parcels;towards a Trusted Support Platform for the Job Placement Task;blockchain Materialization as a General Purpose Technology: A Research Framework;on Refining Design Patterns for Smart Contracts;dataRaceOnAccelerator – A Micro-benchmark Suite for Evaluating Correctness Tools Targeting Accelerators;Adaptive Crown Scheduling for Streaming Tasks on Many-Core systems with Discrete DVFS;application Topology Definition and Tasks Mapping for Efficient Use of Heterogeneous Resources;Toward Heterogeneous MPI+MPI Programming: Comparison of OpenMP and MPI Shared Memory Models;multicore Performance Prediction – Comparing three Recent Approaches in a Case Study;exploiting Historical Data: Pruning Autotuning Spaces and Estimating the Number of Tuning Steps;advancing Automatic Code Generation for Agent-Based Simulations on Heterogeneous Hardware;Optimization of Data-parallel Applications on Heterogeneous HPC Platforms for Dynamic Energy through Workload Distribution;search-Based Scheduling for parallel Tasks on Heterogeneous Platforms;adaptation of Workflow Application Scheduling Algorithm to Serverless Infrastructure;CCAMP: OpenMP and OpenACC Interoperable Framework;HPC Requirements of High-Fidelity Flow Simulations for Aerodynamic Applications;minimizing Self-adaptation Overhead in parallel Stream Processing for Multi-cores;data-Adapted parallel Merge Sort;In Situ Visualization of Performance-Related Data in parallel CFD Applications;time-Based C

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parallel Auto-Scheduling of Counting Queries in Machine Learning Applications on HPC systems

Parallel Auto-Scheduling of Counting Queries in Machine Lear...

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29th international conference on parallel and distributed computing (Euro-Par)

作者： Bratek, Pawel Szustak, Lukasz Zola, Jaroslaw Czestochowa Tech Univ Dabrowskiego 69 PL-42201 Czestochowa Poland Univ Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9783031488023;9783031488030

We introduce a parallel mechanism for auto-scheduling data access queries in machine learning applications. Our solution combines the advantages of three individual strategies to reduce the time of query stream execution. Using bayesian network learning as a use case, we achieve several times speedup compared to the best possible strategy on two different computing servers.

关键词： Machine learning

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CASPER: Carbon-Aware Scheduling and Provisioning for distributed Web Services 14

CASPER: Carbon-Aware Scheduling and Provisioning for Distrib...

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14th international Green and Sustainable computing conference (IGSC)

作者： Souza, Abel Jasoria, Shruti Chakrabarty, Basundhara Irwin, David Shenoy, Prashant Bridgwater, Alexander Lundberg, Axel Skogh, Filip Ali-Eldin, Ahmed Univ Massachusetts Amherst MA 01003 USA Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798400716690

there has been a significant societal push towards sustainable practices, including in computing. Modern interactive workloads such as geo-distributed web-services exhibit various spatiotemporal and performance flexibility, enabling the possibility to adapt the location, time, and intensity of processing to align with the availability of renewable and low-carbon energy. An example is a web application hosted across multiple cloud regions, each with varying carbon intensity based on their local electricity mix. distributed load-balancing enables the exploitation of low-carbon energy through load migration across regions, reducing web applications carbon footprint. In this paper, we present CASPER, a carbon-aware scheduling and provisioning system that primarily minimizes the carbon footprint of distributed web services while also respecting their Service Level Objectives (SLO). We formulate CASPER as an multi-objective optimization problem that considers both the variable carbon intensity and latency constraints of the network. Our evaluation reveals the significant potential of CASPER in achieving substantial reductions in carbon emissions. Compared to baseline methods, CASPER demonstrates improvements of up to 70% with no latency performance degradation.

关键词： Web services

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Cooperative Resource Sharing Between Agents in Multi-distributed Fog Nodes 14

Cooperative Resource Sharing Between Agents in Multi-Distrib...

