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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Ordered Access Memory Based programmable Hardware Accelerator Parallel Architecture 15

Ordered Access Memory Based Programmable Hardware Accelerato...

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15th IEEE International conference on the Experience of Designing and Application of CAD Systems (CADSM)

作者： Melnyk, Anatoliy Melnyk, Viktor Lviv Polytech Natl Univ Dept Comp Engn S Bandery Str 12 Lvov Ukraine John Paul II Catholic Univ Lublin Fac Math IT & Landscape Architecture Konstantynow 1H Lublin Poland Lviv Polytech Natl Univ Dept Informat Technol Secur S Bandery Str 12 Lvov Ukraine

ISBN: (纸本)9781728100531

programmable hardware accelerator architecture and information processing method based on a new model of computation with parallel ordered data and command access is proposed in a paper. The principles of the formation, compilation and execution of the computer program in the hardware accelerator according to the proposed model are given. The advantages of the proposed hardware accelerator architecture over traditional ones are highlighted.

关键词： programmable hardware accelerator parallel architecture ordered access memory model of computation high-performance computing

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NIOECM: A network I/O event control mechanism to provide fairness of network performance among vms with same resource configuration in virtualized environment 12th

NIOECM: A network I/O event control mechanism to provide fai...

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12th International conference on Internet and Distributed Computing Systems, IDCS 2019

作者： Lee, Jaehak Kang, Jihun Yu, Heonchang Department of Computer Science and Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9783030349134

In the virtualization environment, a hypervisor scheduler determines the degree of shared resource occupancy of the virtual machine (VM) according to the degree of CPU processing and it provides a fair CPU processing on virtual machines (VMs). But VMs are experiencing unfair network performance due to the hypervisor scheduler’s policy occupying resources based on CPU processing time. In this paper, we present NIOECM which is a network IO event control technique that controls the network-intensive VM’s network IO event to guarantee a fairness network performance of VMs which have the same resource configuration. The NIOECM performs a network delay processing on the network-intensive VMs which have a high network I/O event set. As a result, the network-intensive VMs which have a low network I/O event set will have more chance to occupy the network resource. In the result of experiments, our approach provides more fairness of network performance and does not give a performance interference on VM which is performing another task. © Springer Nature Switzerland AG 2019.

关键词： Virtual machine

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Multi-Stage Recovery Resilience: A Case Study of the Dique Canal 10th

Multi-Stage Recovery Resilience: A Case Study of the Dique C...

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10th International conference on Computational Logistics (ICCL)

作者： Romero-Rodriguez, Daniel Savachkin, Alex Ardila-Rueda, Weimar Sierra-Altamiranda, Alvaro Daza-Escorcia, Julio-Mario Univ Norte Dept Ind Engn Barranquilla Colombia Univ S Florida Dept Ind Management & Syst Engn Tampa FL 33620 USA Konrad Lorenz Univ Fdn Program Ind Engn Bogota Colombia

ISBN: (纸本)9783030311407;9783030311391

Disasters may cause severe damage to a community with long lasting consequences that will impede a short recovery. In high magnitude events the total recovery time is measured in months or years. These long recoveries are performed in multiple stages and different recovery rates. Traditional resilience metrics have been designed to study strictly increasing recovery functions, which is not a valid assumption for the multi-stage recovery case. This paper defines a model to measure resilience for a multi-stage recovery scenario, where the recovery process is performed in two or more stages with possibly different recovery rates. A linear approximation metric is proposed to improve resilience level estimation accuracy. The new metric is tested on a case study of the Dique Canal breach in Colombia occurred in 2010. The adapted resilience metric is combined with an optimization model to maximize the average performance on multi-stage scenarios, enabling decision makers to decide the best strategy per stage for a predefined budget. A comparative analysis confirms that the proposed model offers a better resilience estimation than previous linear average performance metrics.

关键词： Resilience metric Multi-stage recovery Natural disaster Dique canal

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A detailed simulation study of the push-based protocol for critical data dissemination in vehicular named data networks

A detailed simulation study of the push-based protocol for c...

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2019 International conference on Networking and Network Applications, NaNA 2019

作者： Yaqub, Muhammad Azfar Ahmed, Syed Hassan Kim, Dongkyun School of Computer Science &engineering Kyungpook National University Daegu41566 Korea Republic of Department of Computer Science Georgia Southern University StatesboroGA30548 United States

ISBN: (纸本)9781728126296

In this paper, we present a simulation study of push VNDN, a recently proposed named data network (NDN) based forwarding mechanism for vehicular networking. NDN is an instance of information centric network (ICN) that is based on content-centric networking instead of the traditional host-centric networking. While the assumptions in the protocol are interesting, where an push-based forwarding mechanism is proposed to assist the forwarding of critical data generated randomly in the network, yet they bring further challenges into the picture, such as, flooding of data packets in the network, data loops and overall interest satisfaction delay. We present a set of results that evaluate the network performance in the presence of random network parameters. Our results show that the push VNDN scheme proves to effectively disseminate the critical data in the network with the tradeoff of high data forwarding and interest satisfaction delays. © 2019 IEEE.

