Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization最新文献

Strawman: A Batch In Situ Visualization and Analysis Infrastructure for Multi-Physics Simulation Codes 多物理场仿真代码的批量现场可视化和分析基础设施

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828625

Matthew Larsen, E. Brugger, H. Childs, J. Eliot, Kevin S. Griffin, C. Harrison

引用次数: 34

Loosely Coupled In Situ Visualization: A Perspective on Why It's Here to Stay 松散耦合原位可视化:为什么它会留在这里的观点

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828623

James Kress, S. Klasky, N. Podhorszki, J. Choi, H. Childs, D. Pugmire

引用次数: 29

An Approach to Lowering the In Situ Visualization Barrier 降低原位可视化障碍的一种方法

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828618

T. Fogal, J. Krüger

引用次数: 1

Infrastructure for In Situ System Monitoring and Application Data Analysis 现场系统监测基础设施及应用数据分析

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828621

J. Brandt, K. Devine, A. Gentile

{"title":"Infrastructure for In Situ System Monitoring and Application Data Analysis","authors":"J. Brandt, K. Devine, A. Gentile","doi":"10.1145/2828612.2828621","DOIUrl":"https://doi.org/10.1145/2828612.2828621","url":null,"abstract":"We present an architecture for high-performance computers that integrates in situ analysis of hardware and system monitoring data with application-specific data to reduce application runtimes and improve overall platform utilization. Large-scale high-performance computing systems typically use monitoring as a tool unrelated to application execution. Monitoring data flows from sampling points to a centralized off-system machine for storage and post-processing when root-cause analysis is required. Along the way, it may also be used for instantaneous threshold-based error detection. Applications can know their application state and possibly allocated resource state, but typically, they have no insight into globally shared resource state that may affect their execution. By analyzing performance data in situ rather than off-line, we enable applications to make real-time decisions about their resource utilization. We address the particular case of in situ network congestion analysis and its potential to improve task placement and data partitioning. We present several design and analysis considerations.","PeriodicalId":413309,"journal":{"name":"Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134068026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Lessons Learned from Building In Situ Coupling Frameworks 从构建原位耦合框架中学到的经验教训

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828622

Matthieu Dorier, Matthieu Dreher, T. Peterka, J. Wozniak, Gabriel Antoniu, B. Raffin

引用次数: 25

In Situ Analysis as a Parallel I/O Problem 作为并行I/O问题的原位分析

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828620

S. Ziegeler, Chuck Atkins, A. Bauer, L. Pettey

引用次数: 8

ParaView Catalyst: Enabling In Situ Data Analysis and Visualization ParaView Catalyst:实现现场数据分析和可视化

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612.2828624

Utkarsh Ayachit, A. Bauer, Berk Geveci, P. O’leary, K. Moreland, Nathan Fabian, Jeff Mauldin

{"title":"ParaView Catalyst: Enabling In Situ Data Analysis and Visualization","authors":"Utkarsh Ayachit, A. Bauer, Berk Geveci, P. O’leary, K. Moreland, Nathan Fabian, Jeff Mauldin","doi":"10.1145/2828612.2828624","DOIUrl":"https://doi.org/10.1145/2828612.2828624","url":null,"abstract":"Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. Yet these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis moves some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently perform analysis, generate relevant output data, and visualize results concurrent with a running simulation. In this paper, we provide an overview of the Catalyst framework and some of the success stories.","PeriodicalId":413309,"journal":{"name":"Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization","volume":"434 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129389907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 171

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization 第一届现场基础设施实现极端尺度分析和可视化研讨会论文集

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-11-15 DOI: 10.1145/2828612

G. Weber

引用次数: 0

Enabling Adaptive Scientific Workflows Via Trigger Detection 通过触发检测实现自适应科学工作流

Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization Pub Date : 2015-08-19 DOI: 10.1145/2828612.2828619

M. Salloum, Janine Bennett, Ali Pinar, Ankit Bhagatwala, Jacqueline H. Chen

{"title":"Enabling Adaptive Scientific Workflows Via Trigger Detection","authors":"M. Salloum, Janine Bennett, Ali Pinar, Ankit Bhagatwala, Jacqueline H. Chen","doi":"10.1145/2828612.2828619","DOIUrl":"https://doi.org/10.1145/2828612.2828619","url":null,"abstract":"Next generation architectures necessitate a shift away from traditional workflows in which the simulation state is saved at prescribed frequencies for post-processing analysis. While the need to shift to in situ workflows has been acknowledged for some time, much of the current research is focused on static workflows, where the analysis that would have been done as a post-process is performed concurrently with the simulation at user-prescribed frequencies. Recently, research efforts are striving to enable adaptive workflows, in which the frequency, composition, and execution of computational and data manipulation steps dynamically depend on the state of the simulation. Adapting the workflow to the state of simulation in such a data-driven fashion puts extremely strict efficiency requirements on the analysis capabilities that are used to identify the transitions in the workflow. In this paper we build upon earlier work on trigger detection using sublinear techniques to drive adaptive workflows. Here we propose a methodology to detect the time when sudden heat release occurs in simulations of turbulent combustion. Our proposed method provides an alternative metric that can be used along with our former metric to increase the robustness of trigger detection. We show the effectiveness of our metric empirically for predicting heat release for two use cases.","PeriodicalId":413309,"journal":{"name":"Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121925290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19