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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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An Architectural Framework for Accelerating Dynamic Parallel Algorithms on Reconfigurable Hardware 51

An Architectural Framework for Accelerating Dynamic Parallel...

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51st Annual IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Chen, Tao Srinath, Shreesha Batten, Christopher Suh, G. Edward Cornell Univ Ithaca NY 14850 USA

ISBN: (纸本)9781538662403

In this paper, we propose ParallelXL, an architectural framework for building application-specific parallel accelerators with low manual effort. The framework introduces a task based computation model with explicit continuation passing to support dynamic parallelism in addition to static parallelism. In contrast, today's high-level design frameworks for accelerators focus on static data-level or thread-level parallelism that can be identified and scheduled at design time. To realize the new computation model, we develop an accelerator architecture that efficiently handles dynamic task generation and scheduling as well as load balancing through work stealing. The architecture is general enough to support many dynamic parallel constructs such as fork-join, data-dependent task spawning, and arbitrary nesting and recursion of tasks, as well as static parallel patterns. We also introduce a design methodology that includes an architectural template that allows easily creating parallel accelerators from high-level descriptions. The proposed framework is studied through an FPGA prototype as well as detailed simulations. Evaluation results show that the framework can generate high-performance accelerators targeting FPGAs for a wide range of parallel algorithms and achieve an average of 4.0x speedup over an eight-core out-of-order processor (24.1x over a single core), while being 11.8x more energy efficient.

关键词： Task analysis Parallel processing Hardware Field programmable gate arrays Computational modeling Heuristic algorithms computer architecture

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What-if Scenario Analysis for IT Services in Hybrid Cloud Environments with BDMaaS+

What-if Scenario Analysis for IT Services in Hybrid Cloud En...

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IFIP/IEEE international symposium on Integrated Network Management

作者： Walter Cerroni Luca Foschini Genady Ya. Grabarnik Larisa Shwartz Mauro Tortonesi Department of Electrical University of Bologna Bologna Italy Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Mathematics and Computer Science St. John’s University Queens NY USA Operational Innovation IBM TJ Watson Research Center NY USA Department of Mathematics and Computer Science University of Ferrara Ferrara Italy

ISBN: (纸本)9781728106182;9783903176157

The high sophistication of IT services, more and more often operated over distributed hybrid Cloud environments, makes it extremely difficult to evaluate the impact of changes at the business level over service architecture before deploying them. The performance assessment of possible alternative deployments calls for decision support tools to enable what-if scenario analysis, which requires also to adopt realistic Cloud network models, currently not available. To fill that gap, this paper illustrates the potential of our Business-Driven Management as a Service Plus (BDMaaS+) support that eases management operations in the above context, and presents a thorough experimental evaluation of our solution. Collected results demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business Service Level Agreement (SLA) changes and data center additions to optimize the performance of an enterprise class IT service deployed, in a realistic hybrid Cloud environment, before (re-)enacting them.

关键词： Cloud computing Data centers Business computer architecture Software Tools Optimization

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Textural and Intensity Feature Based Retinal Vessels Classification for the Identification of Hypertensive Retinopathy 21

Textural and Intensity Feature Based Retinal Vessels Classif...

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21st international Multi Topic Conference, INMIC 2018

作者： Ahmad, Faiza Khan Sial, Muhammad Rafay Yousaf, Adnan Khan, Fahad Department of Electrical Engineering Superior University Lahore Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad Campus Pakistan

ISBN: (纸本)9781538675366

Hypertensive retinopathy is a retinal disease which results as a consequence of high blood pressure. Its early detection is necessary in reducing the likelihood of permanent visual damage. The percentage of people suffering from Hypertension is high, so it is required to develop a system which automatically detects the presence of this disease. high blood pressure damages retinal vessels and due to which arteries width is reduced. This damage can be analyzed by extracting the blood vessels, classifying the segmented vessels into veins and arteries and finally computing their Arteriovenous Ratio, which is an important measure to establish whether a person is suffering from Hypertensive Retinopathy or not. This research presents a technique for automatic classification of blood vessels of retina using different classifiers and the performance of each classifier is compared on same feature set. A novel combination of features is used for classification of vessels, which is an essential step for calculation of Arteriovenous Ratio and subsequently the detection of Hypertensive Retinopathy. MATLAB has been used for this research. The results that are achieved using the proposed feature set show's 89% accuracy. © 2018 IEEE.

关键词： Ophthalmology

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Explaining the Workings Principle of Cloud-based Multi-factor Authentication architecture on Banking Sectors

Explaining the Workings Principle of Cloud-based Multi-facto...

