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

共 4336 条记录，以下是1211-1220 订阅

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20th International conference on Runtime Verification, RV 2020

20th International Conference on Runtime Verification, RV 20...

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20th International conference on Runtime Verification, RV 2020

ISBN: (纸本)9783030605070

The proceedings contain 29 papers. The special focus in this conference is on Runtime Verification. The topics include: SOTER on ROS: A Run-Time Assurance Framework on the Robot Operating System;monitoring Cyber-Physical Systems: From Design to Integration;a Benchmark Generator for Online First-Order Monitoring;automatic Optimizations for Stream-based Monitoring Languages;preface;patrIoT: Policy Assisted Resilient Programmable IoT System;from Statistical Model Checking to Run-Time Monitoring Using a Bayesian network Approach;TLTk: A Toolbox for parallel Robustness Computation of Temporal Logic Specifications;a Verified Online Monitor for Metric Temporal Logic with Quantitative Semantics;logical Signal processing: A Fourier Analysis of Temporal Logic;efficient Runtime Assertion Checking for Properties over Mathematical Numbers;test4Enforcers: Test Case Generation for Software Enforcers;runtime-Safety-Guided Policy Repair;Analysing the Performance of Python-based Web Services with the VyPR Framework;unifying the Time-Event Spectrum forStream Runtime Verification;verified Rust Monitors for Lola Specifications;moonLight: A Lightweight Tool forMonitoring Spatio-Temporal Properties;monitoring Spatio-Temporal Properties (Invited Tutorial);property-Directed Verified Monitoring of SignalTemporalLogic;empirical Abstraction;Actor-based Runtime Verification with MESA;monitorability Under Assumptions;Runtime Verification of Autonomous Driving Systems in CARLA;efficient System Verification with Multiple Weakly-Hard Constraints for Runtime Monitoring;BDDs for Representing Data in Runtime Verification;assurance of distributed Algorithms and Systems: Runtime Checking of Safety and Liveness;BISM: Bytecode-Level Instrumentation for Software Monitoring.

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Exploration of the Influence on Training Deep Learning Models by Watermarked Image Dataset

Exploration of the Influence on Training Deep Learning Model...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Shiqin Liu Shiyuan Feng Jinxia Wu Wei Ren Weiqi Wang Wenwen Zheng School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Chinese Academy of Sciences Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang China

Deep learning has achieved great success in various applications with the help of a large scale of datasets. As a result, sharing the valuable big data that can be applied to training deep learning models is of essential importance currently. However, how to claim ownership and protect the copyright of image big data during the sharing process is still a vital issue that should be addressed. The application of digital watermarks can protect the copyright of image data, at the same time, it also degrades the image quality at the same time. As for invisible digital watermarks, the higher the watermark embedding intensity causes the greater the host image changes. Therefore, the performance of deep learning models may decline due to using the watermarked training set. In this paper, we evaluate the influences of different embedding intensities of various watermarking algorithms on several mainstream models and conclude how the watermarking intensity affects the model training. Besides, referring to watermarking algorithms that have been proposed, we proposed a novel discrete Fourier transform-based watermarking algorithm that can achieve image copyright protection yet maintain the utility of models.

关键词： Training Deep learning Image quality Fourier transforms Watermarking Big Data Copyright protection

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Shrink or Substitute: Handling Process Failures in HPC Systems using In-situ Recovery 26

Shrink or Substitute: Handling Process Failures in HPC Syste...

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

作者： Ashraf, Rizwan A. Hukerikar, Saurabh Engelmann, Christian Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA

ISBN: (纸本)9781538649756

Efficient utilization of today's high-performance computing (HPC) systems with complex hardware and software components requires that the HPC applications are designed to tolerate process failures at runtime. With low mean-time-to-failure (MTTF) of current and future HPC systems, long running simulations on these systems require capabilities for gracefully handling process failures by the applications themselves. In this paper, we explore the use of fault tolerance extensions to Message Passing Interface (MPI) called user-level failure mitigation (ULFM) for handling process failures without the need to discard the progress made by the application. We explore two alternative recovery strategies, which use ULFM along with application-driven in-memory checkpointing. In the first case, the application is recovered with only the surviving processes, and in the second case, spares are used to replace the failed processes, such that the original configuration of the application is restored. Our experimental results demonstrate that graceful degradation is a viable alternative for recovery in environments where spares may not be available.

关键词： Fault Tolerance Process Failures Checkpoint/Restart Message Passing Interface Recovery

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Novel application of parallel computing techniques in soft X-rays plasma measurement systems for the WEST experimental thermal fusion reactor 26

Novel application of parallel computing techniques in soft X...

