2008 IEEE International Conference on Cluster Computing最新文献

Empirical-based probabilistic upper bounds for urgent computing applications 紧急计算应用的基于经验的概率上界

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663793

N. Trebon, P. Beckman

{"title":"Empirical-based probabilistic upper bounds for urgent computing applications","authors":"N. Trebon, P. Beckman","doi":"10.1109/CLUSTR.2008.4663793","DOIUrl":"https://doi.org/10.1109/CLUSTR.2008.4663793","url":null,"abstract":"Scientific simulation and modeling often aid in making critical decisions in such diverse fields as city planning, severe weather prediction and influenza modeling. In some of these situations the computations operate under strict deadlines, after which point the results may have very little value. In these cases of urgent computing, it is imperative that these computations begin execution as quickly as possible. The special priority and urgent compute environment (SPRUCE) is a framework designed to enable these high priority computations to quickly access computational grid resources through elevated batch queue priority. However, participating resources are allowed to decide locally how to respond to urgent requests. For instance, some may offer next-to-run status while others may preempt currently executing jobs to clear off the necessary nodes. However, the user is still faced with the problem of resource selection - namely, which resource (and corresponding urgent computing policy) provides the best probability of meeting a given deadline? This paper introduces a set of methodologies and heuristics aimed at generating an empirical-based probabilistic upper bound on the total turnaround time for an urgent computation. These upper bounds can then be used to guide the user in selecting a resource with greater confidence that their deadline will be met.","PeriodicalId":198768,"journal":{"name":"2008 IEEE International Conference on Cluster Computing","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127252238","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: 4

Scalable MPI design over InfiniBand using eXtended Reliable Connection 可扩展的MPI设计在InfiniBand上使用扩展的可靠连接

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663773

Matthew J. Koop, J. K. Sridhar, D. Panda

{"title":"Scalable MPI design over InfiniBand using eXtended Reliable Connection","authors":"Matthew J. Koop, J. K. Sridhar, D. Panda","doi":"10.1109/CLUSTR.2008.4663773","DOIUrl":"https://doi.org/10.1109/CLUSTR.2008.4663773","url":null,"abstract":"A significant component of a high-performance cluster is the compute node interconnect. InfiniBand, is an interconnect of such systems that is enjoying wide success due to low latency (1.0-3.0 musec) and high bandwidth and other features. The Message Passing Interface (MPI) is the dominant programming model for parallel scientific applications. As a result, the MPI library and interconnect play a significant role in the scalability. These clusters continue to scale to ever-increasing levels making the role very important. As an example, the ldquoRangerrdquo system at the Texas Advanced Computing Center (TACC) includes over 60,000 cores with nearly 4000 InfiniBand ports. Previous work has shown that memory usage simply for connections when using the Reliable Connection (RC) transport of InfiniBand can reach hundreds of megabytes of memory per process at that level. To address these scalability problems a new InfiniBand transport, eXtended Reliable Connection, has been introduced. In this paper we describe XRC and design MPI over this new transport. We describe the variety of design choices that must be made as well as the various optimizations that XRC allows. We implement our designs and evaluate it on an InfiniBand cluster against RC-based designs. The memory scalability in terms of both connection memory and memory efficiency for communication buffers is evaluated for all of the configurations. Connection memory scalability evaluation shows a potential 100 times improvement over a similarly configured RC-based design. Evaluation using NAMD shows a 10% performance improvement for our XRC-based prototype for the jac2000 benchmark.","PeriodicalId":198768,"journal":{"name":"2008 IEEE International Conference on Cluster Computing","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114888009","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: 36

A dynamic programming approach to optimizing the blocking strategy for the Householder QR decomposition 一种动态规划方法优化Householder QR分解的阻塞策略

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663801

Takeshi Fukaya, Yusaku Yamamoto, Shaoliang Zhang

{"title":"A dynamic programming approach to optimizing the blocking strategy for the Householder QR decomposition","authors":"Takeshi Fukaya, Yusaku Yamamoto, Shaoliang Zhang","doi":"10.1109/CLUSTR.2008.4663801","DOIUrl":"https://doi.org/10.1109/CLUSTR.2008.4663801","url":null,"abstract":"In this paper, we present a new approach to optimizing the blocking strategy for the householder QR decomposition. In high performance implementations of the householder QR algorithm, it is common to use a blocking technique for the efficient use of the cache memory. There are several well known blocking strategies like the fixed-size blocking and recursive blocking, and usually their parameters such as the block size and the recursion level are tuned according to the target machine and the problem size. However, strategies generated with this kind of parameter optimization constitute only a small fraction of all possible blocking strategies. Given the complex performance characteristics of modern microprocessors, non-standard strategies may prove effective on some machines. Considering this situation, we first propose a new universal model that can express a far larger class of blocking strategies than has been considered so far. Next, we give an algorithm to find a near-optimal strategy from this class using dynamic programming. As a result of this approach, we found an effective blocking strategy that has never been reported. Performance evaluation on the Opteron and Core2 processors show that our strategy achieves about 1.2 times speedup over recursive blocking when computing the QR decomposition of a 6000 times 6000 matrix.","PeriodicalId":198768,"journal":{"name":"2008 IEEE International Conference on Cluster Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122509338","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

