2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing最新文献_第4页

Cloud MapReduce: A MapReduce Implementation on Top of a Cloud Operating System 云MapReduce:基于云操作系统的MapReduce实现

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.25

Huan Liu, D. Orban

{"title":"Cloud MapReduce: A MapReduce Implementation on Top of a Cloud Operating System","authors":"Huan Liu, D. Orban","doi":"10.1109/CCGrid.2011.25","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.25","url":null,"abstract":"Like a traditional Operating System (OS), a cloud OS is responsible for managing the low level cloud resources and presenting a high level interface to the application programmers in order to hide the infrastructure details. However, unlike a traditional OS, a cloud OS has to manage these resources at scale. If a cloud OS has already taken on the complexity to make its services scalable, we should be able to greatly simplify a large-scale system design and implementation if we build on top of it. Unfortunately, a cloud's scale comes at a price. For example, Amazon cloud not only relies on horizontal scaling, but it also adopts a weaker consistency model called eventual consistency. We describe Cloud MapReduce (CMR), which implements the MapReduce programming model on top of the Amazon cloud OS. CMR is a demonstration that it is possible to overcome the cloud limitations and simplify system design and implementation by building on top of a cloud OS. We describe how we overcome the limitations presented by horizontal scaling and the weaker consistency guarantee. Our experimental results show that CMR runs faster than Hadoop, another implementation of MapReduce, and that CMR is a practical system. We believe that the techniques we used are general enough that they can be used to build other systems on top of a cloud OS.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124545994","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: 97

ASDF: An Autonomous and Scalable Distributed File System ASDF:一个自治和可扩展的分布式文件系统

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.21

Chien-Min Wang, Chi-Chang Huang, Huan Liang

引用次数: 6

A Parallel Rectangle Intersection Algorithm on GPU+CPU 基于GPU+CPU的并行矩形交点算法

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.13

Shih-Hsiang Lo, Che-Rung Lee, Yeh-Ching Chung, I. Chung

{"title":"A Parallel Rectangle Intersection Algorithm on GPU+CPU","authors":"Shih-Hsiang Lo, Che-Rung Lee, Yeh-Ching Chung, I. Chung","doi":"10.1109/CCGrid.2011.13","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.13","url":null,"abstract":"In this paper, we investigate efficient algorithms and implementations using GPU plus CPU to solve the rectangle intersection problem on a plane. The problem is to report all intersecting pairs of iso-oriented rectangles, whose parallelization on GPUs poses two major computational challenges: data partition and the massive output. The algorithm we presented is called PRI-GC, Parallel Rectangle Intersection algorithm on GPU+CPU, which consists of two phases: mapping and intersection-checking. In the mapping phase, rectangles are hashed into different subspaces (called cells) to reduce the unnecessary intersection checking for far-apart rectangles. In the intersection-checking phase, pairs of rectangles within the same cell are examined in parallel, and the intersecting pairs of rectangles are reported. Several optimization techniques, including rectangles re-ordering, output data compressing/encoding, and the execution overlapping of GPU and CPU, are applied to enhance the performance. We had evaluated the performance of PRI-GC and the result shows over 30x speedup against two well-implemented sequential algorithms on single CPU. The effectiveness of each optimization technique for this problem was evaluated as well. Several parameters, including different degrees of rectangle coverage, different block sizes, and different cell sizes, were also experimented to explore their influences on the performance of PRI-GC.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126690619","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: 10

Managing Distributed Files with RNS in Heterogeneous Data Grids 基于RNS的异构数据网格分布式文件管理

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.19

Y. Kawai, G. Iwai, Takashi Sasaki, Y. Watase

引用次数: 3

Defining a Cloud Reference Model 定义云参考模型

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.66

Teresa Tung

引用次数: 9

Contention Modeling for Multithreaded Distributed Shared Memory Machines: The Cray XMT 多线程分布式共享内存机的争用建模:Cray XMT

