发布求助
文献互助 智能选刊 最新文献

Proceedings of the 8th International Workshop on Ultrascale Visualization最新文献

筛选
英文 中文
On-demand unstructured mesh translation for reducing memory pressure during in situ analysis 按需非结构化网格转换，以减少在原位分析期间的记忆压力
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 2013-11-17 DOI: 10.1145/2535571.2535592
J. Woodring, J. Ahrens, T. Tautges, T. Peterka, V. Vishwanath, Berk Geveci
{"title":"On-demand unstructured mesh translation for reducing memory pressure during in situ analysis","authors":"J. Woodring, J. Ahrens, T. Tautges, T. Peterka, V. Vishwanath, Berk Geveci","doi":"10.1145/2535571.2535592","DOIUrl":"https://doi.org/10.1145/2535571.2535592","url":null,"abstract":"When coupling two different mesh-based codes, for example with in situ analytics, the typical strategy is to explicitly copy data (deep copy) from one implementation to another, doing translation in the process. This is necessary because codes usually do not share data model interfaces or implementations. The drawback is that data duplication results in an increased memory footprint for the coupled code. An alternative strategy, which we study in this paper, is to share mesh data through on-demand, fine-grained, run-time data model translation. This saves memory, which is an increasingly scarce resource at exascale, for the increased use of in situ analysis and decreasing memory per core. We study the performance of our method compared against a deep copy with in situ analysis at scale.","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121524117","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: 10
GPU-accelerated molecular visualization on petascale supercomputing platforms 千万亿次超级计算平台上gpu加速的分子可视化
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 2013-11-17 DOI: 10.1145/2535571.2535595
J. Stone, Kirby L. Vandivort, K. Schulten
{"title":"GPU-accelerated molecular visualization on petascale supercomputing platforms","authors":"J. Stone, Kirby L. Vandivort, K. Schulten","doi":"10.1145/2535571.2535595","DOIUrl":"https://doi.org/10.1145/2535571.2535595","url":null,"abstract":"Petascale supercomputers create new opportunities for the study of the structure and function of large biomolecular complexes such as viruses and photosynthetic organelles, permitting all-atom molecular dynamics simulations of tens to hundreds of millions of atoms. Together with simulation and analysis, visualization provides researchers with a powerful \"computational microscope\". Petascale molecular dynamics simulations produce tens to hundreds of terabytes of data that can be impractical to transfer to remote facilities, making it necessary to perform visualization and analysis tasks in-place on the supercomputer where the data are generated. We describe the adaptation of key visualization features of VMD, a widely used molecular visualization and analysis tool, for GPU-accelerated petascale computers. We discuss early experiences adapting ray tracing algorithms for GPUs, and compare rendering performance for recent petascale molecular simulation test cases on Cray XE6 (CPU-only) and XK7 (GPU-accelerated) compute nodes. Finally, we highlight opportunities for further algorithmic improvements and optimizations.","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130577064","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: 55
A classification of scientific visualization algorithms for massive threading 海量线程科学可视化算法分类
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 2013-11-17 DOI: 10.1145/2535571.2535591
K. Moreland, Berk Geveci, K. Ma, Robert Maynard
{"title":"A classification of scientific visualization algorithms for massive threading","authors":"K. Moreland, Berk Geveci, K. Ma, Robert Maynard","doi":"10.1145/2535571.2535591","DOIUrl":"https://doi.org/10.1145/2535571.2535591","url":null,"abstract":"As the number of cores in processors increase and accelerator architectures are becoming more common, an ever greater number of threads is required to achieve full processor utilization. Our current parallel scientific visualization codes rely on partitioning data to achieve parallel processing, but this approach will not scale as we approach massive threading in which work is distributed in such a fine level that each thread is responsible for a minute portion of data. In this paper we characterize the challenges of refactoring our current visualization algorithms by considering the finest portion of work each performs and examining the domain of input data, overlaps of output domains, and interdependencies among work instances. We divide our visualization algorithms into eight categories, each containing algorithms with the same interdependencies. By focusing our research efforts to solving these categorial challenges rather than this legion of individual algorithms, we can make attainable advancement for extreme computing.","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121452763","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: 7
A model for optimizing file access patterns using spatio-temporal parallelism 一个使用时空并行性优化文件访问模式的模型
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 2013-11-17 DOI: 10.1145/2535571.2535593
B. Nouanesengsy, J. Patchett, J. Ahrens, A. Bauer, Aashish Chaudhary, Ross G. Miller, Berk Geveci, G. Shipman, Dean N. Williams
