2012 International Conference for High Performance Computing, Networking, Storage and Analysis最新文献_第7页

First-ever full observable universe simulation 首次完整的可观测宇宙模拟

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.58

J. Alimi, V. Bouillot, Y. Rasera, V. Reverdy, Pier Stefano Corasaniti, Irène Balmès, S. Requena, Xavier Delaruelle, Jean-Noel Richet

{"title":"First-ever full observable universe simulation","authors":"J. Alimi, V. Bouillot, Y. Rasera, V. Reverdy, Pier Stefano Corasaniti, Irène Balmès, S. Requena, Xavier Delaruelle, Jean-Noel Richet","doi":"10.1109/SC.2012.58","DOIUrl":"https://doi.org/10.1109/SC.2012.58","url":null,"abstract":"We have performed the first-ever numerical N-body simulation of the full observable universe (DEUS \"Dark Energy Universe Simulation\" FUR \"Full Universe Run\"). This has evolved 550 billion particles on an Adaptive Mesh Refinement grid with more than two trillion computing points along the entire evolutionary history of the universe and across 6 order of magnitudes length scales, from the size of the Milky Way to that of the whole observable Universe. To date, this is the largest and most advanced cosmological simulation ever run. It provides unique information on the formation and evolution of the largest structure in the universe and an exceptional support to future observational programs dedicated to mapping the distribution of matter and galaxies in the universe. The simulation has run on 4752 (of 5040) thin nodes of BULL supercomputer CURIE, using more than 300 TB of memory for 10 million hours of computing time. About 50 PBytes of data were generated throughout the run. Using an advanced and innovative reduction workflow the amount of useful stored data has been reduced to 500 TBytes.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"20 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":"121988797","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: 14

Automatic generation of software pipelines for heterogeneous parallel systems 异构并行系统软件管道的自动生成

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.22

J. Pienaar, S. Chakradhar, A. Raghunathan

引用次数: 23

Demonstrating Lustre over a 100Gbps wide area network of 3,500km 在3500公里的100Gbps广域网上展示光彩

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.43

R. Henschel, Stephen C. Simms, David Y. Hancock, Scott Michael, Tom Johnson, Nathan Heald, T. William, Donald K. Berry, Matthew Allen, R. Knepper, M. Davy, Matthew R. Link, C. Stewart

引用次数: 19

Compiler-directed file layout optimization for hierarchical storage systems 分级存储系统的编译器导向文件布局优化

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.3233/SPR-130365

W. Ding, Yuanrui Zhang, M. Kandemir, S. Son

引用次数: 1

On distributed file tree walk of parallel file systems 并行文件系统的分布式文件树遍历

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.82

Jharrod Lafon, S. Misra, John R. Bringhurst

引用次数: 12

NUMA-aware graph mining techniques for performance and energy efficiency 基于numa的图形挖掘技术提高性能和能源效率

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.5555/2388996.2389125

Michael R. Frasca, Kamesh Madduri, P. Raghavan

引用次数: 41

High performance radiation transport simulations: Preparing for TITAN 高性能辐射输运模拟:为TITAN做准备

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.64

C. Baker, G. Davidson, T. Evans, S. Hamilton, J. Jarrell, W. Joubert

{"title":"High performance radiation transport simulations: Preparing for TITAN","authors":"C. Baker, G. Davidson, T. Evans, S. Hamilton, J. Jarrell, W. Joubert","doi":"10.1109/SC.2012.64","DOIUrl":"https://doi.org/10.1109/SC.2012.64","url":null,"abstract":"In this paper we describe the Denovo code system. Denovo solves the six-dimensional, steady-state, linear Boltzmann transport equation, of central importance to nuclear technology applications such as reactor core analysis (neutronics), radiation shielding, nuclear forensics and radiation detection. The code features multiple spatial differencing schemes, state-of-the-art linear solvers, the Koch-Baker-Alcouffe (KBA) parallel-wavefront sweep algorithm for inverting the transport operator, a new multilevel energy decomposition method scaling to hundreds of thousands of processing cores, and a modern, novel code architecture that supports straightforward integration of new features. In this paper we discuss the performance of Denovo on the 20+ petaflop ORNL GPU-based system, Titan. We describe algorithms and techniques used to exploit the capabilities of Titan's heterogeneous compute node architecture and the challenges of obtaining good parallel performance for this sparse hyperbolic PDE solver containing inherently sequential computations. Numerical results demonstrating Denovo performance on early Titan hardware are presented.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"18 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":"122182482","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: 26

