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Using User-Level Virtualization in Desktop Grid Clients for Application Delivery and Sandboxing

Using User-Level Virtualization in Desktop Grid Clients for ...

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international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Youhui Zhang Yanhua Li Weimin Zheng Department of Computer Science and Technology Tsinghua University Beijing China

Desktop grid is a form of distributed computing that harvests the computing power of idle desktop computers whether these are volunteers or deployed at an institution. This paper proposes to design and implement a client-end framework of desktop grid, based on cloud storage, which can also deliver legacy applications on-demand to volunteer machines. From technical viewpoint, an isolated execution environment based on user-level virtualization will be achieved: on one side, some resource-accessing APIs for applications are intercepted and redirected to real storage positions (on the cloud); therefore legacy applications can run without installation to simplify maintenance. On the other side, this mechanism can also construct a sandbox to restrict the access range of applications for security. This proposal adopts promising cloud-related technologies for requirements of volunteer computing, tests show that it only causes limited performance loss.

关键词： File systems Cloud computing Servers Hidden Markov models Grid computing Security

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Optimal Load Distribution for Multiple Heterogeneous Blade Servers in a Cloud computing Environment

Optimal Load Distribution for Multiple Heterogeneous Blade S...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Keqin Li Department of Computer Science State University of New York New Paltz NY USA

Given a group of heterogeneous blade servers in a cloud computing environment or a data center of a cloud computing provider, each having its own size and speed and its own amount of preloaded special tasks, we are facing the problem of optimal distribution of generic tasks over these blade servers, such that the average response time of generic tasks is minimized. Such performance optimization is important for a cloud computing provider to efficiently utilize all the available resources and to deliver the highest quality of service. We develop a queueing model for a group of heterogeneous blade servers, and formulate and solve the optimal load distribution problem of generic tasks for multiple heterogeneous blade servers in a cloud computing environment in two different situations, namely, special tasks with and without higher priority. Extensive numerical examples and data are demonstrated and some important observations are made. It is found that server sizes, server speeds, task execution requirement, and the arrival rates of special tasks all have significant impact on the average response time of generic tasks, especially when the total arrival rate of generic tasks is large. It is also found that the server size heterogeneity and the server speed heterogeneity do not have much impact on the average response time of generic tasks. Furthermore, larger (smaller, respectively) heterogeneity results in shorter (longer, respectively) average response time of generic tasks.

关键词： Servers Blades Cloud computing Silicon Time factors Computational modeling Load management

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A parallel Packet Processing Method On Multi-Core Systems

A Parallel Packet Processing Method On Multi-Core Systems

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2011年第十届分布式计算及其应用国际学术研讨会

作者： Yunchun Li Xinxin Qiao Computer Science Department Beihang University

The demand of network packet processing is increasing as applications demand more and more bandwidth and computing *** this paper,the characteristics of packet processing and multi-core syst

关键词： Multicore processing Instruction sets Inspection Internet Hardware

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Translating Chapel to Use FREERIDE: A Case Study in Using an HPC Language for Data-Intensive computing

Translating Chapel to Use FREERIDE: A Case Study in Using an...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Bin Ren Gagan Agrawal Brad Chamberlain Steve Deitz Department of Computer Science and Engineering Ohio State Uinversity Columbus OH USA Cray Inc. Seattle WA USA

In the last few years, the growing significance of data-intensive computing has been closely tied to the emergence and popularity of new programming paradigms for this class of applications, including Map-Reduce, and new high-level languages for data-intensive computing. The ultimate goal of these efforts in data-intensive computing has been to achieve parallelism with as little effort as possible, while supporting high efficiency and scalability. While these are also the goals that the parallel language/compiler community has tried meeting for the past several decades, the development of languages and programming systems for data-intensive computing has largely been in isolation to the developments in general parallel programming. Such independent developments in the two areas, i.e., data-intensive computing and high productivity languages lead to the following questions: I) Are HPC languages suitable for expressing data-intensive computations? and if so, II.a) What are the issues in using them for effective parallel programming? or, if not, II.b) What characteristics of data-intensive computations force the need for separate language support?. This paper takes a case study to address these questions. Particularly, we study the suitability of Chapel for expressing data-intensive computations. We also examine compilation techniques required for directly invoking a data-intensive middleware from Chapel's compilation system. The data-intensive middleware we use in this effort is FREERIDE that has been developed at Ohio State. We show how certain transformations can enable efficient invocation of the FREERIDE functions from the Chapel compiler. Our experiments show that after certain optimizations, the performance of the version of Chapel compiler that invokes FREERIDE functions is quite comparable to the performance of hand-written data-intensive applications.

