2012 International Conference for High Performance Computing, Networking, Storage and Analysis最新文献_第5页

Peta-scale Lattice Quantum Chromodynamics on a Blue Gene/Q supercomputer 蓝色基因/Q超级计算机上的peta级晶格量子色动力学

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.96

J. Doi

引用次数: 14

Parallel Bayesian network structure learning with application to gene networks 并行贝叶斯网络结构学习及其在基因网络中的应用

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.90

Olga Nikolova, S. Aluru

引用次数: 18

Extreme-scale UQ for Bayesian inverse problems governed by PDEs 偏微分方程控制下贝叶斯反问题的极值UQ

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.56

T. Bui-Thanh, Carsten Burstedde, O. Ghattas, James Martin, G. Stadler, L. Wilcox

{"title":"Extreme-scale UQ for Bayesian inverse problems governed by PDEs","authors":"T. Bui-Thanh, Carsten Burstedde, O. Ghattas, James Martin, G. Stadler, L. Wilcox","doi":"10.1109/SC.2012.56","DOIUrl":"https://doi.org/10.1109/SC.2012.56","url":null,"abstract":"Quantifying uncertainties in large-scale simulations has emerged as the central challenge facing CS&E. When the simulations require supercomputers, and uncertain parameter dimensions are large, conventional UQ methods fail. Here we address uncertainty quantification for large-scale inverse problems in a Bayesian inference framework: given data and model uncertainties, find the pdf describing parameter uncertainties. To overcome the curse of dimensionality of conventional methods, we exploit the fact that the data are typically informative about low-dimensional manifolds of parameter space to construct low rank approximations of the covariance matrix of the posterior pdf via a matrix-free randomized method. We obtain a method that scales independently of the forward problem dimension, the uncertain parameter dimension, the data dimension, and the number of cores. We apply the method to the Bayesian solution of an inverse problem in 3D global seismic wave propagation with over one million uncertain earth model parameters, 630 million wave propagation unknowns, on up to 262K cores, for which we obtain a factor of over 2000 reduction in problem dimension. This makes UQ tractable for the inverse problem.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120964808","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: 113

ValuePack: Value-based scheduling framework for CPU-GPU clusters ValuePack: CPU-GPU集群的基于值的调度框架

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.111

Vignesh T. Ravi, M. Becchi, G. Agrawal, S. Chakradhar

引用次数: 14

Optimization principles for collective neighborhood communications 社区集体通信的优化原则

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.86

T. Hoefler, Timo Schneider

{"title":"Optimization principles for collective neighborhood communications","authors":"T. Hoefler, Timo Schneider","doi":"10.1109/SC.2012.86","DOIUrl":"https://doi.org/10.1109/SC.2012.86","url":null,"abstract":"Many scientific applications operate in a bulk-synchronous mode of iterative communication and computation steps. Even though the communication steps happen at the same logical time, important patterns such as stencil computations cannot be expressed as collective communications in MPI. We demonstrate how neighborhood collective operations allow to specify arbitrary collective communication relations during runtime and enable optimizations similar to traditional collective calls. We show a number of optimization opportunities and algorithms for different communication scenarios. We also show how users can assert constraints that provide additional optimization opportunities in a portable way. We demonstrate the utility of all described optimizations in a highly optimized implementation of neighborhood collective operations. Our communication and protocol optimizations result in a performance improvement of up to a factor of two for small stencil communications. We found that, for some patterns, our optimization heuristics automatically generate communication schedules that are comparable to hand-tuned collectives. With those optimizations in place, we are able to accelerate arbitrary collective communication patterns, such as regular and irregular stencils with optimization methods for collective communications. We expect that our methods will influence the design of future MPI libraries and provide a significant performance benefit on large-scale systems.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127064742","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: 47

Combining in-situ and in-transit processing to enable extreme-scale scientific analysis 结合原位和中转处理，实现极端尺度的科学分析

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.31

Janine Bennett, H. Abbasi, P. Bremer, R. Grout, A. Gyulassy, Tong Jin, S. Klasky, H. Kolla, M. Parashar, Valerio Pascucci, P. Pébay, D. Thompson, Hongfeng Yu, Fan Zhang, Jacqueline H. Chen

{"title":"Combining in-situ and in-transit processing to enable extreme-scale scientific analysis","authors":"Janine Bennett, H. Abbasi, P. Bremer, R. Grout, A. Gyulassy, Tong Jin, S. Klasky, H. Kolla, M. Parashar, Valerio Pascucci, P. Pébay, D. Thompson, Hongfeng Yu, Fan Zhang, Jacqueline H. Chen","doi":"10.1109/SC.2012.31","DOIUrl":"https://doi.org/10.1109/SC.2012.31","url":null,"abstract":"With the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123692912","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: 210

Cray Cascade: A scalable HPC system based on a Dragonfly network Cray Cascade:一个基于蜻蜓网络的可扩展高性能计算系统

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.39

Greg Faanes, A. Bataineh, D. Roweth, T. Court, E. Froese, Robert Alverson, Tim Johnson, Joe Kopnick, Mike Higgins, James Reinhard

引用次数: 247

Characterizing and mitigating work time inflation in task parallel programs 任务并行程序中工作时间膨胀的表征与缓解

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.3233/SPR-130369

Stephen L. Olivier, B. Supinski, M. Schulz, J. Prins

引用次数: 53

A study on data deduplication in HPC storage systems 高性能计算存储系统中重复数据删除技术的研究

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.14

Dirk Meister, J. Kaiser, A. Brinkmann, Toni Cortes, Michael Kuhn, J. Kunkel

{"title":"A study on data deduplication in HPC storage systems","authors":"Dirk Meister, J. Kaiser, A. Brinkmann, Toni Cortes, Michael Kuhn, J. Kunkel","doi":"10.1109/SC.2012.14","DOIUrl":"https://doi.org/10.1109/SC.2012.14","url":null,"abstract":"Deduplication is a storage saving technique that is highly successful in enterprise backup environments. On a file system, a single data block might be stored multiple times across different files, for example, multiple versions of a file might exist that are mostly identical. With deduplication, this data replication is localized and redundancy is removed -- by storing data just once, all files that use identical regions refer to the same unique data. The most common approach splits file data into chunks and calculates a cryptographic fingerprint for each chunk. By checking if the fingerprint has already been stored, a chunk is classified as redundant or unique. Only unique chunks are stored. This paper presents the first study on the potential of data deduplication in HPC centers, which belong to the most demanding storage producers. We have quantitatively assessed this potential for capacity reduction for 4 data centers (BSC, DKRZ, RENCI, RWTH). In contrast to previous deduplication studies focusing mostly on backup data, we have analyzed over one PB (1212 TB) of online file system data. The evaluation shows that typically 20% to 30% of this online data can be removed by applying data deduplication techniques, peaking up to 70% for some data sets. This reduction can only be achieved by a subfile deduplication approach, while approaches based on whole-file comparisons only lead to small capacity savings.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521184","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: 121

Alleviating scalability issues of checkpointing protocols 缓解检查点协议的可伸缩性问题

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.18

R. Riesen, Kurt B. Ferreira, D. D. Silva, Pierre Lemarinier, D. Arnold, P. Bridges

引用次数: 36