2012 International Conference for High Performance Computing, Networking, Storage and Analysis最新文献_第3页

Billion-particle SIMD-friendly two-point correlation on large-scale HPC cluster systems 大规模HPC集群系统的十亿粒子simd友好两点关联

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.24

J. Chhugani, Changkyu Kim, H. Shukla, Jongsoo Park, P. Dubey, J. Shalf, H. Simon

{"title":"Billion-particle SIMD-friendly two-point correlation on large-scale HPC cluster systems","authors":"J. Chhugani, Changkyu Kim, H. Shukla, Jongsoo Park, P. Dubey, J. Shalf, H. Simon","doi":"10.1109/SC.2012.24","DOIUrl":"https://doi.org/10.1109/SC.2012.24","url":null,"abstract":"Two-point Correlation Function (TPCF) is widely used in astronomy to characterize the distribution of matter/energy in the Universe, and help derive the physics that can trace back to the creation of the universe. However, it is prohibitively slow for current sized datasets, and would continue to be a critical bottleneck with the trend of increasing dataset sizes to billions of particles and more, which makes TPCF a compelling benchmark application for future exa-scale architectures. State-of-the-art TPCF implementations do not map well to the underlying SIMD hardware, and also suffer from load-imbalance for large core counts. In this paper, we present a novel SIMD-friendly histogram update algorithm that exploits the spatial locality of histogram updates to achieve near-linear SIMD scaling. We also present a load-balancing scheme that combines domain-specific initial static division of work and dynamic task migration across nodes to effectively balance computation across nodes. Using Zin supercomputer at Lawrence Livermore National Laboratory (25,600 cores of Intel® Xeon® E5-2670, each with 256-bit SIMD), we achieve 90% parallel efficiency and 96% SIMD efficiency, and perform TPCF computation on a 1.7 billion particle dataset in 5.3 hours (at least 35× faster than previous approaches). In terms of cost per performance (measured in flops/$), we achieve at least an order-of-magnitude (11.1x) higher flops/$ as compared to the best known results [1]. Consequently, we now have line-of-sight to achieving the processing power for correlation computation to process billion+ particles telescopic data.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"21 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":"134130637","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

Parallel particle advection and FTLE computation for time-varying flow fields 时变流场平行粒子平流与FTLE计算

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.93

B. Nouanesengsy, Teng-Yok Lee, Kewei Lu, Han-Wei Shen, T. Peterka

引用次数: 40

High performance RDMA-based design of HDFS over InfiniBand HDFS基于InfiniBand的高性能rdma设计

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.65

Nusrat S. Islam, Md. Wasi-ur-Rahman, Jithin Jose, R. Rajachandrasekar, Hao Wang, H. Subramoni, Chet Murthy, D. Panda

引用次数: 180

On the effectiveness of application-aware self-management for scientific discovery in volunteer computing systems 志愿计算系统中科学发现应用感知自我管理的有效性研究

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.83

Trilce Estrada, M. Taufer

{"title":"On the effectiveness of application-aware self-management for scientific discovery in volunteer computing systems","authors":"Trilce Estrada, M. Taufer","doi":"10.1109/SC.2012.83","DOIUrl":"https://doi.org/10.1109/SC.2012.83","url":null,"abstract":"An important challenge faced by high-throughput, multiscale applications is that human intervention has a central role in driving their success. However, manual intervention is inefficient, error-prone and promotes resource wasting. This paper presents an application-aware modular framework that provides self-management for computational multiscale applications in volunteer computing (VC). Our framework consists of a learning engine and three modules that can be easily adapted to different distributed systems. The learning engine of this framework is based on our novel tree-like structure called KOTree. KOTree is a fully automatic method that organizes statistical information in a multidimensional structure that can be efficiently searched and updated at runtime. Our empirical evaluation shows that our framework can effectively provide application-aware self-management in VC systems. Additionally, we observed that our KOTree algorithm is able to predict accurately the expected length of new jobs, resulting in an average of 85% increased throughput with respect to other algorithms.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"48 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":"124717122","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: 6

Host load prediction in a Google compute cloud with a Bayesian model 使用贝叶斯模型预测Google计算云中的主机负载

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.68

S. Di, Derrick Kondo, W. Cirne

引用次数: 159

A divide and conquer strategy for scaling weather simulations with multiple regions of interest 一种分而治之的策略，用于扩展多个感兴趣区域的天气模拟

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.3233/SPR-130367

Preeti Malakar, Thomas George, Sameer Kumar, R. Mittal, V. Natarajan, Yogish Sabharwal, Vaibhav Saxena, Sathish S. Vadhiyar

引用次数: 13

Parallel I/O, analysis, and visualization of a trillion particle simulation 并行I/O，分析和可视化一万亿粒子模拟

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.92

S. Byna, J. Chou, O. Rübel, Prabhat, H. Karimabadi, W. Daughton, V. Roytershteyn, E. W. Bethel, Mark Howison, K. Hsu, Kuan-Wu Lin, A. Shoshani, A. Uselton, Kesheng Wu

{"title":"Parallel I/O, analysis, and visualization of a trillion particle simulation","authors":"S. Byna, J. Chou, O. Rübel, Prabhat, H. Karimabadi, W. Daughton, V. Roytershteyn, E. W. Bethel, Mark Howison, K. Hsu, Kuan-Wu Lin, A. Shoshani, A. Uselton, Kesheng Wu","doi":"10.1109/SC.2012.92","DOIUrl":"https://doi.org/10.1109/SC.2012.92","url":null,"abstract":"Petascale plasma physics simulations have recently entered the regime of simulating trillions of particles. These unprecedented simulations generate massive amounts of data, posing significant challenges in storage, analysis, and visualization. In this paper, we present parallel I/O, analysis, and visualization results from a VPIC trillion particle simulation running on 120,000 cores, which produces ~30TB of data for a single timestep. We demonstrate the successful application of H5Part, a particle data extension of parallel HDF5, for writing the dataset at a significant fraction of system peak I/O rates. To enable efficient analysis, we develop hybrid parallel FastQuery to index and query data using multi-core CPUs on distributed memory hardware. We show good scalability results for the FastQuery implementation using up to 10,000 cores. Finally, we apply this indexing/query-driven approach to facilitate the first-ever analysis and visualization of the trillion particle dataset.","PeriodicalId":217221,"journal":{"name":"2012 International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"130 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":"130420485","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: 83

Containment domains: A scalable, efficient, and flexible resilience scheme for exascale systems 包含域:用于百亿亿级系统的可伸缩、高效和灵活的弹性方案

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.3233/SPR-130374

Jinsuk Chung, Ikhwan Lee, Michael B. Sullivan, Jee Ho Ryoo, Dong-Wan Kim, D. Yoon, L. Kaplan, M. Erez

引用次数: 96

A framework for low-communication 1-D FFT 低通信1-D FFT框架

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.3233/SPR-130373

P. T. P. Tang, Jongsoo Park, Daehyun Kim, V. Petrov

引用次数: 20

Scalia: An adaptive scheme for efficient multi-cloud storage Scalia:高效多云存储的自适应方案

2012 International Conference for High Performance Computing, Networking, Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.2012.101

Thanasis G. Papaioannou, N. Bonvin, K. Aberer

引用次数: 89