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检索条件"任意字段=12th Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 1250 条记录，以下是221-230 订阅

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Decreasing the Learning Cost of Offline parallel Application Optimization Strategies

Decreasing the Learning Cost of Offline Parallel Application...

引用

euromicro conference on parallel, distributed and network-based processing

作者： Gustavo Berned Fábio D. Rossi Marcelo C. Luizelli Antonio Carlos S. Beck Arthur F. Lorenzon Federal University of Pampa - Campus Alegrete Brazil Federal Institute of Education Science and Technology Farroupilha Brazil Institute of Informatics Federal University of Rio Grande do Sul Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Many parallel applications do not scale as the number of threads increases, which means that executing them with the maximum possible number of threads will not always deliver the best outcome in performance, energy consumption, or the tradeoff between both (represented by the energy-delay product- EDP). Given that, several strategies, online and offline, have already been proposed to rightly tune the number of threads according to the application. While the former can capture some behaviors that can only be known at runtime, the latter do not impose any execution overhead and can use more efficient and costly algorithms. However, these learning algorithms in static strategics may take several hours, precluding their use or a smooth migration across different systems. In this scenario, we propose a generic methodology for such offline strategies to significantly decrease the learning time by inferring the execution behavior of parallel applications using smaller input sets than the ones used by the target applications. through the execution of eighteen well-known benchmarks on two multicore processors, we show that our methodology is capable of converging to results that are very close to those that use the regular input set, but converging 84.7% faster, on average. We also show that such a strategy delivers better results than a dynamic one, presenting an EDP 7.7% lower, on average, when executing the applications with the number of threads found during learning. Finally, we also compare our learning methodology with an exhaustive search. It has an average learning cost (i.e., the time spent by our search algorithm to find the best configuration) of only 3.1% to optimize the EDP of the entire benchmark set 1 .

关键词： Instruction sets Benchmark testing Predictive models Energy consumption Runtime parallel processing

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parallelization of Variable Rate Decompression through Metadata

Parallelization of Variable Rate Decompression through Metad...

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euromicro conference on parallel, distributed and network-based processing

作者： Lennart Noordsij Steven van der Vlugt Mohamed A. Bamakhrama Zaid Al-Ars Peter Lindstrom ASML Netherlands Synopsys Corporation Netherlands TU Delft Netherlands Lawrence Livermore National Laboratory United States of America

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Data movement has long been identified as the biggest challenge facing modern computer systems' designers. To tackle this challenge, many novel data compression algorithms have been developed. Often variable rate compression algorithms are favored over fixed rate. However, variable rate decompression is difficult to parallelize. Most existing algorithms adopt a single parallelization strategy suited for a particular HW platform. Such an approach fails to harness the parallelism found in diverse modern HW architectures. We propose a parallelization method for tiled variable rate compression algorithms that consists of multiple strategies that can be applied interchangeably. this allows an algorithm to apply the strategy most suitable for a specific HW platform. Our strategies are based on generating metadata during encoding, which is used to parallelize the decoding process. To demonstrate the effectiveness of our strategies, we implement them in a state-of-the-art compression algorithm called ZFP. We show that the strategies suited for multicore CPUs are different from the ones suited for GPUs. On a CPU, we achieve a near optimal decoding speedup and an overhead size which is consistently less than 0.04% of the compressed data size. On a GPU, we achieve average decoding rates of up to 100 GiB/s. Our strategies allow the user to make a trade-off between decoding throughput and metadata size overhead.

关键词： Decoding Metadata Compression algorithms Encoding Data transfer Acceleration Multicore processing

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conference Organization PDP 2019

Conference Organization PDP 2019

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euromicro conference on parallel, distributed and network-based processing

conference Organization PDP 2019

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Combining Spark and Snort Technologies for Detection of network Attacks and Anomalies: Assessment of Performance for the Big Data Framework 19

Combining Spark and Snort Technologies for Detection of Netw...

引用

12th International conference on Security of Information and networks (SIN)

作者： Kotenko, Igor Komashinsky, Nikolay Russian Acad Sci St Petersburg Inst Informat & Automat 14 Th Liniya39 St Petersburg 199178 Russia

ISBN: (纸本)9781450372428

the paper proposes an approach to security information processing in order to detect computer attacks and network anomalies based on big data technologies. the main contribution of the work is in the development, implementation and investigation of the proposed combined framework for processing security data using parallel computing environment and measuring the performance of the implemented system for detection of network attacks and anomalies. the research goal is to increase the performance of attack detection (under the given requirements for accuracy of solutions) compared to the traditional IDS application. the implemented approach is built using the open source systems Snort and Spark. the paper discusses the capabilities and performance assessment of parallel data processing in order to detect computer attacks and network anomalies, as well as key principles of working with big data. the presented main results of an experimental performance evaluation of the applied approach confirm its high efficiency for analyzing network traffic and security events.

关键词： cyber security intrusion detection computer attack anomaly network traffic big data signature methods Spark Snort

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IEEE Computer Society Technical & conference Activities Board

IEEE Computer Society Technical & Conference Activities Boar...

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euromicro conference on parallel, distributed and network-based processing

Provides a listing of current committee members and society officers.

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An Interference-Aware Application Classifier based on Machine Learning to Improve Scheduling in Clouds

An Interference-Aware Application Classifier Based on Machin...

