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Scheduling on Unspecified Heterogeneous distributed Resources

Scheduling on Unspecified Heterogeneous Distributed Resource...

引用

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

作者： Daniel Millot Christian Parrot Computer Science Department TELECOM & Management SudParis Evry France

In this paper, we present an adaptive method for scheduling parallel applications on unspecified distributed memory platforms. The presented method can be used to schedule parallel applications when the total workload and the execution parameters (communication speed, available computing power...) are unspecified. When used to schedule divisible load applications according to a master worker model, this method delivers the workload through multiple rounds. In order to maximize the throughput of each worker, it can prevent both idleness in the use of workers and contentions in the use of the links between master and workers. Before focusing on the proposed scheduling method, the paper recalls the underlying methods on which its development relies. The paper then gives a theoretical analysis of the method before presenting results of simulations obtained with the Sim Grid framework on a limited distributed memory platform.

关键词： Processor scheduling Load modeling Scheduling Computational modeling Throughput Adaptation models Equations

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A Fault-Tolerant High Performance Cloud Strategy for Scientific computing

A Fault-Tolerant High Performance Cloud Strategy for Scienti...

引用

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

作者： Ekpe Okorafor Computer Science and Engineering African University of Science and Technology (AUST) Abuja Nigeria

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, high-performance computing solutions and installed facilities such as clusters and super computers have been employed to address these needs. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure with the ability to perform parallel computations using large pools of virtual machines (VMs). The infrastructure services (Infrastructure-as-a-service), provided by these cloud vendors, allow any user to provision a large number of compute instances. However, scientific computing is typically characterized by complex communication patterns and requires optimized runtimes. Today, VMs are manually instantiated, configured and maintained by cloud users. These coupled with the latency, crash and omission failures in service providers, results in an inefficient use of VMs, increased complexity in VM-management tasks, a reduction in the overall computation power and increased time for task completion. In this paper, a high performance cloud computing strategy is proposed that combines the adaptation of a parallel processing framework, such as the Message Passing Interface (MPI) and an efficient checkpoint infrastructure for VMs, enabling its effective use for scientific computing. By developing such a mechanism, we can achieve optimized runtimes comparable to native clusters, improve checkpoints with low interference on task execution and provide efficient task recovery. In addition, check pointing is used to minimize the cost and volatility of resource provisioning, while improving overall reliability. Analysis and simulations show that the proposed approach compares favorably with the native cluster MPI implementations.

关键词： Checkpointing Cloud computing Computer architecture Program processors Protocols Fault tolerance Fault tolerant systems

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Privacy Protection in Service Discovery for Large-Scale distributed computing Systems

Privacy Protection in Service Discovery for Large-Scale Dist...

引用

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

作者： Jun Yeol Choi Zhong Yuan Li Hee Yong Youn Ohyoung Song School of Information and Communication Engineering Sungkyunkwan University Suwon South Korea School of Electrical and Electronics Engineering Chung-Ang University Seoul South Korea

Service discovery is one of the integral tasks of the system running in distributed computing environment. Even though numerous researches on service discovery have been conducted, the security and privacy issue have not yet been addressed adequately. Here letting only legitimate user and service provider for service discovery is a challenging problem. In this paper we present a scheme for secure service discovery by employing an incremental progressive exposure approach, where the user and service provider exchange only partial encrypted information at each step. They compare the information sent from the other party with their own information. The exchange operation continues until a mismatch occurs or the required service is found finally. Computer simulation reveals that the proposed approach significantly reduces the number of message exchanges compared to the existing progressive exposure scheme.

关键词： Privacy Cryptography Servers Protocols Computational modeling Lifting equipment

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Volunteer Cloud computing: MapReduce over the Internet

Volunteer Cloud Computing: MapReduce over the Internet

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

作者： Fernando Costa Luis Silva Michael Dahlin CISUC Department of Informatics Engineering University of Coimbra Portugal Laboratory for Advanced Systems Research University of Texas Austin USA

Volunteer computing harnesses computing resources of machines from around the world to perform distributed independent tasks, following a master/worker model. Despite the recent increase in popularity and power in middleware such as BOINC, there are still several limitations in existing systems. Current research is oriented towards optimizing existing applications, while the number of active users and projects has reached a plateau. A programming paradigm that has been significantly popular and is used by several systems on the cloud is MapReduce. The main advantage of this paradigm is that it can be used to solve a vast amount of different problems, by breaking them into simple steps and taking advantage of distributed resources. Volunteer computing provides these resources, and although it cannot match the conditions offered by a cluster, it has other advantages that can be leveraged. In this paper, we try to increase the computational power of Volunteer computing systems by allowing more complex applications and paradigms such as MapReduce to be run, thus opening new avenues and possibilities for the use of computational devices scattered through the Internet. We created a BOINC prototype that can run MapReduce jobs (BOINC-MR), using a pull-model in which communication is always initiated by the client. By running experiments on a small cluster, with multiple variables, we were able to evaluate a few initial scenarios with this paradigm. We used a simple MapReduce application, word count, as proof of concept, just to demonstrate a typical execution.

关键词： Servers Prototypes Internet Peer to peer computing Hardware Fires Computational modeling

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A hierarchical framework for cross-domain MapReduce execution

A hierarchical framework for cross-domain MapReduce executio...

