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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4330 条记录，以下是321-330 订阅

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Scalable parallel I/O on a Blue Gene/Q supercomputer using compression, topology-aware data aggregation, and subfiling

Scalable parallel I/O on a Blue Gene/Q supercomputer using c...

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22nd euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Bui, Huy Leigh, Jason Vishwanath, Venkatram Finkel, Hal Habib, Salman Heitmann, Katrin Papka, Michael Harms, Kevin Univ Illinois Elect Visualizat Lab Chicago IL 60612 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781479927289

In this paper, we propose an approach to improving the I/O performance of an IBM Blue Gene/Q supercomputing system using a novel framework that can be integrated into high performance applications. We take advantage of the systems tremendous computing resources and high interconnection bandwidth among compute nodes to efficiently exploit I/O bandwidth. This approach focuses on lossless data compression, topology-aware data movement, and subfiling. The efficacy of this solution is demonstrated using microbenchmarks and an application-level benchmark.

关键词： data compression parallel machines parallel processing topology application-level benchmark blue gene/Q supercomputer lossless data compression microbenchmarks scalable parallel I/O performance topology-aware data aggregation topology-aware data movement Bandwidth Benchmark testing Data compression Libraries network topology Supercomputers Writing compression subfiling topology-aware data movement

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Adaptive Load Balancing based on Machine Learning for Iterative parallel Applications 28

Adaptive Load Balancing based on Machine Learning for Iterat...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Oikawa, Anna Victoria C. R. Freitas, Vinicius Castro, Marcio Pilla, Laercio L. Fed Univ Santa Catarina UFSC Florianopolis SC Brazil Univ Paris Saclay CNRS LRI Orsay France

ISBN: (纸本)9781728165820

The performance of irregular scientific applications can be easily affected by an uneven distribution of work among the computing resources. In this context, Load Balancing (LB) stands as one of the most important solutions to improve resource utilization. However, choosing the best-performing load balancing algorithm for a given application is not a trivial task. For instance, manually and statically choosing an LB algorithm does not work in situations where applications have a dynamic or unknown behavior. In this context, we propose a Machine Learning-based Adaptive Load Balancer (ADAPTIVELB) to automate the load balancing algorithm decision at run time. This approach monitors and collects information about the application dynamically, and according to the analyzed data, it makes a decision of invoking the most suitable LB algorithm. Our experiments show that ADAPTIVELB can select a good load balancing algorithm in most of the cases, leading to performance improvements over statically chosen LB algorithms and over the absence of a load balancer.

关键词： dynamic load balancing machine learning performance

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Agricultural Reform: More Efficient Farming Using Advanced parallel Refactoring Tools

Agricultural Reform: More Efficient Farming Using Advanced P...

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22nd euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Brown, Christopher Janjic, Vladimir Hammond, Kevin Schoener, Holger Idrees, Kamran Glass, Cohn W. Software Competence Ctr Hagenberg GmbH Hagenberg Im Muhlkreis Austria HLRS Stuttgart Germany

ISBN: (纸本)9781479927289

Modern multicore systems offer huge computing potential. Exploiting large parallel systems is still a very challenging task, however, especially as many software developers still use overly-sequential programming models. refactoring tool support that allows the programmer to introduce and tune parallelism in an easy and effective way, exploiting high-level parallel patterns such as farms and pipelines. Using our approach, we achieve speedups of up to 21 on a 24-core shared-memory system for a number of realistic use-cases.

关键词： agriculture parallel processing shared memory systems software maintenance advanced parallel refactoring tools agricultural reform farming high-level parallel patterns multicore systems overly-sequential programming models parallel systems shared-memory system Convolution Optimization parallel processing Pipelines Skeleton Software Streaming media Ant-Colony C++ Convolution FastFlow Molecular Dynamics Refactoring Skeletons

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Greedy Multi-cloud Selection Approach to Deploy an Application based on Microservices 27

Greedy Multi-cloud Selection Approach to Deploy an Applicati...