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

作者： Krawczyk, Rafal Linczuk, Pawel Wojenski, Andrzej Pozniak, Krzysztof Kolasinski, Piotr Kasprowicz, Grzegorz Zabolotny, Wojciech Gaska, Michal Mazon, Didier Jardin, Axel Linczuk, Pawel Czarski, Tomasz Chernyshova, Maryna Kowalska-Strzeciwilk, Ewa Malinowski, Karol Warsaw Univ Technol Warsaw Poland CEA Cadarache St Paul Les Durance France Inst Plasma Phys & Laser Microfus Warsaw Poland

ISBN: (纸本)9781538649756

The article presents results of the novel approach of combining high-performance and parallel computing solutions with front-end electronics in the development of scalable specialized soft X-rays measurement tool for high-scale plasma physics experiments with thermal fusion devices. Regarding the need for an easily-modifiable advanced diagnostics of tokamak hot plasma content, the heterogeneous system consisting of FPGAs and the PC server was introduced. The objective is to provide data quality monitoring and evaluation mechanisms along with an algorithm benchmarking tool for fast, low-latency measurements of soft X-rays emitted by hot tokamak plasma. The article describes a method of the development of the computation pipeline on the server side. The novel parallel algorithms and results are discussed. This brand new approach is targeted to adapt a HPC techniques in new areas of science, where comprehensive low-latency measurements and instrumentation are increasingly desired. The presented solution is deployed in the operational tokamak WEST (Tungsten Environment in Steady-State Tokamak) in collaboration with French Alternative Energies and Atomic Energy Commission (CEA), Cadarache, France.

关键词： Heterogeneous Systems High Performance Computing parallel Computing Plasma Physics Fast Data processing Intel Xeon High-Throughput Computing

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DuoFS: A Hybrid Storage System Balancing Energy-Efficiency, Reliability, and Performance 26

DuoFS: A Hybrid Storage System Balancing Energy-Efficiency, ...

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

作者： Jiao, Bing Yin, Shu Ruan, Xiaojun Chen, Si Tang, Zhuo Zhu, Xiaomin ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Hunan Univ Changsha Hunan Peoples R China Calif State Univ East Bay CA USA West Chester Univ W Chester PA USA Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781538649756

As the Energy Wall and the Reliability Wall become unavoidable, it is a demanding and challenging task to reduce energy consumption in large-scale storage systems in modern data centers while retaining acceptable systems reliability. We propose a reliable energy-efficient storage system called DuoFS, which aims at balancing the energy efficiency, the reliability and the performance of parallel storage systems by seamlessly integrating one HDD-based file system and one SSD-based file system. At the heart of the DuoFS is a transformative middleware layer that dispatches files to the one of the two independent parallel file systems based on the files' I/O access popularity. By replicating popular files to the SSD-based file system and pushing the HDD-based file system into the low-power mode under light workload conditions, DuoFS can reduce significant energy consumption, avoid major factors that harm the storage systems reliability, and extract SSDs good I/O performance. Experimental results show that the DuoFS system saves up to 40% of energy, achieves up to 50% better I/O performance while only sacrificing less than 15% of the system's reliability.

关键词： hybrid parallel storage system multiple file systems PVFS PLFS energy- efficient reliability

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Accelerate Graph Neural network Training by Reusing Batch Data on GPUs

Accelerate Graph Neural Network Training by Reusing Batch Da...

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IEEE International conference on Performance, Computing and Communications (IPCCC)

作者： Zhejiang Ran Zhiquan Lai Lizhi Zhang Dongsheng Li National Key Laboratory of Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665443326

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.

关键词： Training Productivity Deep learning Design methodology conferences Graphics processing units Programming

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Privacy preservation for network traffic classification 20

Privacy preservation for network traffic classification

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Lu, Yue Tian, Hui Yu, Jingjing School of Eiectronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)9781728126166

With the rapid development of Internet technology and massive demands of data sharing, data privacy issues have attracted more and more attention in recent years. The paper analyzes the network traffic classification methods and designs the features subset selection algorithm based on information entropy. The proposed privacy preserving algorithm is based on data perturbation. By applying the algorithm on the real network traffic data set, it is shown that the network traffic data protected by the algorithm can effectively ensure data security while maintaining data utility, which contributes to balance the contradiction between them in existing algorithms. It effectively solves the privacy leakage problem of network traffic in the process of data mining. © 2019 IEEE.

关键词： Classification (of information)

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Optimizing Machine Learning Algorithms on Multi-core and Many-core Architectures using Thread and Data Mapping 26

Optimizing Machine Learning Algorithms on Multi-core and Man...