RI2N: High-bandwidth and fault-tolerant network with multi-link Ethernet for PC clusters RI2N:用于PC集群的高带宽、多链路以太网容错网络

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663781

Shin'ichi Miura, Takayuki Okamoto, T. Boku, T. Hanawa, M. Sato

{"title":"RI2N: High-bandwidth and fault-tolerant network with multi-link Ethernet for PC clusters","authors":"Shin'ichi Miura, Takayuki Okamoto, T. Boku, T. Hanawa, M. Sato","doi":"10.1109/CLUSTR.2008.4663781","DOIUrl":"https://doi.org/10.1109/CLUSTR.2008.4663781","url":null,"abstract":"Although recent high-end interconnection network devices and switches provide a high performance/cost ratio, most of the small to medium sized PC clusters are still built on the commodity network, Ethernet. To enhance performance on commonly used gigabit Ethernet networks, link aggregation or binding technology is used. Currently, a Linux kernel is equipped with a software solution named linux channel bonding (LCB), which is based on IEEE802.3ad Link Aggregation technology. However, standard LCB has the problem of mismatching with the commonly used TCP protocol, which consequently implies several problems of both large latency and instability on bandwidth improvement. The fault-tolerant feature is also supported, but the usability is not sufficient. We have developed a new implementation similar to LCB named RI2N/DRV (redundant interconnection with inexpensive network with driver) for use on a gigabit Ethernet with a complete software stack that is very compatible with the TCP protocol. Our algorithm suppresses unnecessary ACK packets and retransmission of packets even in imbalanced network traffic and link failures on multiple links. It provides both high-bandwidth and fault-tolerant communication on multi-link gigabit Ethernet. We confirmed that this system improves the performance and reliability of the network, and our system can be applied to ordinary UNIX services such as NFS, without any modification of other modules.","PeriodicalId":198768,"journal":{"name":"2008 IEEE International Conference on Cluster Computing","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123985606","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: 5

OpenMP-centric performance analysis of hybrid applications 以openmp为中心的混合应用程序性能分析

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663767

K. Fürlinger, S. Moore

引用次数: 3

Workflows for performance evaluation and tuning 用于性能评估和调优的工作流

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663758

J. Tilson, Mark S. C. Reed, R. Fowler

引用次数: 4

Gather-arrange-scatter: Node-level request reordering for parallel file systems on multi-core clusters 收集-安排-分散:多核集群上并行文件系统的节点级请求重新排序

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663792

Kazuki Ohta, Hiroya Matsuba, Y. Ishikawa

引用次数: 3

An optimized Dynamic Load Balancing method for parallel 3-D mesh refinement for finite element electromagnetics with Tetrahedra 四面体有限元电磁学并行三维网格细化的优化动态负载平衡方法

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663804

D. Ren, D. Giannacopoulos, R. Suda

引用次数: 2

Parallel multistage preconditioners by Hierarchical Interface Decomposition on “T2K Open Super Computer (Todai Combined Cluster)” with Hybrid parallel programming models 基于混合并行编程模型的T2K开放式超级计算机(Todai组合集群)分层接口分解并行多级预调节器

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663785

K. Nakajima

引用次数: 1

Using cluster computing to support automatic and dynamic database clustering 利用集群计算支持自动和动态的数据库集群

2008 IEEE International Conference on Cluster Computing Pub Date : 2008-10-31 DOI: 10.1109/CLUSTR.2008.4663800

Sylvain Guinepain, L. Gruenwald

{"title":"Using cluster computing to support automatic and dynamic database clustering","authors":"Sylvain Guinepain, L. Gruenwald","doi":"10.1109/CLUSTR.2008.4663800","DOIUrl":"https://doi.org/10.1109/CLUSTR.2008.4663800","url":null,"abstract":"Query response time is the number one metrics when it comes to database performance. Because of data proliferation, efficient access methods and data storage techniques have become increasingly critical to maintain an acceptable query response time. Retrieving data from disk is several orders of magnitude slower than retrieving it from memory, it is easy to see the direct correlation between query response time and the number of disk I/Os. One of the common ways to reduce disk I/Os and therefore improve query response time is database clustering, which is a process that partitions the database vertically (attribute clustering) and/or horizontally (record clustering). A clustering is optimized for a given set of queries. However in dynamic systems the queries change with time, the clustering in place becomes obsolete, and the database needs to be re-clustered dynamically. This paper presents an efficient algorithm for attribute clustering that dynamically and automatically generates attribute clusters based on closed item sets mined from the attributes sets found in the queries running against the database. The paper then discusses how this algorithm can be implemented using the cluster computing paradigm to reduce query response time even further through parallelism and data redundancy.","PeriodicalId":198768,"journal":{"name":"2008 IEEE International Conference on Cluster Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130659775","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: 14