2016 IEEE High Performance Extreme Computing Conference (HPEC)最新文献_第7页

Julia implementation of the Dynamic Distributed Dimensional Data Model Julia实现的动态分布式维度数据模型

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-08-14 DOI: 10.1109/HPEC.2016.7761626

Alexander Chen, A. Edelman, J. Kepner, V. Gadepally, D. Hutchison

引用次数: 9

High-throughput ingest of data provenance records into Accumulo 将数据来源记录高吞吐量地摄取到Accumulo中

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-08-12 DOI: 10.1109/HPEC.2016.7761589

Thomas Moyer, V. Gadepally

引用次数: 11

Novel graph processor architecture, prototype system, and results 新的图形处理器架构，原型系统和结果

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-07-22 DOI: 10.1109/HPEC.2016.7761635

William S. Song, Vitaliy Gleyzer, A. Lomakin, J. Kepner

引用次数: 22

Scheduler technologies in support of high performance data analysis 支持高性能数据分析的调度器技术

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-07-22 DOI: 10.1109/HPEC.2016.7761604

A. Reuther, C. Byun, W. Arcand, David Bestor, Bill Bergeron, M. Hubbell, Michael Jones, P. Michaleas, Andrew Prout, Antonio Rosa, J. Kepner

{"title":"Scheduler technologies in support of high performance data analysis","authors":"A. Reuther, C. Byun, W. Arcand, David Bestor, Bill Bergeron, M. Hubbell, Michael Jones, P. Michaleas, Andrew Prout, Antonio Rosa, J. Kepner","doi":"10.1109/HPEC.2016.7761604","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761604","url":null,"abstract":"Job schedulers are a key component of scalable computing infrastructures. They orchestrate all of the work executed on the computing infrastructure and directly impact the effectiveness of the system. Recently, job workloads have diversified from long-running, synchronously-parallel simulations to include short-duration, independently parallel high performance data analysis (HPDA) jobs. Each of these job types requires different features and scheduler tuning to run efficiently. A number of schedulers have been developed to address both job workload and computing system heterogeneity. High performance computing (HPC) schedulers were designed to schedule large-scale scientific modeling and simulations on supercomputers. Big Data schedulers were designed to schedule data processing and analytic jobs on clusters. This paper compares and contrasts the features of HPC and Big Data schedulers with a focus on accommodating both scientific computing and high performance data analytic workloads. Job latency is critical for the efficient utilization of scalable computing infrastructures, and this paper presents the results of job launch benchmarking of several current schedulers: Slurm, Son of Grid Engine, Mesos, and Yarn. We find that all of these schedulers have low utilization for short-running jobs. Furthermore, employing multilevel scheduling significantly improves the utilization across all schedulers.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414625","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: 28

Enhancing HPC security with a user-based firewall 使用基于用户的防火墙增强HPC安全性

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-07-11 DOI: 10.1109/HPEC.2016.7761641

Andrew Prout, W. Arcand, David Bestor, Bill Bergeron, C. Byun, V. Gadepally, M. Hubbell, Michael Houle, Michael Jones, P. Michaleas, Lauren Milechin, J. Mullen, Antonio Rosa, S. Samsi, A. Reuther, J. Kepner

{"title":"Enhancing HPC security with a user-based firewall","authors":"Andrew Prout, W. Arcand, David Bestor, Bill Bergeron, C. Byun, V. Gadepally, M. Hubbell, Michael Houle, Michael Jones, P. Michaleas, Lauren Milechin, J. Mullen, Antonio Rosa, S. Samsi, A. Reuther, J. Kepner","doi":"10.1109/HPEC.2016.7761641","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761641","url":null,"abstract":"High Performance Computing (HPC) systems traditionally allow their users unrestricted use of their internal network. While this network is normally controlled enough to guarantee privacy without the need for encryption, it does not provide a method to authenticate peer connections. Protocols built upon this internal network, such as those used in MPI, Lustre, Hadoop, or Accumulo, must provide their own authentication at the application layer. Many methods have been employed to perform this authentication, such as operating system privileged ports, Kerberos, munge, TLS, and PKI certificates. However, support for all of these methods requires the HPC application developer to include support and the user to configure and enable these services. The user-based firewall capability we have prototyped enables a set of rules governing connections across the HPC internal network to be put into place using Linux netfilter. By using an operating system-level capability, the system is not reliant on any developer or user actions to enable security. The rules we have chosen and implemented are crafted to not impact the vast majority of users and be completely invisible to them. Additionally, we have measured the performance impact of this system under various workloads.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125168046","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

Generating massive complex networks with hyperbolic geometry faster in practice 使用双曲几何更快地生成大规模复杂网络

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-06-30 DOI: 10.1109/HPEC.2016.7761644

