2011 IEEE International Conference on Cluster Computing最新文献_第5页

Analyzing the Performance Bottlenecks of the POWER7-IH Network POWER7-IH网络性能瓶颈分析

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.35

D. Kerbyson, K. Barker

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引用次数: 16

Parallel Greedy Genetic Algorithm for Job Scheduling in Cluster Enviornments 集群环境下作业调度的并行贪心遗传算法

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.57

Gholamali Rahnavard, Jharrod Lafon, Hadi Sharifi

引用次数: 3

An ISO-Energy-Efficient Approach to Scalable System Power-Performance Optimization 可扩展系统功率性能优化的iso -能效方法

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.37

S. Song, M. Grove, K. Cameron

引用次数: 19

Design and Implementation of Broadcast Algorithms for Extreme-Scale Systems 极端规模系统广播算法的设计与实现

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.17

Pavel Shamis, R. Graham, Manjunath Gorentla Venkata, Joshua Ladd

引用次数: 4

An Energy-Efficient Scheme for Cloud Resource Provisioning Based on CloudSim

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.63

Yuxiang Shi, Xiaohong Jiang, Kejiang Ye

引用次数: 84

Optimizing Network I/O Virtualization with Efficient Interrupt Coalescing and Virtual Receive Side Scaling 利用高效中断合并和虚拟接收端扩展优化网络I/O虚拟化

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.12

Yaozu Dong, Dongxiao Xu, Yang Zhang, Guangdeng Liao

引用次数: 38

Multiphase LBM Distributed over Multiple GPUs 多相LBM分布在多个gpu上

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.9

C. Rosales

引用次数: 15

TDP-Shell: A Generic Framework to Improve Interoperability between Batch Queue Systems and Monitoring Tools TDP-Shell:改进批处理队列系统和监控工具之间互操作性的通用框架

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.73

Vicente Ivars, M. A. Senar, E. Heymann

引用次数: 0

Model-Driven Simulation to Evaluate Performance Impact of Workload Features on Parallel Systems 模型驱动仿真评估工作负载特征对并行系统性能的影响

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.27

T. Minh, L. Wolters

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引用次数: 2

PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets PIDX:多分辨率多维科学数据集的高效并行I/O

2011 IEEE International Conference on Cluster Computing Pub Date : 2011-09-26 DOI: 10.1109/CLUSTER.2011.19

Sidharth Kumar, V. Vishwanath, P. Carns, B. Summa, G. Scorzelli, Valerio Pascucci, R. Ross, Jacqueline H. Chen, H. Kolla, R. Grout

{"title":"PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets","authors":"Sidharth Kumar, V. Vishwanath, P. Carns, B. Summa, G. Scorzelli, Valerio Pascucci, R. Ross, Jacqueline H. Chen, H. Kolla, R. Grout","doi":"10.1109/CLUSTER.2011.19","DOIUrl":"https://doi.org/10.1109/CLUSTER.2011.19","url":null,"abstract":"The IDX data format provides efficient, cache oblivious, and progressive access to large-scale scientific datasets by storing the data in a hierarchical Z (HZ) order. Data stored in IDX format can be visualized in an interactive environment allowing for meaningful explorations with minimal resources. This technology enables real-time, interactive visualization and analysis of large datasets on a variety of systems ranging from desktops and laptop computers to portable devices such as iPhones/iPads and over the web. While the existing ViSUS API for writing IDX data is serial, there are obvious advantages of applying the IDX format to the output of large scale scientific simulations. We have therefore developed PIDX - a parallel API for writing data in an IDX format. With PIDX it is now possible to generate IDX datasets directly from large scale scientific simulations with the added advantage of real-time monitoring and visualization of the generated data. In this paper, we provide an overview of the IDX file format and how it is generated using PIDX. We then present a data model description and a novel aggregation strategy to enhance the scalability of the PIDX library. The S3D combustion application is used as an example to demonstrate the efficacy of PIDX for a real-world scientific simulation. S3D is used for fundamental studies of turbulent combustion requiring exceptionally high fidelity simulations. PIDX achieves up to 18 GiB/s I/O throughput at 8,192 processes for S3D to write data out in the IDX format. This allows for interactive analysis and visualization of S3D data, thus, enabling in situ analysis of S3D simulation.","PeriodicalId":200830,"journal":{"name":"2011 IEEE International Conference on Cluster Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134061859","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: 34