关键词： Named Data Networks (NDN) Pull-based Data Forwarding Push-based Data Forwarding Vehicular NDN

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Prediction of Plant Lipocalin Genes based on Convolutional Neural Networks 2019

Prediction of Plant Lipocalin Genes based on Convolutional N...

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Proceedings of the 2019 International conference on Artificial Intelligence and computer Science

作者： Siquan Hu Zhizhou Liao Haitao Jia School of Computer and Communication Engineering University of Science and Technology Beijing School of Communication and Information Engineering University of Electronic Science and Technology of China

ISBN: (纸本)9781450371506

Lipocalins play a key role in regulating biological functions such as modulation of cell growth and metabolism,binding of cellsurface receptors,nerve growth and regeneration,and regulating of immune *** and analyzing plant lipocalins has become one of the important issues in the study of lipocalin *** methods such as protein structure analysis,cell localization and phylogenetic studies are complex and very expensive,which makes current exploration progress of plant lipocalins still slow compared with deep learning *** this paper,based on convolutional neural network,we constructed a deep learning model called 'LCNet',which has sensitivity and specificity for plant lipocalin genes of 0.953 and 0.941 *** addition,we further verified the prediction performance of LCNet model by studying the similarities and differences of gene relative expression levels between lipocalin genes already identified biologically in Oryza and the genes predicted as Oryza lipocalin by LCNet model during the process of absorbing and transporting *** combination of deep learning and biological experiments has high precision,simple operation and low cost,which can reduce the workload of biologists and can be extended to other proteins to solve similar problems.

关键词： lipocalin gene Convolutional neural network Biological experiments

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Energy efficient cooperative edge computing with multi-source multi-relay devices 21

Energy efficient cooperative edge computing with multi-sourc...

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Yao, Mianyang Chen, Long Liu, Tonglai Wu, Jigang School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781728120584

As the extension of cloud computing, multi-access edge computing (MEC) can better support applications to accomplish larger tasks. Existing works on energy optimization of MEC systems fail to utilize multi-relay diversity, which plays a vital role to reduce the computing power consumption of mobile devices by leveraging different offloading modes. In this paper, we propose a novel computing architecture with multi-source, multi-relay, and a single edge server in an orthogonal frequency division multiplexing access (OFDMA) wireless network. The application task on a mobile device can be calculated locally, offloaded to the relay device and completed on the relay device, offloaded to the edge server directly or via a relay device with cooperative communication and executed in the edge server. The two optimization targets are total energy consumption minimization problem, and maximum energy consumption per source-relay pair devices minimization problem. For the first sub-problem, we design an optimal total energy consumption algorithm (OTCA) based on bipartite matching. The optimal energy consumption assignment algorithm (OECAA) is designed for the second sub-problem with an approximation ratio of (1+ϵ). Extensive simulation results show that OTCA outperforms random algorithm by 53.14% on the reduction of total energy consumption, and algorithm OECAA outperforms OTCA by 30.58% on the reduction of maximum energy consumption per source-relay pair devices. © 2019 IEEE.

关键词： Energy utilization

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high-level approaches for leveraging deep-memory hierarchies on modern supercomputers 2nd

High-level approaches for leveraging deep-memory hierarchies...

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2nd Workshop on Software Challenges to Exascale Computing, SCEC 2018

作者： Gómez-Iglesias, Antonio Arora, Ritu Intel Corporation Hillsboro United States Texas Advanced Computing Center The University of Texas at Austin Austin United States

ISBN: (纸本)9789811377280

There is a growing demand for supercomputers that can support memory-intensive applications to solve large-scale problems from various domains. Novel supercomputers with fast and complex memory subsystems are being provisioned to meet this demand. While complex and deep-memory hierarchies offer increased memory-bandwidth they can also introduce additional latency. Optimizing the memory usage of the applications is required to improve performance. However, this can be an effort-intensive and a time-consuming activity if done entirely manually. Hence, high-level approaches for supporting the memory-management and memory-optimization on modern supercomputers are needed. Such scalable approaches can contribute towards supporting the users at the open-science data centers - mostly domain scientists and students - in their code modernization efforts. In this paper, we present a memory management and optimization workflow based on high-level tools. While the workflow can be generalized for supercomputers with different architectures, we demonstrate its usage on the Stampede2 system at the Texas Advanced Computing Center that contains both Intel Knights Landing and Intel Xeon processors, and each Knights Landing node offers both DDR4 and MCDRAM. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Supercomputers

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ALIC: A Superpixel Segmentation Algorithm Based on Autonomous Attachment 19

ALIC: A Superpixel Segmentation Algorithm Based on Autonomou...

