2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing最新文献_第2页

Big Data Provenance Analysis and Visualization 大数据来源分析与可视化

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.85

Peng Chen, Beth Plale

引用次数: 15

Towards Context-Aware Mobile Crowdsensing in Vehicular Social Networks 面向车载社交网络的情境感知移动人群感知

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.155

Xiping Hu, Victor C. M. Leung

引用次数: 12

Revisiting ILP Designs for Throughput-Oriented GPGPU Architecture 面向吞吐量的GPGPU架构的ILP设计重述

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGRID.2015.14

Ping Xiang, Yi Yang, Mike Mantor, Norman Rubin, Huiyang Zhou

引用次数: 4

Lessons Learned Implementing User-Level Failure Mitigation in MPICH 在MPICH中实施用户级故障缓解的经验教训

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.51

Wesley Bland, Huiwei Lu, Sangmin Seo, P. Balaji

引用次数: 14

Full Integrity and Freshness for Outsourced Storage 外包储存的完整性和新鲜度

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.90

Hao Jin, Hong Jiang, Ke Zhou, Ronglei Wei, Dongliang Lei, Ping Huang

引用次数: 0

Energy Profiling Using IgProf 使用IgProf进行能量分析

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.118

Kashif Nizam Khan, Filip Nyback, Zhonghong Ou, J. Nurminen, T. Niemi, G. Eulisse, P. Elmer, David Abdurachmanov

引用次数: 11

Modeling Gather and Scatter with Hardware Performance Counters for Xeon Phi 建模收集和分散与硬件性能计数器为Xeon Phi

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.59

James Lin, Akira Nukada, S. Matsuoka

引用次数: 1

SparkSW: Scalable Distributed Computing System for Large-Scale Biological Sequence Alignment SparkSW:大规模生物序列比对可扩展分布式计算系统

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.55

Guoguang Zhao, Cheng Ling, Donghong Sun

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引用次数: 36

Modeling Cross-Architecture Co-Tenancy Performance Interference 跨架构共租性能干扰建模

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.152

Wei Kuang, Laura E. Brown, Zhenlin Wang

{"title":"Modeling Cross-Architecture Co-Tenancy Performance Interference","authors":"Wei Kuang, Laura E. Brown, Zhenlin Wang","doi":"10.1109/CCGrid.2015.152","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.152","url":null,"abstract":"Cloud computing has become a dominant computing paradigm to provide elastic, affordable computing resources to end users. Due to the increased computing power of modern machines powered by multi/many-core computing, data centers often co-locate multiple virtual machines (VMs) into one physical machine, resulting in co-tenancy, and resource sharing and competition. Applications or VMs co-locating in one physical machine can interfere with each other despite of the promise of performance isolation through virtualization. Modelling and predicting co-run interference therefore becomes critical for data center job scheduling and QoS (Quality of Service) assurance. Co-run interference can be categorized into two metrics, sensitivity and pressure, where the former denotes how an application's performance is affected by its co-run applications, and the latter measures how it impacts the performance of its co-run applications. This paper shows that sensitivity and pressure are both application-and architecture dependent. Further, we propose a regression model that predicts an application's sensitivity and pressure across architectures with high accuracy. This regression model enables a data center scheduler to guarantee the QoS of a VM/application when it is scheduled to co-locate with another VMs/applications.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"194 1","pages":"231-240"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85473011","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

The Challenge of Scaling Genome Big Data Analysis Software on TH-2 Supercomputer 基因组大数据分析软件在TH-2超级计算机上的扩展挑战

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2015-05-04 DOI: 10.1109/CCGrid.2015.46

Shaoliang Peng, Xiangke Liao, Canqun Yang, Yutong Lu, Jie Liu, Yingbo Cui, Heng Wang, Chengkun Wu, Bingqiang Wang

{"title":"The Challenge of Scaling Genome Big Data Analysis Software on TH-2 Supercomputer","authors":"Shaoliang Peng, Xiangke Liao, Canqun Yang, Yutong Lu, Jie Liu, Yingbo Cui, Heng Wang, Chengkun Wu, Bingqiang Wang","doi":"10.1109/CCGrid.2015.46","DOIUrl":"https://doi.org/10.1109/CCGrid.2015.46","url":null,"abstract":"Whole genome re-sequencing plays a crucial role in biomedical studies. The emergence of genomic big data calls for an enormous amount of computing power. However, current computational methods are inefficient in utilizing available computational resources. In this paper, we address this challenge by optimizing the utilization of the fastest supercomputer in the world - TH-2 supercomputer. TH-2 is featured by its neo-heterogeneous architecture, in which each compute node is equipped with 2 Intel Xeon CPUs and 3 Intel Xeon Phi coprocessors. The heterogeneity and the massive amount of data to be processed pose great challenges for the deployment of the genome analysis software pipeline on TH-2. Runtime profiling shows that SOAP3-dp and SOAPsnp are the most time-consuming components (up to 70% of total runtime) in a typical genome-analyzing pipeline. To optimize the whole pipeline, we first devise a number of parallel and optimization strategies for SOAP3-dp and SOAPsnp, respectively targeting each node to fully utilize all sorts of hardware resources provided both by CPU and MIC. We also employ a few scaling methods to reduce communication between different nodes. We then scaled up our method on TH-2. With 8192 nodes, the whole analyzing procedure took 8.37 hours to finish the analysis of a 300 TB dataset of whole genome sequences from 2,000 human beings, which can take as long as 8 months on a commodity server. The speedup is about 700x.","PeriodicalId":6664,"journal":{"name":"2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"8 1","pages":"823-828"},"PeriodicalIF":0.0,"publicationDate":"2015-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78334165","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: 0