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

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Injecting Execution Traces into a Model-Driven Framework for program Analysis

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conference on Software Technologies - Applications and Foundations (STAF)

作者： la Fosse, Thibault Beziers Tisi, Massimo Mottu, Jean-Marie IMT Atlantique INRIA LS2N AtlanMod Team Nantes France

ISBN: (纸本)9783319747309;9783319747293

Model-Driven Engineering (MDE) has been successfully used in static program analysis. Frameworks like MoDisco inject the program structure into a model, available for further processing by query and transformation tools, e.g., for program understanding, reverse-engineering, modernization. In this paper we present our first steps towards extending MoDisco with capabilities for dynamic program analysis. We build an injector for program execution traces, one of the basic blocks of dynamic analysis. Our injector automatically instruments the code, executes it and captures a model of the execution behavior of the program, coupled with the model of the program structure. We use the trace injection mechanism for model-driven impact analysis on test sets. We identify some scalability issues that remain to be solved, providing a case study for future efforts in improving performance of model-management tools.

关键词： Model-Driven Engineering Dynamic program analysis Execution traces

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Toward a programmable analysis and visualization framework for interactive performance analytics 1

Toward a programmable analysis and visualization framework f...

引用

1st IEEE/ACM Workshop on programming and performance Visualization Tools, ProTools 2019

作者： Islam, Tanzima Ayala, Alexis Jensen, Quentin Ibrahim, Khaled Department of Computer Science Texas State University United States Department of Computer Science Western Washington University United States Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9781728160269

Understanding the performance characteristics of applications in modern HPC environments is becoming more challenging due to the increase in the architectural and programming complexities. HPC software developers rely on sources such as hardware counters and event traces to infer performance problems while focusing on designing mitigation strategies. A large number of in-house tools exist in the community, which indicate replicated effort. This paper presents a customizable framework for analyzing performance measurements and visualizing through a web-based interactive dashboard for interactively exploring a large volume of hierarchical information. In this paper, we analyze three ECP applications as use cases and identify as well as optimize problematic resource utilization behaviors exposed by our visualizations. This framework is a step towards a unified platform for visual identification of performance scaling bottlenecks to ease the collaboration between application developers, performance analysts, and hardware vendors. © 2019 IEEE.

关键词： Visualization

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Quantitative and qualitative assessment of large-scale interventions in a first-year experience program 126

Quantitative and qualitative assessment of large-scale inter...

引用

126th ASEE Annual conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Menezes, Gustavo B. Allen, Emily L. Ragusa, Gisele Schiorring, Eva Nerenberg, Paul S. California State University Los Angeles United States University of Southern California United States Canada College

In this Work-in-Progress paper, we report on the challenges and successes of a large-scale First-Year Engineering and computer Science program at an urban comprehensive university, using quantitative and qualitative assessment results. Large-scale intervention programs are especially relevant to comprehensive minority serving institutions (MSIs) that serve a high percentage of first-generation college students who often face academic and socioeconomic barriers. Our program was piloted in 2015 with 30 engineering students, currently enrolls 60 engineering and computer science students, and is expected to grow to over 200 students by Fall 2020. The first-year program interventions include: (i) block schedules for each cohort in the first year;(ii) redesigned project-based introduction to engineering and introduction to computer science courses;(iii) an introduction to mechanics course, which provides students with the foundation needed to succeed in the traditional physics sequence;and (iv) peer-led supplemental instruction (SI) workshops for Calculus, Physics and Chemistry. A faculty mentorship program was implemented to provide additional support to students, but was phased out after the first year. Challenges encountered in the process of expanding the program include administrative, such as scheduling and training faculty and SI leaders;barriers to improvement of math and science instruction;and more holistic concerns such as creating a sense of community and identity for the program. Quantitative data on academic performance includes metrics such as STEM GPA and persistence, along with the Force Concept Inventory (FCI) for physics. Qualitative assessments of the program have used student and instructor surveys, focus groups, and individual interviews to measure relationships among factors associated with college student support and to extract student perspectives on what works best for them. Four years of data tell a mixed story, in which the qualitative effect o

关键词： Students

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

引用

15th EAI International conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Using a Graph Visualization Tool for Parallel program Dynamic Visualization and Communication Analysis

Using a Graph Visualization Tool for Parallel Program Dynami...