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27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Carvalho, Juliana Vieira, Dario Trinta, Fernando Fed Univ Piaui UFPI Picos Brazil EFREI PARIS Paris France Fed Uniers Ceara UFC Fortaleza Ceara Brazil

ISBN: (纸本)9781728116440

The multi-cloud delivery model allows the deployment of a distributed application in a manner in which the software architect chooses the services that better meet the application and the software architect's requirements. However, choosing cloud services is a complex task for a software architect, as there are several cloud providers that offer many services, which makes the selection process from the user perspective a relevant research problem. Therefore, in this paper, we propose a multiple providers selection approach to host a distributed application based on microservices to facilitate the software architect's decision-making. Our approach differs from others because each microservice is deployed in a single provider that better meets requirements from the software architect's perspective. Hence, our approach selects various services of a single provider for a microservice, and a distinct provider for each microservice, which makes this a complex task. We also use multi-criteria decision-making to rank the cloud services, and we map the selection process to multi-choice knapsack problem, which we develop using a greedy algorithm. Furthermore, we implement a tool to evaluate our approach performance, and the results show our approach feasibility, varying the number of cloud providers and services, microservices and requirements.

关键词： multi-cloud selection microservices multi-requirements multi-choice knapsack problem greedy algorithm

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Combining parallel Genetic Algorithms and Machine Learning to improve the research of optimal vaccination protocols 26

Combining Parallel Genetic Algorithms and Machine Learning t...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Pennisi, Marzio Russo, Giulia Pappalardo, Francesco Univ Catania Dept Math & Comp Sci Catania Italy Univ Catania Dept Biomed Sci Catania Italy Univ Catania Dept Drug Sci Catania Italy

ISBN: (纸本)9781538649756

The developing of novel prophylactic and therapeutic vaccine candidates in the field of cancer immunology brought to very promising results against tumors, entitling full protection with reduced amount of the typical side effects of the actual conventional treatments. However, such treatments required a constant, life-long, administration procedure to keep protection. As both the period of protection and the relative number of administrations grow, the problem of finding the best administration protocol, in time and dosage, becomes more and more complex. Such a problem cannot be usually solved in in vivo experiments, as the costs in terms of time, money, and people would be prohibitive. We propose a hybrid approach that integrates machine learning and parallel genetic algorithms to enhance the research in silico of optimal administration protocols for a cancer vaccine. A neural network is used to improve both crossover and mutation operators. Preliminary results suggest that the use of such could bring to better administration protocols using a similar computational effort.

关键词： Genetic algorithms machine learning neural networks agent based models optimal vaccine protocol cancer

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Boosting Graph Analytics by Tuning Threads and Data Affinity on NUMA Systems 29

Boosting Graph Analytics by Tuning Threads and Data Affinity...

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29th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Rocha, Hiago Mayk G. de A. Schwarzrock, Janaina Lorenzon, Arthur F. Beck, Antonio Carlos S. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Fed Univ Pampa Lab Optimizat Syst Alegrete Brazil

ISBN: (纸本)9781665414555

The execution of large real-world graphs, such as web searches and social networks, has been boosting by modern HPC systems. However, their irregular communication patterns and poor data locality impose many challenges, mainly when executed on NUMA systems. As we show in this paper, there is no one-fits-all configuration for threads/data mapping, and the best combination will vary according to the NUMA system, graph algorithm, and input graph at hand. based on that, we propose Graphith: a framework that automatically enhances graph processing performance by adapting its execution considering the variables mentioned above. Graphith also goes one step further and improves the existing policies: it uses a Genetic Algorithm to tine-tune the thread-to-core allocation combined with data mapping policies. With that, Graphith improves in 21%, on average, the default execution, and is, on average, 7% better than the best possible combination of standard policies.

关键词： Automatic Tuning Transparent Optimization Threads and Data Placement Graph processing Genetic Algorithm

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Latency Analysis of network-On-Chip based Many-Core Processors

Latency Analysis of Network-On-Chip based Many-Core Processo...

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22nd euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Kumar, Sunil Lipari, Giuseppe Scuola Super Sant Anna Pisa PI Italy LNM ITT Jaipur Rajasthan India LSV ENS Cachan Cachan France

ISBN: (纸本)9781479927289

The next generation of processor will contain an increasing number of cores, connected to the main memory and to each other using fast network-on-Chip (NoC) organised in complex mesh structures. In order to analyse real-time programs running on such architectures, it is necessary to estimate the communication latency between processes running on different cores. The goal of this paper is to propose an analytic model for bounding the communication latency on NoC for many-core architectures. In particular, we introduce a new approach to analyse the communication latency on NoC with wormhole switching and credit-based virtual channel flow control. The proposed model is evaluated by comparing the results predicted by the model with real measurements obtained running a set of experiments on an Intel SCC platform.