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Amity international Conference on Artificial Intelligence (AICAI)

作者： Bose, Rajesh Chakraborty, Srabanti Roy, Sandip Simplex Infrastruct Ltd Data Ctr IT Kolkata India Elitte Inst Engn & Management Comp Sci & Technol Sodepur India Brainware Grp Inst Comp Sci & Engn Kolkata India

ISBN: (纸本)9781538693469

Already in the initial years of the 21st century, Cloud computing has left an indelible mark as one of the key emergent technologies that already has begun revolutionizing the way we look at Information Technology. The banking sectors have also ventured into cloud-based systems, solutions, and services. Transferring banking applications to the cloud reduces bulk investments, time for initial set-up and also carbon footprint. However, if the security aspect of banking applications to the cloud is not managed comprehensively, it can lead to irreversible damage to the brand, loss of customer trust, and enormous liabilities. Our research objective is to offer cloud-based multi-factor authentication architecture to the financial institutions which would provide high levels of data protection. We propose a triple-stage verification process to establish identity and root out any malware attacks during access of banking applications by users from remote locations.

关键词： Carbon footprint Cloud computing Malware Multi-factor authentication Security

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Lazy Persistency: a high-Performing and Write-Efficient Software Persistency Technique 18

Lazy Persistency: a High-Performing and Write-Efficient Soft...

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45th ACM/IEEE Annual international symposium on computer architecture (ISCA)

作者： Alshboul, Mohammad Tuck, James Solihin, Yan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781538659847

Emerging Non-Volatile Memories (NVMs) are expected to be included in future main memory, providing the opportunity to host important data persistently in main memory. However, achieving persistency requires that programs be written with failure-safety in mind. Many persistency models and techniques have been proposed to help the programmer reason about failure-safety. They require that the programmer eagerly flush data out of caches to make it persistent. Eager persistency comes with a large overhead because it adds many instructions to the program for flushing cache lines and incurs costly stalls at barriers to wait for data to become durable. To reduce these overheads, we propose Lazy Persistency (LP), a software persistency technique that allows caches to slowly send dirty blocks to the NVMM through natural evictions. With LP, there are no additional writes to NVMM, no decrease in write endurance, and no performance degradation from cache line flushes and barriers. Persistency failures are discovered using software error detection (checksum), and the system recovers from them by recomputing inconsistent results. We describe the properties and design of LP and demonstrate how it can be applied to loop-based kernels popularly used in scientific computing. We evaluate LP and compare it to the state-of-the-art Eager Persistency technique from prior work. Compared to it, LP reduces the execution time and write amplification overheads from 9% and 21% to only 1% and 3%, respectively.

关键词： Emerging Memory Technology Memory Systems Multi-core and Parallel architectures

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OTTer: A Scalable high-Resolution Encrypted Traffic Identification Engine 21st

OTTer: A Scalable High-Resolution Encrypted Traffic Identifi...

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21st international symposium on Research in Attacks, Intrusions and Defenses (RAID)

作者： Papadogiannaki, Eva Halevidis, Constantinos Akritidis, Periklis Koromilas, Lazaros Niometrics Singapore Singapore

ISBN: (纸本)9783030004705;9783030004699

Several security applications rely on monitoring network traffic, which is increasingly becoming encrypted. In this work, we propose a pattern language to describe packet trains for the purpose of fine-grained identification of application-level events in encrypted network traffic, and demonstrate its expressiveness with case studies for distinguishing Messaging, Voice, and Video events in Facebook, Skype, Viber, and WhatsApp network traffic. We provide an efficient implementation of this language, and evaluate its performance by integrating it into our proprietary DPI system. Finally, we demonstrate that the proposed pattern language can be mined from traffic samples automatically, minimizing the otherwise high ruleset maintenance burden.

关键词： Traffic analysis OTT applications Network monitoring

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Characterizing Packet Losses in Vehicular Networks

Characterizing Packet Losses in Vehicular Networks

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作者： Bocharova, Irina Kudryashov, Boris Rabi, Maben Lyamin, Nikita Dankers, Wouter Frick, Erik Vinel, Alexey Department of Information Systems St.-Petersburg University of Information Technologies Mechanics and Optics St.-Petersburg197101 Russia School of Information Technology Halmstad University Halmstad301 18 Sweden Volvo GTT Göteborg418 75 Sweden AstaZero Hällered Sandhult504 91 Sweden Institute of Computer Science University of Tartu Tartu50090 Estonia Department of Electrical Engineering Western Norway University of Applied Sciences Bergen5020 Norway

To enable testing and performance evaluation of new connected and autonomous driving functions, it is important to characterize packet losses caused by degradation in vehicular (V2X) communication channels. In this paper we suggest an approach to constructing packet loss models based on the so-called Pseudo-Markov chains (PMC). The PMC-based model needs only short training sequences, has low computational complexity, and yet provides more precise approximations than known techniques. We show how to learn PMC models from either empirical records of packet receptions, or from analytical models of fluctuations in the received signal strength. In particular, we validate our approach by applying it on: 1) V2X packet reception data collected from an active safety test run, which used the LTE network of the AstaZero automotive testing site in Sweden, and 2) variants of the Rician fading channel models corresponding to two models of correlations of packet losses. We also show that initializing the Baum-Welch algorithm with a second order PMC model leads to a high accuracy model. © 1967-2012 IEEE.

