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

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high performance Computing (HPC) Implementation: A Survey 1

High Performance Computing (HPC) Implementation: A Survey

引用

1st Indonesian Association for Pattern Recognition International conference, INAPR 2018

作者： Assiroj, Priati Hananto, April Lia Fauzi, Ahmad Hendric Spits Warnars, Harco Leslie Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Information System Faculty of Technology and Computer Science United States Buana Perjuangan University Faculty of Technology and Computer Science Information Technology Karawang Indonesia

ISBN: (纸本)9781538694220

To maintain excellence in global hyper-competitive economies in the upcoming decades, manufacturers must improve the design, development, and distribution of subsequent product generations, production technologies, equipment, and processes. high-performance computing (HPC) solves the application of significant challenges using computer modeling, simulation, and analytical technology. By using HPC technology, products design will be faster, making and testing prototypes will save time, the production process can be simplified, the cost of innovation will decrease, and high-value innovation will be quickly developed. This study uses the PRISMA method, based on predetermined keywords and analyzes the algorithms, frameworks and programming languages and the processes that are most commonly used to optimize the Graphics Processing Unit (GPU). The results of this study can be a reference for future research on the implementation of high-performance Computing (HPC) Application by combine modeling, simulation, and analysis. © 2018 IEEE.

关键词： Graphics processing unit

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DTN-Knca: A high Throughput Routing Based on Contact Pattern Detection in DTNs 42

DTN-Knca: A High Throughput Routing Based on Contact Pattern...

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42nd Annual IEEE-computer-Society computers, Software and Applications (COMPSAC) conference - Staying Smarter in a Smartening World

作者： Wang, Weitao Bai, Yuebin Feng, Peng Gu, Yuhao Liu, Shuai Jiang, Wenhao Huang, Jun Beihang Univ Sch Engn & Comp Sci Beijing 100191 Peoples R China Peking Univ Ctr Energy Efficient Comp & Applicat CECA Beijing 100871 Peoples R China

ISBN: (纸本)9781538626672

In current routing algorithms based on encounter history in Delay-Tolerant Networks (DTNs), packets are always forwarded to nodes with highest probability to reach destination node. However, to the best of our knowledge, no analytical node transient contact pattern detection to achieve high performance, is reported in the literature. In this letter, DTN-Knca - a novel routing which detects frequently encountered nodes' transient contact patterns by correlation analysis is proposed. The trace-driven simulations demonstrate the higher throughput of DTN-Knca in comparison to the state-of-the-art DTN typical routing algorithms based on the encounter history knowledge.

关键词： DTN routing correlation analysis contact pattern detection

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4th International conference on Smart Computing and Communications, SmartCom 2019

4th International Conference on Smart Computing and Communic...

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4th International conference on Smart Computing and Communications, SmartCom 2019

ISBN: (纸本)9783030341381

The proceedings contain 40 papers. The special focus in this conference is on Smart Computing and Communications. The topics include: I2P Anonymous Communication Network Measurement and Analysis;plot Digitizing over Big Data Using Beam Search;trust-Aware Resource Provisioning for Meteorological Workflow in Cloud;do Top Social Apps Effect Voice Call? Evidence from Propensity Score Matching Methods;Cognitive Hierarchy Based Coexistence and Resource Allocation for URLLC and eMBB;smart Custom Package Decision for Mobile Internet Services;a Space Dynamic Discovery Scheme for Crowd Flow of Urban City;automated Classification of Attacker Privileges Based on Deep Neural Network;nnD: Shallow Neural Network Based Collision Decoding in IoT Communications;a Smart Roll Wear Check Scheme for Ensuring the Rolling Quality of Steel Plates;subordinate Relationship Discovery Method Based on Directed Link Prediction;an Elephant Flow Detection Method Based on Machine Learning;AI Enhanced Automatic Response System for Resisting Network Threats;A Cross-Plane Cooperative DDoS Detection and Defense Mechanism in Software-Defined Networking;a Hardware Trojan Detection Method Design Based on TensorFlow;Resolving the Loop in high-Level SDN program for Multi-table Pipeline Compilation;DC Coefficients Recovery from AC Coefficients in the JPEG Compression Scenario;a performance Evaluation Method of Coal-Fired Boiler Based on Neural Network;Analysis and Prediction of Commercial Big Data Based on WIFI Probe;Design and Optimization of Camera HAL Layer Based on Android;an Improved Prediction Model for the Network Security Situation;music Rhythm Customized Mobile Application Based on Information Extraction;computational Challenges and Opportunities in Financial Services;A Shamir Threshold Model Based Recoverable IP Watermarking Scheme.

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Improving regional cyberinfrastructure services through collaboration: Cyberteam for the Rocky Mountain Advanced Computing Consortium 19

Improving regional cyberinfrastructure services through coll...