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14th international conference on Information and Communication systems, ICICS 2023

作者： Malkawi, Anfal Shurman, Mohammad Jordan University of Science and Technology Network Engineering and Security Dept. Irbid Jordan

ISBN: (纸本)9798350307870

Cloud computing provides high computation resources to the user's applications. Some of the user's applications are time-sensitive. therefore, processing these applications in traditional cloud computing centers might fail due to the high latency between the cloud layer and the user equipment layer. thus, to solve this issue, fog computing is deployed near the user's nodes layer to extend the cloud layer and provide computation resources with low latency. the fog computing layer may contain limited devices compared to the cloud layer. As a result at peak hours, when multiple tasks are requested from IoT applications to the fog nodes, communication congestion might occur and the processing time will significantly increase. In addition, the fog layer may contain limited resources and limited power, and many of the requested tasks will be offloaded to the cloud layer for processing. Consequently, the purpose of the fog layer is not accomplished. In this paper, we propose a cooperation scheme between fog nodes to process tasks requested from IoT nodes with minimum service latency and maximum throughput. © 2023 IEEE.

关键词： Fog

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An Insight into the State-of-the-Art Vehicular Fog computing with an Opportunistic Flavour 1

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14th international conference on parallel Processing and Applied Mathematics, PPAM 2022

作者： Ostrowski, Krzysztof Malecki, Krzysztof West Pomeranian University of Technology Szczecin Poland

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

Vehicular fog computing constitutes an environment for execution of demanding computation and storage tasks. there formed a hierarchical decentralised and distributed architecture supports the resource-constrained devices in completion of assignments too complex for them, and too latency-sensitive to be delegated to a cloud. this paper evaluates the recent advances in the research on vehicular fog computing focused on the exploitation of resources of the moving vehicles that operate in the opportunistic device-to-device dynamic networking environments. the proposed evaluation criteria consider structural, behavioural, and functional safety aspects of such systems. A set of research directions concludes this article. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fog computing

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distributed Machine Learning 24

Distributed Machine Learning

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25th international conference on distributed computing and Networking, ICDCN 2024

作者： Chatterjee, Bapi Iiit New Delhi Delhi India

ISBN: (纸本)9798400716737

We explore the landscape of distributed machine learning, focusing on advancements, challenges, and potential future directions in this rapidly evolving field. We delve into the motivation for distributed machine learning, its essential techniques, real-world applications, and open research questions. the theoretical discussion will give an overview of proving the convergence of popular Stochastic Gradient Descent (SGD) Algorithms to train contemporary machine learning models, including the deep learning models with the assumption of non-convexity, in a distributed setting. We will specify the convergence of data parallel SGD for various distributed systems properties, such as asynchronous and compressed communication. We will also discuss distributed machine learning techniques such as model parallelism and tensor parallelism to train large language models (LLMs). © 2024 ACM.

关键词： Stochastic systems

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Enabling Dynamic Selection of Implementation Variants in Component-Based parallel Programming for Heterogeneous systems

Enabling Dynamic Selection of Implementation Variants in Com...

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29th international conference on parallel and distributed computing (Euro-Par)

作者： Memeti, Suejb Blekinge Inst Technol Dept Comp Sci DIDA Karlskrona Sweden

ISBN: (纸本)9783031506833;9783031506840

Heterogeneous systems, consisting of CPUs and GPUs, offer the capability to address the demands of compute- and data-intensive applications. However, programming such systems is challenging, requiring knowledge of various parallel programming frameworks. this paper introduces COMPAR, a component-based parallel programming framework that enables the exposure and selection of multiple implementation variants of components at runtime. the framework leverages compiler directive-based language extensions to annotate the source code and generate the necessary glue code for the StarPU runtime system. COMPAR provides a unified view of implementation variants and allows for intelligent selection based on runtime context. Our evaluation demonstrates the effectiveness of COMPAR through benchmark applications. the proposed approach simplifies heterogeneous parallel programming and promotes code reuse while achieving optimal performance.

