2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV)最新文献

Scalable and Modular Online Data Processing for Ultrafast Computed Tomography Using CUDA Pipelines 使用CUDA管道的超快速计算机断层扫描的可扩展和模块化在线数据处理

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.5555/3018859.3018861

Tobias Frust, G. Juckeland, A. Bieberle

{"title":"Scalable and Modular Online Data Processing for Ultrafast Computed Tomography Using CUDA Pipelines","authors":"Tobias Frust, G. Juckeland, A. Bieberle","doi":"10.5555/3018859.3018861","DOIUrl":"https://doi.org/10.5555/3018859.3018861","url":null,"abstract":"For investigations of rapidly moving structures in opaque technical devices ultrafast electron beam X-ray computed tomography (CT) scanners are available at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). Currently, measurement data must be initially downloaded after each CT scan from the scanner to a data processing machine. Afterwards, cross-sectional images are reconstructed. This limits the application fields of the scanners. For online observations and even automated process control of scanned objects a new modular data processing tool is presented consisting of user-definable pipeline stages that work independently together in a so called data processing pipeline that can keep up with the CT scanner's frame rate of up to 8 kHz. The data processing stages are arbitrarily programmable and combinable and are connected by a fast custom memory pool to optimize data transfer processes. As a result, this processing structure is not limited to CT application only. In order to achieve highest processing performances for the electron beam X-ray CT scanners all relevant data processing steps are individually implemented in separate stages using graphic processing units (GPUs) and NVIDIA's CUDA programming language. Data processing performance tests on two different high-end GPUs (Tesla K20c, GeForce GTX 1080) offer a slice image reconstruction performance that is well-suited for online application.","PeriodicalId":229382,"journal":{"name":"2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131757330","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: 0

In Situ Statistical Analysis for Parametric Studies 参数研究的原位统计分析

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.5555/3018859.3018866

Théophile Terraz, B. Raffin, A. Ribés, Y. Fournier

{"title":"In Situ Statistical Analysis for Parametric Studies","authors":"Théophile Terraz, B. Raffin, A. Ribés, Y. Fournier","doi":"10.5555/3018859.3018866","DOIUrl":"https://doi.org/10.5555/3018859.3018866","url":null,"abstract":"In situ processing proposes to reduce storage needs and I/O traffic by processing results of parallel simulations as soon as they are available in the memory of the compute processes. We focus here on computing in situ statistics on the results of N simulations from a parametric study. The classical approach consists in running various instances of the same simulation with different values of input parameters. Results are then saved to disks and statistics are computed post mortem, leading to very I/O intensive applications. Our solution is to develop Melissa, an in situ library running on staging nodes as a parallel server. When starting, simulations connect to Melissa and send the results of each time step to Melissa as soon as they are available. Melissa implements iterative versions of classical statistical operations, enabling to update results as soon as a new time step from a simulation is available. Once all statistics ar updated, the time step can be discarded. We also discuss two different approaches for scheduling simulation runs: the jobs-in-job and the multi-jobs approaches. Experiments run instances of the Computational Fluid Dynamics Open Source solver Code_Saturne. They confirm that our approach enables one to avoid storing simulation results to disk or in memory.","PeriodicalId":229382,"journal":{"name":"2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129098857","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: 3

Visualization and Analysis Requirements for In Situ Processing for a Large-Scale Fusion Simulation Code 大型核聚变模拟程序现场处理的可视化和分析要求

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.14

James Kress, D. Pugmire, S. Klasky, H. Childs

引用次数: 9

The SENSEI Generic In Situ Interface SENSEI通用的原位界面

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.13

Utkarsh Ayachit, B. Whitlock, M. Wolf, B. Loring, Berk Geveci, David C. Lonie, E. W. Bethel

引用次数: 68

Asynchronous In Situ Connected-Components Analysis for Complex Fluid flows 复杂流体流动的异步原位连接分量分析

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.8

J. McClure, M. Berrill, J. Prins, Cass T. Miller

{"title":"Asynchronous In Situ Connected-Components Analysis for Complex Fluid flows","authors":"J. McClure, M. Berrill, J. Prins, Cass T. Miller","doi":"10.1109/ISAV.2016.8","DOIUrl":"https://doi.org/10.1109/ISAV.2016.8","url":null,"abstract":"The simulation of multiscale physics is an important challenge for scientific computing. For this class of problem, large three-dimensional simulations are performed to advance scientific inquiry. On massively parallel computing systems, the volume of data generated by such approaches can become a productivity bottleneck if the raw data generated from the simulation is analyzed in a post-processing step. To address this, we present a physics-based framework for in situ data reduction that is theoretically grounded in multiscale averaging theory. We show how task parallelism can be exploited to concurrently perform a variety of analysis tasks with data-dependent costs, including the generation of iso-surfaces, morphological analyses, and connected components analysis. All analyses are performed in parallel using distributed memory and use the same domain decomposition as the simulation. A task management framework is constructed to leverage available parallelism within a node for analysis. The capabilities of the framework are to launch asynchronous analysis threads, manage dependencies between different tasks, promote data locality and minimize the impact of data transfers. The framework is applied to analyze GPU-based simulations of two-fluid-phase flow in porous media, generating a set of averaged measures that represents the overall system behavior. We demonstrate how the approach can be applied to perform physically-consistent analysis over fluid sub-regions determined from connected components analysis. Simulations performed on Oak Ridge National Lab's Titan supercomputer are profiled to demonstrate the performance of the associated multi-threaded in situ analysis approach for typical production simulation of two-fluid-phase flow.","PeriodicalId":229382,"journal":{"name":"2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV)","volume":"557 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131258131","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: 3

Early Investigations into Using a Remote RAM Pool with the vl3 Visualization Framework 用vl3可视化框架使用远程RAM池的早期研究

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.10

Dawid Zawislak, B. Toonen, W. Allcock, S. Rizzi, J. Insley, V. Vishwanath, M. Papka

引用次数: 5

An I/O Mini-App Dedicated to In Situ Visualization 一个I/O迷你应用程序，致力于现场可视化

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.11

S. Ziegeler

引用次数: 1

A HYDRA UQ Workflow for NIF Ignition Experiments NIF点火实验的HYDRA UQ工作流

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.6

S. Langer, B. Spears, J. Peterson, J. Field, R. Nora, S. Brandon

引用次数: 13

In-Situ Visual Exploration of Multivariate Volume Data Based on Particle Based Volume Rendering 基于粒子体绘制的多变量体数据的原位可视化勘探

2016 Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV) Pub Date : 2016-11-13 DOI: 10.1109/ISAV.2016.9

Takuma Kawamura, Tomoyuki Noda, Y. Idomura

引用次数: 11