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.39

Simone Secchi, Antonino Tumeo, Oreste Villa

{"title":"Contention Modeling for Multithreaded Distributed Shared Memory Machines: The Cray XMT","authors":"Simone Secchi, Antonino Tumeo, Oreste Villa","doi":"10.1109/CCGrid.2011.39","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.39","url":null,"abstract":"Distributed Shared Memory (DSM) machines are a wide class of multi-processor computing systems where a large virtually-shared address space is mapped on a network of physically distributed memories. High memory latency and network contention are two of the main factors that limit performance scaling of such architectures. Modern high-performance computing DSM systems have evolved towards the exploitation of massive hardware multi-threading and fine-grained memory hashing to tolerate irregular latencies, avoiding network hot-spots and improving scalability. Parallel simulation is a promising approach, which has been extensively used to model the performance of such large-scale machines. One of the most critical factors in coping with the simulation speed-accuracy trade-off is network modeling. The Cray XMT is a massively multi-threaded supercomputing architecture that belongs to the DSM class. In this paper, we discuss the development of a network contention model for a full-system XMT simulator. We start by measuring the effects of network contention on a 128-processorXMT machine, we then investigate the trade-off that exists between simulation accuracy and speed, comparing three network models which operate at different levels of accuracy. The comparison and model validation is performed by executing a string-matching algorithm on the full-system simulator and on the actual machine, using three datasets that generate noticeably different contention patterns. Results prove that simulator accuracy in execution time remains within 10% of the real machine. We also show that the slowdown due to contention modeling is limited to 20%, when simulating a small number of processors, and becomes negligible for simulations with higher processor counts.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120949545","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

DDFTP: Dual-Direction FTP DDFTP:双向FTP

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.32

J. Al-Jaroodi, N. Mohamed

引用次数: 58

Enabling Multi-physics Coupled Simulations within the PGAS Programming Framework 在PGAS编程框架内实现多物理场耦合仿真

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.73

Fan Zhang, C. Docan, M. Parashar, S. Klasky

{"title":"Enabling Multi-physics Coupled Simulations within the PGAS Programming Framework","authors":"Fan Zhang, C. Docan, M. Parashar, S. Klasky","doi":"10.1109/CCGrid.2011.73","DOIUrl":"https://doi.org/10.1109/CCGrid.2011.73","url":null,"abstract":"Complex coupled multi-physics simulations are playing increasingly important roles in scientific and engineering applications such as fusion plasma and climate modeling. At the same time, extreme scales, high levels of concurrency and the advent of multicore and many core technologies are making the high-end parallel computing systems on which these simulations run, hard to program. While the Partitioned Global Address Space (PGAS) languages is attempting to address the problem, the PGAS model does not easily support the coupling of multiple application codes, which is necessary for the coupled multi-physics simulations. Furthermore, existing frameworks that support coupled simulations have been developed for fragmented programming models such as message passing, and are conceptually mismatched with the shared memory address space abstraction in the PGAS programming model. This paper explores how multi-physics coupled simulations can be supported within the PGAS programming framework. Specifically, in this paper, we present the design and implementation of the XpressSpace programming system, which enables efficient and productive development of coupled simulations across multiple independent PGAS Unified Parallel C (UPC) executables. XpressSpace provides the global-view style programming interface that is consistent with the memory model in UPC, and provides an efficient runtime system that can dynamically capture the data decomposition of global-view arrays and enable fast exchange of parallel data structures between coupled codes. In addition, XpressSpace provides the flexibility to define the coupling process in specification file that is independent of the program source codes. We evaluate the performance and scalability of Xpress Space prototype implementation using different coupling patterns extracted from real world multi-physics simulation scenarios, on the Jaguar Cray XT5 system of Oak Ridge National Laboratory.","PeriodicalId":376385,"journal":{"name":"2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117247068","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: 8

A Performance Goal Oriented Processor Allocation Technique for Centralized Heterogeneous Multi-cluster Environments 集中异构多集群环境下面向性能目标的处理器分配技术

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.81

Po-Chi Shih, Kuo-Chan Huang, Che-Rung Lee, I. Chung, Yeh-Ching Chung

引用次数: 2

A Hybrid Shared-Nothing/Shared-Data Storage Architecture for Large Scale Databases 面向大型数据库的无共享/数据共享混合存储架构

2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing Pub Date : 2011-05-23 DOI: 10.1109/CCGrid.2011.78

Huaiming Song, Xian-He Sun, Yong Chen

引用次数: 2