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International Supercomputing conference (ISC) on high performance

作者： Stringhini, Denise Gomes, Pedro Spoljaric Fazenda, Alvaro Univ Fed Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9783030024659;9783030024642

Parallel program visualization and performance analysis tools have a high cost of development. As a consequence, there are many of these tools that are proprietary what makes difficult their adoption by the general community. This work introduces the use of general purpose open software for visualization and characterization of parallel programs. In particular, the use of an open graph visualization tool is presented as a case study for the dynamic communication characterization of a NAS parallel benchmark. The results show that a general purpose open graph tool could be used to analyze some important aspects related to the communication of parallel message passing programs.

关键词： Visualization

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Cider: highly efficient processing of densely overlapped communities in big graphs 3rd

Cider: Highly efficient processing of densely overlapped com...

引用

3rd APWeb and WAIM Joint conference on Web and Big Data, APWeb-WAIM 2019

作者： Chen, Yadi Bai, Wen Chen, Runyuan Wu, Di Ye, Guoqiao Huang, Zhichuan School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9783030260712

As one of the most fundamental operations in graph analytics, community detection is to find groups of vertices that are more densely connected internally than with the rest of the graph. However, the detection of densely overlapped communities in big graphs is extremely challenging due to high time complexity. In this paper, we propose an effective and efficient graph algorithm called Cider to detect densely overlapped communities in big graphs. The intuition behind our algorithm is to exploit inherent properties of densely overlapped communities, and expand the community by minimizing its conductance. To make Cider more efficient, we extend the algorithm to expand the community more quickly by merging vertices in batches. We explicitly derive the time complexity of our algorithm and conclude that it can be implemented in near-linear time. Besides, we also implement a parallelized version of Cider to further improve its performance. Experimental results on real datasets show that our algorithms outperform existing approaches in terms of Flake Out Degree Fraction (FODF) and F1 Score. © 2019, Springer Nature Switzerland AG.

关键词： Graphic methods

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Sequence-Based Prediction of Hot Spots in Protein-RNA Complexes Using an Ensemble Approach 15th

Sequence-Based Prediction of Hot Spots in Protein-RNA Comple...

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15th International conference on Intelligent Computing, ICIC 2019

作者： Zhao, Le Zhang, Sijia Xia, Junfeng School of Computer Science and Technology Anhui University HefeiAnhui230601 China Institutes of Physical Science and Information Technology Anhui University HefeiAnhui230601 China

ISBN: (纸本)9783030267629

RNA-binding hot spots are dominant and fundamental residues that contribute most to the binding free energy of protein-RNA interfaces. As experimental methods for identifying hot spots are expensive and time-consuming, high-efficiency computational approaches are required in predicting hot spots on a large scale. In this work, we proposed a sequence-based machine learning method to predict hot spots in protein-RNA complexes. We extracted 83 relative independent physicochemical features from a set of the 544 properties in AAindex1. Each physicochemical feature was combined with the predicted relative accessible surface area (RASA) and substitution probability feature from Blocks Substitution Matrix (BLOSUM) for training models by support vector machine (SVM) and k-nearest neighbor algorithm (k-NN). The combinations of the 166 individual models were explored and 33 top-performance models were selected to construct the final ensemble classifier by a majority voting technique. The ensemble classifier outperformed the state-of-the-art computational methods, yielding F1 score of 0.742 and AUC of 0.824 on the independent test set. © 2019, Springer Nature Switzerland AG.

关键词： Proteins

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high-Dimensional Global Optimization Method for high-Frequency Electronic Design

High-Dimensional Global Optimization Method for High-Frequen...