{"title":"A model for optimizing file access patterns using spatio-temporal parallelism","authors":"B. Nouanesengsy, J. Patchett, J. Ahrens, A. Bauer, Aashish Chaudhary, Ross G. Miller, Berk Geveci, G. Shipman, Dean N. Williams","doi":"10.1145/2535571.2535593","DOIUrl":"https://doi.org/10.1145/2535571.2535593","url":null,"abstract":"For many years now, I/O read time has been recognized as the primary bottleneck for parallel visualization and analysis of large-scale data. In this paper, we introduce a model that can estimate the read time for a file stored in a parallel filesystem when given the file access pattern. Read times ultimately depend on how the file is stored and the access pattern used to read the file. The file access pattern will be dictated by the type of parallel decomposition used. We employ spatio-temporal parallelism, which combines both spatial and temporal parallelism, to provide greater flexibility to possible file access patterns. Using our model, we were able to configure the spatio-temporal parallelism to design optimized read access patterns that resulted in a speedup factor of approximately 400 over traditional file access patterns.","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"4613 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125819454","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: 1
Ray tracing and volume rendering large molecular data on multi-core and many-core architectures 基于多核和多核架构的大型分子数据的光线追踪和体绘制
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 2013-11-17 DOI: 10.1145/2535571.2535594
A. Knoll, I. Wald, P. Navrátil, M. Papka, K. Gaither
{"title":"Ray tracing and volume rendering large molecular data on multi-core and many-core architectures","authors":"A. Knoll, I. Wald, P. Navrátil, M. Papka, K. Gaither","doi":"10.1145/2535571.2535594","DOIUrl":"https://doi.org/10.1145/2535571.2535594","url":null,"abstract":"Visualizing large molecular data requires efficient means of rendering millions of data elements that combine glyphs, geometry and volumetric techniques. The geometric and volumetric loads challenge traditional rasterization-based vis methods. Ray casting presents a scalable and memory- efficient alternative, but modern techniques typically rely on GPU-based acceleration to achieve interactive rendering rates. In this paper, we present bnsView, a molecular visualization ray tracing framework that delivers fast volume rendering and ball-and-stick ray casting on both multi-core CPUs and many-core Intel® Xeon Phi™ co-processors, implemented in a SPMD language that generates efficient SIMD vector code for multiple platforms without source modification. We show that our approach running on co- processors is competitive with similar techniques running on GPU accelerators, and we demonstrate large-scale parallel remote visualization from TACC's Stampede supercomputer to large-format display walls using this system.","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129550491","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: 31
An analytical framework for particle and volume data of large-scale combustion simulations 大规模燃烧模拟中颗粒和体积数据的分析框架
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 2013-11-17 DOI: 10.1145/2535571.2535590
F. Sauer, Hongfeng Yu, K. Ma
{"title":"An analytical framework for particle and volume data of large-scale combustion simulations","authors":"F. Sauer, Hongfeng Yu, K. Ma","doi":"10.1145/2535571.2535590","DOIUrl":"https://doi.org/10.1145/2535571.2535590","url":null,"abstract":"This paper presents a framework to enable parallel data analyses and visualizations that combine both Lagrangian particle data and Eulerian field data of large-scale combustion simulations. Our framework is characterized by a new range query based design that facilitates mutual queries between particles and volumetric segments. Scientists can extract complex features, such as vortical structures based on vector field classifications, and obtain detailed statistical information from the corresponding particle data. This framework also works in reverse as it can extract vector field information based on particle range queries. The effectiveness of our approach has been demonstrated by an experimental study on vector field data and particle data from a large-scale direct numerical simulation of a turbulent lifted ethylene jet flame. Our approach provides a foundation for scalable heterogeneous data analytics of large scientific applications.","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124363542","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
Proceedings of the 8th International Workshop on Ultrascale Visualization 第八届超尺度可视化国际研讨会论文集
Proceedings of the 8th International Workshop on Ultrascale Visualization Pub Date : 1900-01-01 DOI: 10.1145/2535571
{"title":"Proceedings of the 8th International Workshop on Ultrascale Visualization","authors":"","doi":"10.1145/2535571","DOIUrl":"https://doi.org/10.1145/2535571","url":null,"abstract":"","PeriodicalId":262085,"journal":{"name":"Proceedings of the 8th International Workshop on Ultrascale Visualization","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117208457","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信