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

作者： Serpa, Matheus S. Krause, Arthur M. Cruz, Eduardo H. M. Navaux, Philippe O. A. Pasin, Marcelo Felber, Pascal Fed Univ Rio Grande Sul UFRGS Inst Informat Porto Alegre RS Brazil Univ Neuchatel Neuchatel Switzerland

ISBN: (纸本)9781538649756

Driven by the development of new technologies such as personal assistants or autonomous cars, machine learning has rapidly become one of the most active fields in computer science. The algorithms at the core of machine learning are notoriously demanding in terms of resources. It is therefore of paramount importance to optimize their operation on modern processors. Several approaches have been proposed to accelerate machine learning on GPUs and massively parallel computers, as well as dedicated ASICs. In this paper, we focus on Intel's multi-core Xeon and many-core accelerator Xeon Phi Knights Landing, which can host several hundreds of threads on the same CPU. In such architectures, thread and data mapping are keys for performance. We study the impact of mapping strategies, revealing that, with smart mapping policies, one can indeed significantly speed up machine learning applications on manycore architectures. Execution time was reduced by up to 25.2% and 18.5% on Intel Xeon and Xeon Phi KNL, respectively.

关键词： Machine learning Thread mapping Data mapping Memory accesses

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Data-layout reorganization for an efficient intra-node assembly of a Spectral Finite-Element Method 26

Data-layout reorganization for an efficient intra-node assem...

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

作者： Sornet, Gauthier Jubertie, Sylvain Dupros, Fabrice De Martin, Florent Thierry, Philippe Limet, Sebastien Univ Orleans INSA Ctr Val de Loire LIFO EA 4022 Orleans France Bur Rech Geol & Minieres BP 6009 F-45060 Orleans 2 France Intel Corp Paris France

ISBN: (纸本)9781538649756

The Finite-Element Method (FEM) is routinely used to solve Partial Differential Equations (PDE) in various scientific domains. For seismic waves modeling, the Spectral Element Method (SEM), which is a specific formulation of the classical FEM approach, have gained significant attention for the last two decades. This is explained both from the very good numerical accuracy of this method and from the parallel performance of classical MPI-based implementations that scale up to several tens of thousands computing cores. Nevertheless, the trend for current processors with an increasing level of low-level parallelism requires significant efforts at the shared-memory level. One major bottleneck is coming from the standard FEM assembly phase that leads to significant amount of irregular memory accesses. This prevents any efficient automatic optimizations from the compiler for instance. In this paper, we extract a kernel from a spectral-element application dedicated to earthquake simulations in complex geological medium (EFISPEC code developed at BRGM, the French Geological Survey). We study the intra-node behavior and we propose different levels of optimization (data-layout, manual vectorization, multi-threading) to fully benefit from SIMD units and NUMA architectures. Experiments performed on Intel Broadwell architecture show that the proposed optimizations dramatically improve the intra-node performance of the mini-application. Moreover, our results show a good match with rooflines theoretical performance models. We believe that these optimizations are not specific to this mini-application and may be implemented in different SEM and FEM based solvers as well.

关键词： Finite element analysis Optimization Kernel Instruction sets parallel processing Memory management

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parallelHashClone: a parallel implementation of HashClone suite for clonality assessment from NGS data 26

ParallelHashClone: a parallel implementation of HashClone su...

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

作者： Romano, Greta Genuardi, Elisa Calogero, Raffaele Ferrero, Simone Univ Turin Dept Comp Sci Turin Italy Univ Turin Dept Mol Biotechnol & Hlth Sci Turin Italy

ISBN: (纸本)9781538649756

In the last years, B/T cell clonality assessment and Minimal Residual Disease (MRD) monitoring acquired a strong prediction value in the therapy response evaluation of haematologic B disorders, improving patients outcome prediction. Polymerase Chain Reaction (PCR) based methods are the most standardized and widely used techniques, allowing a risk stratification in a variable proportion of patients, depending on the analyzed disease. Since its recently introduction, Next Generation Sequencing (NGS) technology could increase the number of patients with a traceable disease during the clinical course. This issue is strictly associated with an appropriate computational analysis of the huge volume of complex data obtained by NGS. In this context, recently, we presented an innovative bioinformatics approach, called HashClone, an easy-to-use and reliable bioinformatics tool that simultaneously provides clonality assessment and MRD detection over time in patients affected by Mantle Cell Lymphoma (MCL). Actually, HashClone original strategy is organized in three steps that provide the simultaneous analysis of a set of samples reads returning to the corresponding clonotypes list, in which each clone is featured by frequency reads and aligned target nomenclature notification with respect to the reference database [1]. HashClone is composed by four C++ applications combined to implement B-cells clonality assessment in patient's samples. Since its successful preliminary application, in this paper, we present parallelHashClone, an improved version with a parallel implementation of HashClone suite. In detail, the parallelization of this two applications allows to analyze more efficiently the samples from the same patient in parallel. Moreover we integrated parallelHashClone in a Docker container platform that allows to easily install and run the application since the Docker packages parallelHashClone with all its dependencies and libraries. We tested parallelHashClone version for f

关键词： Hash-based algorithm Clonality assessment parallelization

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