关键词： component-based programming implementation variant selection heterogeneous parallel computing systems source-to-source compilation performance optimization

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Triad: Trusted Timestamps in Untrusted Environments 14

Triad: Trusted Timestamps in Untrusted Environments

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14th IEEE international conference on Cloud computing Technology and Science (CloudCom)

作者： Fernandez, Gabriel Brito, Andrey Fetzer, Christof Tech Univ Dresden Dresden Germany Univ Fed Campina Grande Campina Grande Brazil

ISBN: (纸本)9798350339826

We aim to provide trusted time measurement mechanisms to applications and cloud infrastructure deployed in environments that could harbor potential adversaries, including the hardware infrastructure provider. Despite Trusted Execution Environments (TEEs) providing multiple security functionalities, timestamps from the Operating System are not covered. Nevertheless, some services require time for validating permissions or ordering events. To address that need, we introduce Triad, a trusted timestamp dispatcher of time readings. the solution provides trusted timestamps enforced by mutually supportive enclave-based clock servers that create a continuous trusted timeline. We leverage enclave properties such as forced exits and CPU-based counters to mitigate attacks on the server's timestamp counters. Triad produces trusted, confidential, monotonically-increasing timestamps with bounded error and desirable, non-trivial properties. Our implementation relies on Intel SGX and SCONE, allowing transparent usage. We evaluate Triad's error and behavior in multiple dimensions.

关键词： Trusted computing distributed systems TEE Trusted Clocks

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Flink-Stream-computing-Driven parallel Particle Filter for the Predictive Maintenance of Industrial Facilities 14

Flink-Stream-Computing-Driven Parallel Particle Filter for t...

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14th international conference on Reliability, Maintainability and Safety, ICRMS 2023

作者： Shi, Jianming Wang, Wei Wang, Gong Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350329988

With the advent and development of big data technology, advanced stream computing represented by Flink has demonstrated significant application value in the condition monitoring of industrial facilities. In recent years, particle filter (PF) technology has been extensively applied to time series data prediction and has demonstrated superiority in nonlinear and nonGaussian systems. When undertaking the predictive maintenance of industrial facilities, stream computing must be conducted on the continuously emerging data of facilities to dynamically predict the key performance indices at any future time. However, the fusion application of a PF and big data stream computing has no precedent. thus, this study proposes a parallel PF method driven by Flink stream computing for real-time prognostics of facilities. the main contributions of this study include: 1) execution of parallel online modeling of time series data through Flink, as well as periodic autonomous generation of prognostic models, and 2) embedding of the update and prediction processes of the PF into the Flink stream computing program to improve the PF efficiency in practical industrial applications. the monitoring data streams for multiple facilities are simulated through Kafka to test the functionality and performance of the parallel PF. Modeling tasks are periodically triggered and followed closely with the prediction results output by the PF, with operation under parallel mode for all the facilities. the relative prediction error is less than 1.9%. Further, the average lag between the output time of the prediction results and event time triggering modeling is 3.6 s, and the average execution time of the PF computing tasks is 90 ms. © 2023 IEEE.

关键词： Predictive maintenance

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SwitchFlow: Optimizing HPC Workflow Performance with Heterogeneous Serverless Frameworks 30

SwitchFlow: Optimizing HPC Workflow Performance with Heterog...

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30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Chen, Hao Dong, Yucong Wen, Xin Xu, Zichen Nanchang University School of Mathematics and Computer Science Jiangxi China Jiangxi Academy of Water Science and Engineering Jiangxi China

ISBN: (纸本)9798331515966

High-performance serverless computing has garnered significant attention. Researchers have developed numerous optimization strategies for serverless frameworks to fully leverage the benefits of serverless computing. However, they often overlook the architectural heterogeneity of various serverless frameworks, leading to performance discrepancies. We designs SwitchFlow by analyzing and characterizing the performance of different serverless frameworks. the core idea is to utilize the time series characteristics of scientific workflows to predict upcoming functions, thereby switching them to the optimal framework for execution. Furthermore, SwitchFlow's unique design concept enables integration with existing optimization strategies. Experimental results demonstrate that SwitchFlow can effectively reduce overall workflow execution time, optimize resource efficiency, and enhance service availability compared to running on a single serverless framework. © 2024 IEEE.

关键词： High Performance computing Serverless computing

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