M. V. Looz, Mustafa Safa Ozdayi, S. Laue, Henning Meyerhenke

引用次数: 22

From NoSQL Accumulo to NewSQL Graphulo: Design and utility of graph algorithms inside a BigTable database 从NoSQL Accumulo到NewSQL Graphulo: BigTable数据库中图形算法的设计和应用

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-06-22 DOI: 10.1109/HPEC.2016.7761577

D. Hutchison, J. Kepner, V. Gadepally, Bill Howe

引用次数: 18

Scalability of VM provisioning systems 虚拟机发放系统的可扩展性

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-06-18 DOI: 10.1109/HPEC.2016.7761629

Mike Jones, Bill Arcand, Bill Bergeron, David Bestor, C. Byun, Lauren Milechin, V. Gadepally, M. Hubbell, J. Kepner, P. Michaleas, J. Mullen, Andrew Prout, Tony Rosa, S. Samsi, Charles Yee, A. Reuther

{"title":"Scalability of VM provisioning systems","authors":"Mike Jones, Bill Arcand, Bill Bergeron, David Bestor, C. Byun, Lauren Milechin, V. Gadepally, M. Hubbell, J. Kepner, P. Michaleas, J. Mullen, Andrew Prout, Tony Rosa, S. Samsi, Charles Yee, A. Reuther","doi":"10.1109/HPEC.2016.7761629","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761629","url":null,"abstract":"Virtual machines and virtualized hardware have been around for over half a century. The commoditization of the x86 platform and its rapidly growing hardware capabilities have led to recent exponential growth in the use of virtualization both in the enterprise and high performance computing (HPC). The startup time of a virtualized environment is a key performance metric for high performance computing in which the runtime of any individual task is typically much shorter than the lifetime of a virtualized service in an enterprise context. In this paper, a methodology for accurately measuring the startup performance on an HPC system is described. The startup performance overhead of three of the most mature, widely deployed cloud management frameworks (OpenStack, OpenNebula, and Eucalyptus) is measured to determine their suitability for workloads typically seen in an HPC environment. A 10x performance difference is observed between the fastest (Eucalyptus) and the slowest (OpenNebula) framework. This time difference is primarily due to delays in waiting on networking in the cloud-init portion of the startup. The methodology and measurements presented should facilitate the optimization of startup across a variety of virtualization environments.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126358733","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: 17

Mathematical foundations of the GraphBLAS GraphBLAS的数学基础

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-06-18 DOI: 10.1109/HPEC.2016.7761646

J. Kepner, Peter Aaltonen, David A. Bader, A. Buluç, F. Franchetti, J. Gilbert, D. Hutchison, Manoj Kumar, A. Lumsdaine, Henning Meyerhenke, Scott McMillan, Carl Yang, John Douglas Owens, Marcin Zalewski, T. Mattson, J. Moreira

{"title":"Mathematical foundations of the GraphBLAS","authors":"J. Kepner, Peter Aaltonen, David A. Bader, A. Buluç, F. Franchetti, J. Gilbert, D. Hutchison, Manoj Kumar, A. Lumsdaine, Henning Meyerhenke, Scott McMillan, Carl Yang, John Douglas Owens, Marcin Zalewski, T. Mattson, J. Moreira","doi":"10.1109/HPEC.2016.7761646","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761646","url":null,"abstract":"The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest possible audience. Mathematically, the GraphBLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix multiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of a small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128627846","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: 203

Associative array model of SQL, NoSQL, and NewSQL databases SQL、NoSQL和NewSQL数据库的关联数组模型

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-06-18 DOI: 10.1109/HPEC.2016.7761647

J. Kepner, V. Gadepally, D. Hutchison, Hayden Jananthan, T. Mattson, S. Samsi, A. Reuther

{"title":"Associative array model of SQL, NoSQL, and NewSQL databases","authors":"J. Kepner, V. Gadepally, D. Hutchison, Hayden Jananthan, T. Mattson, S. Samsi, A. Reuther","doi":"10.1109/HPEC.2016.7761647","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761647","url":null,"abstract":"The success of SQL, NoSQL, and NewSQL databases is a reflection of their ability to provide significant functionality and performance benefits for specific domains, such as financial transactions, internet search, and data analysis. The BigDAWG polystore seeks to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Associative arrays provide a common approach to the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). This work presents the SQL relational model in terms of associative arrays and identifies the key mathematical properties that are preserved within SQL. These properties include associativity, commutativity, distributivity, identities, annihilators, and inverses. Performance measurements on distributivity and associativity show the impact these properties can have on associative array operations. These results demonstrate that associative arrays could provide a mathematical model for polystores to optimize the exchange of data and execution queries.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121181687","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: 50