关键词： Arrays Middleware Productivity Runtime Data mining Program processors

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The Evaluation of an Effective Out-of-Core Run-Time System in the Context of parallel Mesh Generation

The Evaluation of an Effective Out-of-Core Run-Time System i...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Andriy Kot Andrey N. Chernikov Nikos P. Chrisochoides Computer Science Department College of William and Mary Williamsburg VA USA Computer Science Department Old Dominion University Norfolk VA USA

We present an out-of-core run-time system that supports effective parallel computation of large irregular and adaptive problems, in particular unstructured mesh generation (PUMG). PUMG is a highly challenging application due to intensive memory accesses, unpredictable communication patterns, and variable and irregular data dependencies reflecting the unstructured spatial connectivity of mesh elements. Our runtime system allows to transform the footprint of parallel applications from wide and shallow into narrow and deep by extending the memory utilization to the out-of-core level. It simplifies and streamlines the development of otherwise highly time consuming out-of-core applications as well as the converting of existing applications. It utilizes disk, network and memory hierarchy to achieve high utilization of computing resources without sacrificing performance with PUMG. The runtime system combines different programming paradigms: multi-threading within the nodes using industrial strength software framework, one-sided active messages among the nodes, and an out-of-core subsystem for managing large datasets. We performed an evaluation on traditional parallel platforms to stress test all layers of the run-time system using three different PUMG methods with significantly varying communication and synchronization patterns. We demonstrated high overlap in computation, communication, and disk I/O which results in good performance when computing large out-of-core problems. The runtime system adds very small overhead (up to 18% on most configurations) when computing in-core which means performance is not compromised.

关键词： Mobile communication Runtime Mesh generation Libraries parallel processing Memory management Synchronization

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Single Node On-Line Simulation of MPI Applications with SMPI

Single Node On-Line Simulation of MPI Applications with SMPI

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international symposium on parallel and distributed Processing (IPDPS)

作者： Pierre-Nicolas Clauss Mark Stillwell Stephane Genaud Frederic Suter Henri Casanova Martin Quinson LORIA INRIA Nancy University Nancy France Department of Information and Computer Sciences University of Hawaii Mānoa Honolulu USA ICPS-LSIIT University of Strasbourg Illkirch-Graffenstaden France IN2P3 Computing Center CNRS IN2P3 Lyon-Villeurbanne France

Simulation is a popular approach for predicting the performance of MPI applications for platforms that are not at one's disposal. It is also a way to teach the principles of parallel programming and high-performance computing to students without access to a parallel computer. In this work we present SMPI, a simulator for MPI applications that uses on-line simulation, i.e., the application is executed but part of the execution takes place within a simulation component. SMPI simulations account for network contention in a fast and scalable manner. SMPI also implements an original and validated piece-wise linear model for data transfer times between cluster nodes. Finally SMPI simulations of large-scale applications on large-scale platforms can be executed on a single node thanks to techniques to reduce the simulation's compute time and memory footprint. These contributions are validated via a large set of experiments in which SMPI is compared to popular MPI implementations with a view to assess its accuracy, scalability, and speed.

关键词： Computational modeling Analytical models Delay Data models Random access memory Scalability Kernel

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iMapReduce: A distributed computing Framework for Iterative Computation

iMapReduce: A Distributed Computing Framework for Iterative ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yanfeng Zhang Qinxin Gao Lixin Gao Cuirong Wang Northeastern University China University of Massachusetts Amherst USA

Relational data are pervasive in many applications such as data mining or social network analysis. These relational data are typically massive containing at least millions or hundreds of millions of relations. This poses demand for the design of distributed computing frameworks for processing these data on a large cluster. MapReduce is an example of such a framework. However, many relational data based applications typically require parsing the relational data iteratively and need to operate on these data through many iterations. MapReduce lacks built-in support for the iterative process. This paper presents iMapReduce, a framework that supports iterative processing. iMapReduce allows users to specify the iterative operations with map and reduce functions, while supporting the iterative processing automatically without the need of users' involvement. More importantly, iMapReduce significantly improves the performance of iterative algorithms by (1) reducing the overhead of creating a new task in every iteration, (2) eliminating the shuffling of the static data in the shuffle stage of MapReduce, and (3) allowing asynchronous execution of each iteration, i.e., an iteration can start before all tasks of a previous iteration have finished. We implement iMapReduce based on Apache Hadoop, and show that iMapReduce can achieve a factor of 1.2 to 5 speedup over those implemented on MapReduce for well-known iterative algorithms.