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conference on Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC)

作者： Monaghan, Andrew J. Milash, Brett Magle, Tobin Hauser, Thomas Cheatham, Thomas E. Burns, Patrick J. Knuth, Shelley Orendt, Anita M. White, Suzi Paschal, Dawn Siegel, H. J. Kaiser, Timothy Johnson, Andrew M. Yeager, Becky Univ Colorado Res Comp Boulder CO 80309 USA Univ Utah Ctr High Perf Comp Salt Lake City UT USA Univ Wisconsin Steenbock Lib Madison WI 53706 USA Colorado State Univ Cent IT & CSU Lib Ft Collins CO 80523 USA Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Colorado State Univ Acad Net & Comp Resources Ft Collins CO 80523 USA Univ Colorado Univ Lib Boulder CO 80309 USA

ISBN: (纸本)9781450372275

Advances in the volume, diversity, and complexity of research data and associated workflows requires enhanced capabilities to access, secure, reuse, process, analyze, understand, curate, share, and preserve data. To address this need at the regional level the University of Colorado, Colorado State University, and the University of Utah formed a "Cyberteam" in 2017 to provide cyberinfrastructure (CI) support to researchers at institutions in the Rocky Mountain Advanced Computing Consortium (RMACC) encompassing states across the Intermountain West. The Cyberteam is comprised of CI professionals across the three institutions who collaborate closely, sharing expertise and resources. Since its establishment, the Cyberteam has worked to broaden accessibility and options for computing, storage, and data publishing for RMACC researchers;enhance training on data- and workflow-oriented topics;improve engagement with researchers using CI;and better understand user needs and challenges. One key accomplishment has been the development of a series of focus group and survey instruments to achieve better understanding the CI needs and challenges of researchers across a diverse spectrum of disciplines. This paper provides an overview of the RMACC Cyberteam's objectives, accomplishments, challenges, and future direction.

关键词： cyberinfrastructure facilitation high performance computing workflows

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A preemptive scheduling for bursty flow in data center

A preemptive scheduling for bursty flow in data center

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International conference on Intelligent Computing, Communication and Devices, ICCD 2017

作者： Zhang, Xingyan Yang, Li Liu, Limin Hu, Luokai School of Computer Science Hubei University of Education WuhanHubei China Hubei Education Cloud Service Engineering Technology Research Center Hubei University of Education WuhanHubei430205 China

ISBN: (纸本)9789811089435

Data centers provide services for various real-time applications, such as social networks, instance message, which produce a large number of bursty and urgent mice flow. However, the traditional flow queuing model cannot schedule these flow effectively, which leading the performance loss for some important applications. In this paper, a preemptive scheduling in data center for bursty flow (PSBF) was proposed. The scheduling leverage the preemptive scheduling for more critical flow and set the right queue for other data flow to keep the high aggregated throughput. The experiment and analysis show that our scheduling can handle bursty flow better than traditional flow scheduling for data center. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Scheduling

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An edge computing platform for intelligent internet data center operational monitoring 2nd

An edge computing platform for intelligent internet data cen...

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2nd International Workshop on high performance Computing for Advanced Modeling and Simulation in Nuclear Energy and Environmental Science, HPCMS 2018, and 1st International Workshop on HPC Supported Data Analytics for Edge Computing, HiDEC 2018 held at the 32nd ACM International conference on Supercomputing, ACM ICS 2018

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wan, Jian Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China

ISBN: (纸本)9789813299863

The explosive growth in cloud-based services, big data analytics, and artificial intelligence related services provisioning leads to the rapid growth of construction of large scale Internet data centers (IDCs). Modern IDCs are equipped with various sensors to monitor its healthy operation and maintenance states, such as temperature, thermal distribution, air flow, etc. However, fine-grained, single-node level and even mainboard level monitoring including server resource consumption and power consumption is still needed for more progressive resource multiplexing and job scheduling in IDCs. In this paper, we propose an edge computing platform for intelligent Internet data center operational monitoring, which integrates wireless sensors and on-board built-in sensors to sense and collect the data of data center operation and maintenance. The edge computing based solution reduces the latency for data transportation to central clouds and reduces the amount of data and enhances the real time resource capping decisions in data centers. © Springer Nature Singapore Pte Ltd 2019.

关键词： Energy efficiency

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Online Anomaly Detection Using Machine Learning and HPC for Power System Synchrophasor Measurements

Online Anomaly Detection Using Machine Learning and HPC for ...