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3rd International conference on computer Science and Application Engineering (CSAE)

作者： Wang, Juan Shi, Hao Liu, Min Cai, Linkang Ke, Cong Deng, Bin Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ene Wuhan Hubei Peoples R China Hubei Univ Technol Wuhan Huaan Sci & Technol Co Ltd Wuhan Peoples R China Hubei Univ Technol Hubei Collaborat Innovat Ctr Highefficiency Utili Wuhan Peoples R China

ISBN: (纸本)9781450362948

In this paper, we reconsider the problem of low computational efficiency in traditional superpixel segmentation methods based on clustering. We propose a superpixel segmentation method based on autonomous attachment named ALIC, which use a simpler and more efficient distance measurement method to accelerate the algorithm. Simultaneously, the allocation of pixel autonomous attached fully considers the natural continuity between pixels, and each pixel can share label with its neighbors, which allows us to obtain better boundary performance. In the experiment, our method only achieves convergence in five iterations. On the basis of obtaining more sensitive boundaries, our algorithm improves the operation speed. (On a simple CPU core, it only takes about 0.1s to segment a 481x321 image into 400 homogenic superpixels).

关键词： Superpixel Gradient descent Autonomous attachment Natural comtinuity

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Classifying Mixed Patterns of Proteins in high-Throughput Microscopy Images Using Deep Neural Networks 15th

Classifying Mixed Patterns of Proteins in High-Throughput Mi...

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15th International conference on Intelligent Computing, ICIC 2019

作者： Zhang, Enze Zhang, Boheng Hu, Shaohan Zhang, Fa Wan, Xiaohua High Performance Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China School of Software Tsinghua University Beijing China

ISBN: (纸本)9783030267629

Proteins contribute significantly in most body functions within cells, and are essential to the physiological activities of every creature. Microscopy imaging, as a remarkable technique, is applied to observe and identify proteins in different kinds of cells, by which the analysis results are critical to the biomedical studies. However, as the development of high-throughput microscopy imaging, images of protein microscopy are generated in a faster pace ever, making it harder for experts to manually identify them. For better digging and understanding the information of the proteins in those huge amounts of images, it is urgent for methods to identify the mixed-patterned proteins within various cells automatically and accurately. Here in this paper, we design some novel and effective data preparation and preprocessing methods for high-throughput microscopy protein datasets. We propose ACP layer and "buffering" layers, using them to design customized architectures for some typical CNN classifiers with new inputs and head parts. The modifications let the models be more adaptive and accurate to our task. We train the models in more effective and efficient optimization strategies that we design, e.g., cycle learning with learning rate scheduling. Besides, greedy selection of thresholds and multi-sized models ensembling in the post-process stage are proposed to further improve the prediction accuracy. Our experimental results based on Human Protein Atlas datasets demonstrates that the proposed methods show an excellent performance in mixed-patterned protein classifications to date, even beyond the state-of-the-art architecture GapNet-PL by 0.02 to 0.03 in F1 score. The whole work reveals the usefulness of our methods for high-throughput microscopy protein images identification. © 2019, Springer Nature Switzerland AG.

关键词： Proteins

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A robust approach to the cell switch-off problem in 5G ultradense networks

A robust approach to the cell switch-off problem in 5G ultra...

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2019 International conference on high performance Computing and Simulation, HPCS 2019

作者： Luna, Francisco Zapata-Cano, Pablo H. Valenzuela-Valdes, Juan F. Padilla, Pablo Universidad de Málaga Dept. of Languages and Computer Science Málaga Spain Dept. Signal Theory Telematics and Communications Universidad de Granada Granada Spain

ISBN: (纸本)9781728144849

Ultra-dense networks (UDNs) are recognized as one of the key enabling technologies of the fifth generation (5G) networks, as they allow for an efficient spatial reuse of the spectrum, which is required to meet the traffic demands foreseen for the next coming years. However, the power consumption of UDNs, with potentially hundreds of small base stations (SBSs) within each macrocell, is a major concern for the cellular operators, and has to be properly addressed prior to the actual deployment of these 5G networks. Among the different existing approaches to address this issue, a widely accepted strategy lies in the selective deactivation of SBSs, but without compromising the QoS provided to the User Equipments (UEs). This is known as the Cell Switch-Off (CSO) problem. The typical formulation of this problem is based on estimations of the traffic demand of the User Equipments (UEs) within the network. But these estimations could not be met. This work approaches these uncertain scenarios by extending the CSO problem with additional objectives that account for the robustness of the solutions to disturbances in these traffic estimates. To do so, a computationally demanding Monte-Carlo sampling is used to evaluate each solution. To manage such an increasingly large computing cost, a parallel version of the NSGA-II algorithm that is able to run on a computing platform composed of more than 500 cores has been used. It is able to compute in roughly 2 hours, an accumulated execution time of more than 42 days, which is within the expected timeframe of operators to make changes in the network configuration. © 2019 IEEE.

关键词： Multiobjective optimization

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