2017 IEEE High Performance Extreme Computing Conference (HPEC)最新文献_第2页

Database engine integration and performance analysis of the BigDAWG polystore system BigDAWG多存储系统的数据库引擎集成与性能分析

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091081

Katherine Yu, V. Gadepally, M. Stonebraker

引用次数: 4

Efficient parallel streaming algorithms for large-scale inverse problems 大规模反问题的高效并行流算法

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091033

H. Sundar

引用次数: 0

An introduction to an array memory processor for application specific acceleration 介绍用于特定应用程序加速的阵列存储器处理器

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091069

G. Pechanek, N. Pitsianis

引用次数: 1

Software-defined extreme scale networks for bigdata applications 面向大数据应用的软件定义极端规模网络

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091087

Haitham Ghalwash, Chun-Hsi Huang

{"title":"Software-defined extreme scale networks for bigdata applications","authors":"Haitham Ghalwash, Chun-Hsi Huang","doi":"10.1109/HPEC.2017.8091087","DOIUrl":"https://doi.org/10.1109/HPEC.2017.8091087","url":null,"abstract":"Software-Defined Networking (SDN) is an emerging technology that supports recent network applications. An SDN redefines networks by introducing the concept of decoupling the control plane from the data plane, thus providing centralized management, programmability, and dynamic reconfiguration. In this research, we specifically investigate the significance of using SDNs in support of Big-Data applications. SDN proved to support Big-Data applications through a more efficient use of distributed nodes. With Hadoop as an example of Big-Data application, we investigate the performance in terms of throughput and execution time for the read/write and sorting operations. The experiments take into consideration different network sizes of a Fat-tree topology. A Hadoop multi-node cluster is installed in Docker containers connected through a Fat-tree of OpenFlow switches. The packet forwarding is either by way of an SDN controller or the normal packet switching rules. Experimental results show that using an SDN controller outperforms normal forwarding by the switches. As a result, our research suggests that using SDN controllers has a strong potential to greatly enhance the performance of Big-Data applications on extreme-scale networks.","PeriodicalId":364903,"journal":{"name":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"5 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113955708","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

Scalable stochastic block partition 可伸缩随机块划分

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091050

Ahsen J. Uppal, Guy Swope, H. H. Huang, The George Washington

{"title":"Scalable stochastic block partition","authors":"Ahsen J. Uppal, Guy Swope, H. H. Huang, The George Washington","doi":"10.1109/HPEC.2017.8091050","DOIUrl":"https://doi.org/10.1109/HPEC.2017.8091050","url":null,"abstract":"The processing of graph data at large scale, though important and useful for real-world applications, continues to be challenging, particularly for problems such as graph partitioning. Optimal graph partitioning is NP-hard, but several methods provide approximate solutions in reasonable time. Yet scaling these approximate algorithms is also challenging. In this paper, we describe our efforts towards improving the scalability of one such technique, stochastic block partition, which is the baseline algorithm for the IEEE HPEC Graph Challenge [1]. Our key contributions are: improvements to the parallelization of the baseline bottom-up algorithm, especially the Markov Chain Monte Carlo (MCMC) nodal updates for Bayesian inference; a new top-down divide and conquer algorithm capable of reducing the algorithmic complexity of static partitioning and also suitable for streaming partitioning; a parallel single-node multi-CPU implementation and a parallel multi-node MPI implementation. Although our focus is on algorithmic scalability, our Python implementation obtains a speedup of 1.65× over the fastest baseline parallel C++ run at a graph size of 100k vertices divided into 8 subgraphs on a multi-CPU single node machine. It achieves a speedup of 61× over itself on a cluster of 4 machines with 256 CPUs for a 20k node graph divided into 4 subgraphs, and 441× speedup over itself on a 50k node graph divided into 8 subgraphs on a multi-CPU single node machine.","PeriodicalId":364903,"journal":{"name":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127653851","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: 12

A quantitative and qualitative analysis of tensor decompositions on spatiotemporal data 时空数据张量分解的定量和定性分析

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091028

Thomas Henretty, M. Baskaran, J. Ezick, David Bruns-Smith, T. Simon

引用次数: 12

Distributed-memory fast maximal independent set 分布式内存快速最大独立集

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091032

Thejaka Amila Kanewala, Marcin Zalewski, A. Lumsdaine

引用次数: 1

xDCI, a data science cyberinfrastructure for interdisciplinary research xDCI，用于跨学科研究的数据科学网络基础设施

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091022

A. Krishnamurthy, K. Bradford, Chris Calloway, C. Castillo, Mike C. Conway, Jason Coposky, Yue Guo, R. Idaszak, W. Lenhardt, K. Robasky, Terrell G. Russell, Erik Scott, Marcin Sliwowski, M. Stealey, Kelsey Urgo, Hao Xu, H. Yi, S. Ahalt

引用次数: 2

Memory-efficient parallel tensor decompositions 内存高效并行张量分解

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091026

M. Baskaran, Thomas Henretty, B. Pradelle, M. H. Langston, David Bruns-Smith, J. Ezick, R. Lethin

{"title":"Memory-efficient parallel tensor decompositions","authors":"M. Baskaran, Thomas Henretty, B. Pradelle, M. H. Langston, David Bruns-Smith, J. Ezick, R. Lethin","doi":"10.1109/HPEC.2017.8091026","DOIUrl":"https://doi.org/10.1109/HPEC.2017.8091026","url":null,"abstract":"Tensor decompositions are a powerful technique for enabling comprehensive and complete analysis of real-world data. Data analysis through tensor decompositions involves intensive computations over large-scale irregular sparse data. Optimizing the execution of such data intensive computations is key to reducing the time-to-solution (or response time) in real-world data analysis applications. As high-performance computing (HPC) systems are increasingly used for data analysis applications, it is becoming increasingly important to optimize sparse tensor computations and execute them efficiently on modern and advanced HPC systems. In addition to utilizing the large processing capability of HPC systems, it is crucial to improve memory performance (memory usage, communication, synchronization, memory reuse, and data locality) in HPC systems. In this paper, we present multiple optimizations that are targeted towards faster and memory-efficient execution of large-scale tensor analysis on HPC systems. We demonstrate that our techniques achieve reduction in memory usage and execution time of tensor decomposition methods when they are applied on multiple datasets of varied size and structure from different application domains. We achieve up to 11× reduction in memory usage and up to 7× improvement in performance. More importantly, we enable the application of large tensor decompositions on some important datasets on a multi-core system that would not have been feasible without our optimization.","PeriodicalId":364903,"journal":{"name":"2017 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126164376","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: 22

A distributed algorithm for the efficient computation of the unified model of social influence on massive datasets 一种高效计算海量数据集社会影响统一模型的分布式算法

2017 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2017-09-01 DOI: 10.1109/HPEC.2017.8091084

Alex Popa, M. Frîncu, C. Chelmis

引用次数: 2