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euromicro conference on parallel, distributed and network-based processing

作者： Vinícius Meyer Dionatrã F. Kirchoff Matheus L. da Silva César A.F. De Rose Polytechnic School Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

To maximize resource utilization and system throughput in cloud platforms, hardware resources are often shared across multiple virtualized services or applications. In such a consolidated scenario, performance of applications running concurrently in the same physical host can be negatively affected due to interference caused by resource contention. this should be taken into account for efficient scheduling of such applications and performance prediction at user level. Nevertheless, resource scheduling in cloud computing is usually based solely on resource capacity, implemented by heuristics such as bin-packing. Our previous work has introduced an interference-aware scheduling model for web-applications considering their resource utilization profile, and to classify applications we applied fixed interference intervals based on common utilization patters. Although this resulted in placements with better overall results, we observed that some applications with more dynamic workload patterns were wrongly classified with intervals. In this paper, we propose an alternative to the use of intervals and present an interference-aware application classifier for cloud-based applications that deals better with dynamic workloads. Our classifier defines automatically interference levels ranges combining two well-known machine learning techniques: Support Vector Machines and K-Means. Preliminary experiments evaluated the applied machine learning techniques in three quality metrics: Accuracy, F1-Score and Rand Index, observing rates over 80%. the proposed solution creates a workload-aware fine-grained classification that was compared with previous work over different workload scenarios. the results demonstrate that our classification approach improves the placement efficiency by 23% on average.

关键词： Interference Cloud computing Measurement Machine learning Support vector machines Degradation Processor scheduling

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Single High-Value Region Detection based on Image Equal-Segmentation 12

Single High-Value Region Detection Based on Image Equal-Segm...

引用

12th International conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC 2020

作者： Chen, Jinlong Huang, Lei Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Computer Information and Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781728184487

With the development of computer hardware equipment and deep neural network (DNN) technology, computer vision has received more and more attention from AI developers and researchers. In the field of digital image processing, people try to use some calculation methods and techniques to identify objects in images. In the process of finding the rules, this method is called target detection, which is also a research hotspot in the field of computer vision. High-value area detection is based on target detection technology. On the basis of image detection of digital images, some methods of image are used to segment more concerned areas in the target. In this paper, the method of equally segmenting the image takes the image containing the detected object as input and performs detection to achieve the purpose of detecting high-value areas. the steps of the algorithm are divided into high-value area estimation, image equal segmentation, image data attenuation, algorithm acceleration, target fusion and classification. (Abstract) © 2020 IEEE.

关键词： Deep neural networks

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A new execution model for improving performance and flexibility of CAPE 26

A new execution model for improving performance and flexibil...

引用

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

作者： Van Long Tran Renault, Eric Xuan Huyen Do Viet Hai Ha Univ Paris Saclay CNRS SAMOVAR Telecom SudParis 9 Rue Charles Fourier F-91011 Evry France Hue Ind Coll Hue Vietnam Hue Univ Coll Sci Hue Vietnam Hue Univ Coll Educ Hue Vietnam

ISBN: (纸本)9781538649756

Checkpointing-Aided parallel Execution (CAPE) is a framework that is based on checkpointing technique and serves to automatically translates and execute OpenMP programs on distributed-memory architectures. through some comparisons with MPI, CAPE has demonstrated high-performance and the potential for fully compatibility with OpenMP on distributed-memory systems. However, there is still a long way to improve the performance, flexibility, portability and capability. this paper presents the new execution model for CAPE that improves its performance and makes CAPE even more flexible.

关键词： CAPE Checkpointing OpenMP HPC distributed Computing parallel computing

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Improving availability in distributed tuple spaces via sharing abstractions and replication strategies 26

Improving availability in distributed tuple spaces via shari...

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

作者： Buravlev, Vitaly De Nicola, Rocco Lafuente, Alberto Lluch Mezzina, Claudio Antares IMT Sch Adv Studies Lucca Lucca Italy Tech Univ Denmark Lyngby Denmark

ISBN: (纸本)9781538649756

Data availability is a key aspect of modern distributed systems. We discuss an extension of coordination languages based on tuple spaces with programming abstractions for sharing data and guaranteeing availability with different consistency guarantees. Data can be spread over the system according to user-specified replica placement strategies and user-specified consistency requirements. the framework takes care then of low-level management of the replicas, so that the programmer can just focus on the business logic of the application. We advocate that the proposed programming primitives are beneficial for data-oriented applications where different kinds of data may have different needs in terms of availability and consistency.

关键词： tuple space dynamic replication data consistency network

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Memory-aware tree partitioning on homogeneous platforms 26

Memory-aware tree partitioning on homogeneous platforms

引用

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

作者： Gou, Changjiang Benoit, Anne Marchal, Loris Ecole Normale Super Lyon Lab LIP Lyon France ECNU Shanghai Peoples R China

ISBN: (纸本)9781538649756

Scientific applications are commonly modeled as the processing of directed acyclic graphs of tasks, and for some of them, the graph takes the special form of a rooted tree. this tree expresses both the computational dependencies between tasks and their storage requirements. the problem of scheduling/traversing such a tree on a single processor to minimize its memory footprint has already been widely studied. Hence, we move to parallel processing and study how to partition the tree for a homogeneous multiprocessor platform, where each processor is equipped with its own memory. We formally state the problem of partitioning the tree into subtrees such that each subtree can be processed on a single processor and the total resulting processing time is minimized. We prove that the problem is NP-complete, and we design polynomial-time heuristics to address it. An extensive set of simulations demonstrates the usefulness of these heuristics. © 2018 IEEE.

关键词： memory aware parallel processing tree partitioning

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