引用

IEEE International conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Ren, Huiying Hou, Zhangshuan Etingov, Pavel Pacific Northwest Natl Lab Energy & Environm Directorate Richland WA 99354 USA Pacific Northwest Natl Lab Energy & Environm Directorate Seattle WA USA

ISBN: (纸本)9781538635964

\ This paper presents an online early anomaly detection framework for phasor measurement units (PMUs) to monitor power system dynamics and help prevent blackouts using machine learning approaches. Dynamical machine learning solutions including state space model and Kalman filter are presented in this study to learn the nonlinear and nonstationary PMU measurements and accurately predict system behaviors in real-time. The anomalies can be detected within seconds by comparing the predicted system behaviors with the real system observations. The method proposed in this framework uses PMU data with a given time window (e.g., 5 seconds) using a dynamic nonlinear model, and then predicts system behaviors during the following time window. high prediction accuracy is achieved by applying the dynamic nonlinear model to the real-world PMU measurements - the anomalies detected are successfully validated given the recorded real- world events. high-performance-computing (HPC) techniques are utilized to further reduce computational time to provide real time power system situational awareness.

关键词： Phasor measurement units online anomaly detection machine learning dynamic nonlinear model Kalman filter high-performance computing

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Accelerating CNN Algorithm with Fine-grained Dataflow Architectures 20

Accelerating CNN Algorithm with Fine-grained Dataflow Archit...

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20th IEEE International conference on high performance Computing and Communications (HPCC) / 16th IEEE International conference on Smart City (SmartCity) / 4th IEEE International conference on Data Science and Systems (DSS)

作者： Xiang, Taoran Feng, Yujing Ye, Xiaochun Tan, Xu Li, Wenming Zhu, Yatao Wu, Meng Zhang, Hao Fan, Dongrui Chinese Acad Sci ICT State Key Lab Comp Architecture Beijing Peoples R China UCAS Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538666142

Convolutional Neural Network(CNN) is a hot and state-of-the-art algorithm which is widely used in applications such as face recognition, intelligent monitoring, image recognition and text recognition. Because of its high computational complexity, many efficient hardware accelerators have been proposed to exploit high degree of parallel processing for CNN. However, accelerators which are implemented on FPGAs and ASICs usually sacrifice generality for higher performance and lower power consumption. Other accelerators, such as GPUs, are general enough, but they lead to higher power consumption. Fine-grained dataflow architectures, which break conventional Von Neumann architectures, show natural advantages in processing CNN-like algorithms with high computational efficiency and low power consumption. At the same time, it remains broadly applicable and adaptable. In this paper, we propose a scheme for implementing and optimizing CNN on fine-grained dataflow architecture based accelerators. The experiment results reveal that by using our scheme, the performance of AlexNet running on the dataflow accelerator is 3.11x higher than that on NVIDIA Tesla K80, and the power consumption of our hardware is 8.52x lower than that of K80.

关键词： fine-grained dataflow Convolutional Neural Network general accelerator data reuse high parallel

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Work-in-Progress: Reconfigurable accelerator on FPGA for scientific computing : From a space-borne instrument to a high-performance computing data center

Work-in-Progress: Reconfigurable accelerator on FPGA for sci...

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Proceedings of the International conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)

作者： J. P. Cobos Carrascosa D. Hernández Expósito J. L. Ramos Mas B. Aparicio del Moral A. Sánchez Gómez M. Balaguer A. C. López Jiménez D. Orozco Suárez J. C. del Toro Iniesta IAA-CSIC Instituto de Astrofísica de Andalucía Granada Spain

We present a scientific computing accelerator on FPGA that uses hundreds of processors working in parallel organized in several SIMD cores. The accelerator is installed within an Ethernet network and acts as a high-performance computing server. A prototype is presented for processing solar images and achieves a great performance that can compete with a cluster.

关键词： Field programmable gate arrays computer architecture program processors Scientific computing Instruments Ethernet Servers

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Effective program Debloating via Reinforcement Learning 18

Effective Program Debloating via Reinforcement Learning

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ACM SIGSAC conference on computer and Communications Security (CCS)

作者： Heo, Kihong Lee, Woosuk Pashakhanloo, Pardis Naik, Mayur Univ Penn Philadelphia PA 19104 USA Hanyang Univ Seoul South Korea

ISBN: (纸本)9781450356930

Prevalent software engineering practices such as code reuse and the "one-size-fits-all" methodology have contributed to significant and widespread increases in the size and complexity of software. The resulting software bloat has led to decreased performance and increased security vulnerabilities. We propose a system called Chisel to enable programmers to effectively customize and debloat programs. Chisel takes as input a program to be debloated and a high-level specification of its desired functionality. The output is a reduced version of the program that is correct with respect to the specification. Chisel significantly improves upon existing program reduction systems by using a novel reinforcement learning-based approach to accelerate the search for the reduced program and scale to large programs. Our evaluation on a suite of 10 widely used UNIX utility programs each comprising 13-90 KLOC of C source code demonstrates that Chisel is able to successfully remove all unwanted functionalities and reduce attack surfaces. Compared to two state-of-the-art program reducers C-Reduce and Perses, which time out on 6 programs and 2 programs respectively in 12 hours, Chisel runs up to 7.1x and 3.7x faster and finishes on all programs.

关键词： program debloating reinforcement learning

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