关键词： Fading channels

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high performance Data Driven Agent-based Modeling Framework for Simulation of Commute Mode Choices in Metropolitan Area 21

High performance Data Driven Agent-based Modeling Framework ...

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21st IEEE international Conference on Intelligent Transportation Systems, ITSC 2018

作者： Park, Byung H. Abdul Aziz, H.M. Morton, April stewart, Robert Computer Science and Mathematics Division Computational Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9781728103235

Active transportation, human-powered transportation modes such as walking and bicycling, not only reduces the carbon footprint from the transportation sector but also promotes healthy living by offering opportunities for people to build physical activity into their daily routine. To encourage active transportation through urban planning and public campaigns, it is of significant importance to infer factors that substantially influence commuters in their transportation mode choice process. This necessitates a flexible and repeatable tool that can evaluate how a policy is perceived by individual commuters and convert their decisions into macro level understanding. This paper introduces one such effort that is specifically designed for studies of transportation mode choices in metropolitan areas. It provides results from a high-resolution data driven simulation based on high performance computing implementation of the agent-based model framework for home-to-work commute trips. The framework uses a graph-partition based technique that can leverage the interaction structure of agents within a geographic proximity and can boost the simulation execution time. Further, based on a flexible design, it can run ABM with different levels of computing resources-from multi core workstations to an HPC grid. The framework has been tested on the Titan Cray XK7 supercomputer of the Oak Ridge Leadership computing Facility. © 2018 IEEE.

关键词： Supercomputers

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Using Molecular Fingerprints as Descriptors in Toxicity Prediction: A Survey

Using Molecular Fingerprints as Descriptors in Toxicity Pred...

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IEEE international Conference on Bioinformatics and Biomedicine

作者： Rana S. Mahmoud Ahmed H. Yousef Center for Informatics Science (CIS) Nile UniversityGizaEgypt School of Information Technology and Computer Science (ITCS) Nile UniversityGizaEgypt

ISBN: (纸本)9781728118680

During humans' lifetime, their bodies deal with different chemicals through various sources. Chemical toxicity is a challenging problem that needs rapid and efficient methods for evaluation of environmental chemicals, or medications development. computer science helps in toxicity prediction through building models from pre-tested compounds by learning from data. These models raise a flag to avoid trying some combinations for trial in wet-lab, which reduces the high cost of clinical trials. A compound chemical structure features are represented by Molecular Fingerprint. This survey searches for answers to Q1 How Fingerprints are dependent on the type of the prediction model? Q2 How valid are Fingerprints in toxicity prediction?. The Toxicology in the 21st Century challenge is the case study on this work. By showing the impact of fingerprints as descriptors on different predictive modeling techniques. The survey shows the independence of fingerprints as descriptors, as they work with different QSAR models. Also a need is noticed to test fingerprints separately without other descriptors in toxicity prediction to test their performance.

关键词： chemistry computing feature extraction knowledge based systems molecular biophysics QSAR toxicology Fingerprints Descriptors molecular biology Knowledge based systems Toxicology Quantitative structure-Activity Relationship Feature extraction Subject Headings chemical compounds Toxicity Building models

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Joint high-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer's Disease 21st

Joint High-Order Multi-Task Feature Learning to Predict the ...

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21st international Conference on Medical Image computing and computer Assisted Intervention (MICCAI)

作者： Brand, Lodewijk Wang, Hua Huang, Heng Risacher, Shannon Saykin, Andrew Shen, Li Colorado Sch Mines Dept Comp Sci Golden CO 80401 USA Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA USA Indiana Univ Dept Radiol & Imaging Sci Dept BioHlth Informat Indianapolis IN 46204 USA Univ Penn Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA

ISBN: (纸本)9783030009281;9783030009274

Alzheimer's disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United states and worldwide age, the prevalence of Alzheimer's disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe. By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer's Disease Neuroimaging Initiative (ADNI), we propose a new Joint high-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD.

关键词： Alzheimer's disease Multi-modal Longitudinal Tensor

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