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An Adaptive Response Routing Mechanism to Improve DHT Performance in the Presence of NATs

An Adaptive Response Routing Mechanism to Improve DHT Perfor...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David A. Bryan Cogent Force LLC and College of William and Mary Williamsburg VA USA

Asymmetric network connectivity, frequently the result of NATs in the network environment, can significantly impact the selection of an appropriate DHT response routing technique. We show that neither response mechanism commonly used today (symmetric recursive or direct response) function well for all network NAT densities. We present an adaptive response routing algorithm for use in generic libraries or protocols where the deployment environment is not known in advance. Through worst-case analytical analysis and average-case simulation we show that our technique offers a significant performance advantage in environments (such as the Internet) where NAT densities are not known a priori.

关键词： Strontium Routing Peer to peer computing Analytical models Adaptation models Accuracy Internet

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CATCH: A Cloud-Based Adaptive Data Transfer Service for HPC

CATCH: A Cloud-Based Adaptive Data Transfer Service for HPC

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international symposium on parallel and distributed Processing (IPDPS)

作者： Henry M. Monti Ali R. Butt Sudharshan S. Vazhkudai Department of Computer Science Virginia Polytechnic Institute and State University USA Computer Science and Mathematics Division Oak Ridge National Laboratory USA

Modern High Performance computing (HPC) applications process very large amounts of data. A critical research challenge lies in transporting input data to the HPC center from a number of distributed sources, e.g., scientific experiments and web repositories, etc., and offloading the result data to geographically distributed, intermittently available end-users, often over under-provisioned connections. Such end-user data services are typically performed using point-to-point transfers that are designed for well-endowed sites and are unable to reconcile the center's resource usage and users' delivery deadlines, unable to adapt to changing dynamics in the end-to-end data path and are not fault-tolerant. To overcome these inefficiencies, decentralized HPC data services are emerging as viable alternatives. In this paper, we develop and enhance such distributed data services by designing CATCH, a Cloud-based Adaptive data Transfer service for HPC. CATCH leverages a bevy of cloud storage resources to orchestrate a decentralized data transport with fail-over capabilities. Our results demonstrate that CATCH is a feasible approach, and can help improve the data transfer times at the HPC center by as much as 81.1% for typical HPC workloads.

关键词： Cloud computing distributed databases Software Data models Collaboration Availability Fault tolerance

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Flexible Development of Dense Linear Algebra Algorithms on Massively parallel Architectures with DPLASMA

Flexible Development of Dense Linear Algebra Algorithms on M...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： George Bosilca Aurelien Bouteiller Anthony Danalis Mathieu Faverge Azzam Haidar Thomas Herault Jakub Kurzak Julien Langou Pierre Lemarinier Hatem Ltaief Piotr Luszczek Asim YarKhan Jack Dongarra Innovative Computing Laboratory University of Tennessee USA Oak Ridge National Laboratory USA University of Paris XI France University of Colorado Denver USA

We present a method for developing dense linear algebra algorithms that seamlessly scales to thousands of cores. It can be done with our project called DPLASMA (distributed PLASMA) that uses a novel generic distributed Direct Acyclic Graph Engine (DAGuE). The engine has been designed for high performance computing and thus it enables scaling of tile algorithms, originating in PLASMA, on large distributed memory systems. The underlying DAGuE framework has many appealing features when considering distributed-memory platforms with heterogeneous multicore nodes: DAG representation that is independent of the problem-size, automatic extraction of the communication from the dependencies, overlapping of communication and computation, task prioritization, and architecture-aware scheduling and management of tasks. The originality of this engine lies in its capacity to translate a sequential code with nested-loops into a concise and synthetic format which can then be interpreted and executed in a distributed environment. We present three common dense linear algebra algorithms from PLASMA (parallel Linear Algebra for Scalable Multi-core Architectures), namely: Cholesky, LU, and QR factorizations, to investigate their data driven expression and execution in a distributed system. We demonstrate through experimental results on the Cray XT5 Kraken system that our DAG-based approach has the potential to achieve sizable fraction of peak performance which is characteristic of the state-of-the-art distributed numerical software on current and emerging architectures.

关键词： Tiles Engines Linear algebra Heuristic algorithms Multicore processing Plasmas

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