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作者： Torun, Hakki Mert Swaminathan, Madhavan School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

Efficient global optimization of microwave systems is a very challenging task that emerges in importance for rapid design closure and discovery of novel structures. As the operating frequency increases, additional difficulties in design optimization occur due to increased nonlinearity, creating a high-dimensional nonconvex response surface. Bayesian optimization (BO) is a promising solution to solve such problems. However, BO-based methods suffer from the curse of dimensionality, where the number of simulations required for convergence increases exponentially with the number of parameters. In this paper, we address this problem and propose a new BO-based high-dimensional global optimization method titled, Bayesian Optimization with Deep Partioning Tree (DPT-BO). DPT-BO leverages a novel DPT that allows for rapid coverage of high-dimensional sample spaces and utilizes an additive Gaussian process (ADD-GP) with a fully additive decomposition, making it more suitable for high-frequency design optimization. We apply DPT-BO to different optimization test functions along with three high-frequency design applications, namely, maximizing signal integrity in high-speed channels, minimizing losses of substrate integrated waveguides with air cavity, and maximizing efficiency of wireless power transfer systems. The results show that DPT-BO finds control parameters that provide better performance in less CPU time compared to other techniques. © 2019 IEEE.

关键词： Millimeter waves

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Identifying Cancer Biomarkers from high-Throughput RNA Sequencing Data by Machine Learning 15th

Identifying Cancer Biomarkers from High-Throughput RNA Seque...

引用

15th International conference on Intelligent Computing, ICIC 2019

作者： Zhang, Zishuang Liu, Zhi-Ping School of Control Science and Engineering Shandong University JinanShandong250061 China

ISBN: (纸本)9783030269685

In cancer progression, the expression level of relevant genes will change significantly in tumors comparing to their healthy counterparts. Therefore, the discovery of specific genes serving as biomarkers is of practical significance for diagnosis and prognosis. The available high-throughput ‘-omic’ datasets provide unprecedented resources and opportunities of deriving cancer biomarkers, such as the public RNA-sequencing data generated by the Cancer Genome Atlas (TCGA) consortium. Here, we explore the identification of biomarker genes in 12 types of cancers from the classification effects in control and disease samples by machine learning. We firstly identify differentially expressed genes individually. Then, we implement feature selection by integrating recursive feature reduction and random forest classification with feature ranking. The final feature number will be determined via a parsimony principle that the features will be as few as possible, while they are still with the highest classification accuracy. In each cancer, the biomarker genes are then evaluated by tenfold cross-validations via several classification algorithms. We find extreme learning machine achieves the best classification performance when compared to the other methods. The further gene enrichment analyses indicate the dysfunctional and pathogenic mechanism in these identified biomarkers. © 2019, Springer Nature Switzerland AG.

关键词： RNA

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A New Method of Named Entity Recognition on Biology Domain

A New Method of Named Entity Recognition on Biology Domain

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International conference on Intelligent Computation Technology and Automation, ICICTA

作者： Wande Zhuang Chengfan Li Qingjie Zhang Xuehai Ding Junjuan Zhao School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute of Advanced Communication and Data Science Shanghai University Shanghai China

It is the phenotypic data of microbial taxonomy that is of great significance. It can be used not only for the identification of microbial taxonomy but also for the study of the interaction between human and the environment. There are bilstm-CRF, Bert-CRF and other models are commonly used for named entity recognition (NER) recently. However, these methods do not consider the sequence information of neurons and do not analyze the hierarchical structure of sentences. In the field of biological phenotypes, there are longer sentences and complex words, the named entity recognition with neural network performs not very well. Based on this, this paper proposed a new NER method which is named BERT-ON-LSTM to identify and extract microbial phenotypic entities from the literature. This paper improved the Bidirectional Encoder Representations from Transformers (Bert) which enabled the model to learn the hierarchical structure information and be more sensitive to words that require long-term memory. And then, conditional random field (CRF) is used to decode since it can be calculated the joint probability distribution of the whole tag sequence. The experimental results on three data sets prove that our model is effective.

关键词： Biological system modeling Neurons Taxonomy Memory management Transformers Data models Probability distribution

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