Critical lock analysis: Diagnosing critical section bottlenecks in multithreaded applications 临界锁分析:诊断多线程应用程序中的临界段瓶颈

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.40

Guancheng Chen, P. Stenström

引用次数: 30

Efficient backprojection-based synthetic aperture radar computation with many-core processors 基于多核处理器的高效反投影合成孔径雷达计算

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.3233/SPR-130372

Jongsoo Park, P. T. P. Tang, M. Smelyanskiy, Daehyun Kim, T. Benson

{"title":"Efficient backprojection-based synthetic aperture radar computation with many-core processors","authors":"Jongsoo Park, P. T. P. Tang, M. Smelyanskiy, Daehyun Kim, T. Benson","doi":"10.3233/SPR-130372","DOIUrl":"https://doi.org/10.3233/SPR-130372","url":null,"abstract":"Tackling computationally challenging problems with high efficiency often requires the combination of algorithmic innovation, advanced architecture, and thorough exploitation of parallelism. We demonstrate this synergy through synthetic aperture radar (SAR) via backprojection, an image reconstruction method that can require hundreds of TFLOPS. Computation cost is significantly reduced by our new algorithm of approximate strength reduction; data movement cost is economized by software locality optimizations facilitated by advanced architecture support; parallelism is fully harnessed in various patterns and granularities. We deliver over 35 billion backprojections per second throughput per compute node on an Intel® Xeon® processor E5-2670-based cluster, equipped with Intel® Xeon Phi coprocessors. This corresponds to processing a 3K×3K image within a second using a single node. Our study can be extended to other settings: backprojection is applicable elsewhere including medical imaging, approximate strength reduction is a general code transformation technique, and many-core processors are emerging as a solution to energy-efficient computing.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"119 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120894129","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

Dataflow-driven GPU performance projection for multi-kernel transformations 多核转换的数据流驱动GPU性能投影

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.42

Jiayuan Meng, V. Morozov, V. Vishwanath, Kalyan Kumaran

{"title":"Dataflow-driven GPU performance projection for multi-kernel transformations","authors":"Jiayuan Meng, V. Morozov, V. Vishwanath, Kalyan Kumaran","doi":"10.1109/SC.2012.42","DOIUrl":"https://doi.org/10.1109/SC.2012.42","url":null,"abstract":"Applications often have a sequence of parallel operations to be offloaded to graphics processors; each operation can become an individual GPU kernel. Developers typically explore a variety of transformations for each kernel. Furthermore, it is well known that efficient data management is critical in achieving high GPU performance and that \"fusing\" multiple kernels into one may greatly improve data locality. Doing so, however, requires transformations across multiple, potentially nested, parallel loops; at the same time, the original code semantics and data dependency must be preserved. Since each kernel may have distinct data access patterns, their combined dataflow can be nontrivial. As a result, the complexity of multi-kernel transformations often leads to significant effort with no guarantee of performance benefits. This paper proposes a dataflow-driven analytical framework to project GPU performance for a sequence of parallel operations. Users need only provide CPU code skeletons for a sequence of parallel loops. The framework can then automatically identify opportunities for multi-kernel transformations and data management. It is also able to project the overall performance without implementing GPU code or using physical hardware.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"14 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":"128030787","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