关键词： microprocessor chips multiprocessing systems multiprocessor interconnection networks network-on-chip parallel architectures program diagnostics Intel SCC platform NoC communication latency estimation complex mesh structures credit-based virtual channel flow control latency analysis main memory many-core architectures network-on-chip based many-core processors real-time program analysis wormhole switching Computer architecture Equations Mathematical model Program processors Switches System-on-chip Tiles

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Supporting the Development of Resilient Message Passing Applications using Simulation

Supporting the Development of Resilient Message Passing Appl...

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22nd euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Naughton, Thomas Engelmann, Christian Vallee, Geoffroy Boehm, Swen Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA

ISBN: (纸本)9781479927289

An emerging aspect of high-performance computing (HPC) hardware/software co-design is investigating performance under failure. The work in this paper extends the Extreme-scale Simulator (xSim), which was designed for evaluating the performance of message passing interface (MPI) applications on future HPC architectures, with fault-tolerant MPI extensions proposed by the MPI Fault Tolerance Working Group. xSim permits running MPI applications with millions of concurrent MPI ranks, while observing application performance in a simulated extreme-scale system using a lightweight parallel discrete event simulation. The newly added features offer user-level failure mitigation (ULFM) extensions at the simulated MPI layer to support algorithm-based fault tolerance (ABFT). The presented solution permits investigating performance under failure and failure handling of ABFT solutions. The newly enhanced xSim is the very first performance tool that supports ULFM and ABFT.

关键词： High-performance Computing Message Passing Interface Algorithm-based Fault Tolerance parallel Discrete Event Simulation Performance Prediction

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Memory-Aware Genetic Algorithms for Task Mapping on Hard Real-Time networks-on-Chip 26

Memory-Aware Genetic Algorithms for Task Mapping on Hard Rea...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Still, Lloyd Robert Indrusiak, Leandro Soares Univ York Real Time Syst Grp Dept Comp Sci York N Yorkshire England

ISBN: (纸本)9781538649756

The problem of mapping hard real-time tasks onto networks-on-chip has previously been successfully addressed by genetic algorithms. However, none of the existing problem formulations consider memory constraints. State-of-the-art genetic mappers are therefore able to find fully-schedulable mappings which are incompatible with the memory limitations of realistic platforms. In this paper, we extend the problem formulation and devise a memory architecture, in the form of private local memories. We then propose three memory models of increasing complexity and realism, and evaluate the impact these additional constraints pose to the genetic search. We conduct extensive experiments using tasks and communications from a realistic benchmark application, and compare the proposed approach against a state-of-the-art baseline mapper.

关键词： Real-time systems Multi-core processing

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Energy Efficient Scheduling of Real Time Signal processing Applications Through Combined DVFS and DPM 24

Energy Efficient Scheduling of Real Time Signal Processing A...

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24th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Nogues, Erwan Pelcat, Maxime Menard, Daniel Mercat, Alexandre UEB INSA Rennes UMR 6164 IETR F-35000 Rennes France

ISBN: (纸本)9781467387767

This paper proposes a framework to design energy efficient signal processing systems. The energy efficiency is provided by combining Dynamic Frequency and Voltage Scaling (DVFS) and Dynamic Power Management (DPM). The framework is based on Synchronous Dataflow (SDF) modeling of signal processing applications. A transformation to a single rate form is performed to expose the application parallelism. An automated scheduling is then performed, minimizing the constraint of energy efficiency and providing DVFS and DPM decisions. This framework uses an architecture model including the number of available cores, the per-actor processing load and the energy per cycle, derived from time and power measurements of modelled applications. After introducing the proposed framework, the energy characterization of *** SoC systems is described. A generic approach is presented to generate the energy model of a platform from power measurements as customized polynomials. Finally, the experimental results on a Samsung Exynos 5410 *** processor show that the energy optimal execution is not obtained by Linux governors that can execute either as fast-as-possible or as-slow-as-possible. Instead, the most energy efficient scheduling is obtained by adapting both DVFS and DPM to application needs.

关键词： DPM DVFS Energy low power

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