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2nd international Workshop on Emerging Computational Methods for the Life Sciences, ECMLS'11, Co-located with 20th international ACM symposium on High-Performance parallel and distributed computing, HPDC 2011

作者： Luo, Yuan Guo, Zhenhua Sun, Yiming Plale, Beth Qiu, Judy Li, Wilfred W. School of Informatics and Computing Indiana University Bloomington IN 47405 United States San Diego Supercomputer Center University of California San Diego San Diego CA 92093 United States

ISBN: (纸本)9781450307024

The MapReduce programming model provides an easy way to execute pleasantly parallel applications. Many data-intensive life science applications fit this programming model and benefit from the scalability that can be delivered using this model. One such application is AutoDock, which consists of a suite of automated tools for predicting the bound conformations of flexible ligands to macromolecular targets. However, researchers also need sufficient computation and storage resources to fully enjoy the benefit of MapReduce. For example, a typical AutoDock based virtual screening experiment usually consists of a very large number of docking processes from multiple ligands and is often time consuming to run on a single MapReduce cluster. Although commercial clouds can provide virtually unlimited computation and storage resources on-demand, due to financial, security and possibly other concerns, many researchers still run experiments on a number of small clusters with limited number of nodes that cannot unleash the full power of MapReduce. In this paper, we present a hierarchical MapReduce framework that gathers computation resources from different clusters and run MapReduce jobs across them. The global controller in our framework splits the data set and dispatches them to multiple "local" MapReduce clusters, and balances the workload by assigning tasks in accordance to the capabilities of each cluster and of each node. The local results are then returned back to the global controller for global reduction. Our experimental evaluation using AutoDock over MapReduce shows that our load-balancing algorithm makes promising workload distribution across multiple clusters, and thus minimizes overall execution time span of the entire MapReduce execution. © Copyright 2011 ACM.

关键词： MapReduce

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XML Processing in the Cloud: Large-Scale Digital Preservation in Small Institutions

XML Processing in the Cloud: Large-Scale Digital Preservatio...

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

作者： Peter Wittek Thierry Jacquin Herve Dejean Jean-Pierre Chanod S´ndor Dar´nyi Swedish School of Library and Information Science University of Borås Boras Sweden Swedish School of Library and Information Science University of Barås Barås Sweden Xerox Research Centre Europe Meylan France

Digital preservation deals with the problem of retaining the meaning of digital information over time to ensure its accessibility. The process often involves a workflow which transforms the digital objects. The workflow defines document pipelines containing transformations and validation checkpoints, either to facilitate migration for persistent archival or to extract metadata. The transformations, nevertheless, are computationally expensive, and therefore digital preservation can be out of reach for an organization whose core operation is not in data conservation. The operations described the document workflow, however, do not frequently reoccur. This paper combines an implementation-independent workflow designer with cloud computing to support small institution in their ad-hoc peak computing needs that stem from their efforts in digital preservation.

关键词： XML Pipelines Organizations Cloud computing Complexity theory Context Production

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Efficiently computing Tensor Eigenvalues on a GPU

Efficiently Computing Tensor Eigenvalues on a GPU

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

作者： Grey Ballard Tamara Kolda Todd Plantenga Computer Science Department University of California Berkeley Berkeley CA USA Sandia National Laboratories Livermore CA USA

The tensor eigenproblem has many important applications, generating both mathematical and application-specific interest in the properties of tensor eigenpairs and methods for computing them. A tensor is an m-way array, generalizing the concept of a matrix (a 2-way array). Kolda and Mayo have recently introduced a generalization of the matrix power method for computing real-valued tensor eigenpairs of symmetric tensors. In this work, we present an efficient implementation of their algorithm, exploiting symmetry in order to save storage, data movement, and computation. For an application involving repeatedly solving the tensor eigenproblem for many small tensors, we describe how a GPU can be used to accelerate the computations. On an NVIDIA Tesla C 2050 (Fermi) GPU, we achieve 318 Gflops/s (31% of theoretical peak performance in single precision) on our test data set.

关键词： Indexes Tensile stress Arrays Symmetric matrices Graphics processing unit Equations Eigenvalues and eigenfunctions

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Real-Time Image Rectification and Stereo Reconstruction System on the GPU

Real-Time Image Rectification and Stereo Reconstruction Syst...

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international symposium on parallel and distributed computing

作者： Cosmin D. Pantilie Istvan Haller Marius Drulea Sergiu Nedevschi Technical University of Cluj-Napoca Romania

The increase in computational power of consumer graphic cards has successfully motivated adaptation of stereo algorithms to this kind of hardware. In order to solve the stereo correspondence problem efficiently, the images need to be rectified and lens distortions need to be removed. This paper presents an efficient two step solution for rectifying and correcting lens distortions in images captured using a pair of stereo cameras. The first step consists of a one-time, off-line calculation of a look-up table, based on the calibration parameters, for each of the two cameras. The second step computes the final pixel intensities based on the pre-calculated mappings stored in the look-up table. The GPU implementation proposed makes use of the inherent parallelism in a cost-effective manner, making the method suitable for rectifying high resolution images in real-time. Results are compared against an optimized CPU-based implementation, written in assembly language using MMX instructions, for reference. The complete stereo reconstruction system was implemented and evaluated on a current generation GPU and offers a running time of 11ms for images with resolution 512×383.

关键词： Graphics processing unit parallel processing Optimization Stereo image processing Cameras Image reconstruction Image resolution

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Hybrid OpenCL: Enhancing OpenCL for distributed Processing

Hybrid OpenCL: Enhancing OpenCL for Distributed Processing

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ryo Aoki Shuichi Oikawa Takashi Nakamura Satoshi Miki Department of Computer Science University of Tsukuba Tsukuba Japan Fixstars Corp. Tokyo Japan

We have been developing Hybrid OpenCL, which enables the utilization of OpenCL devices by connecting them over the network. Hybrid OpenCL opens a gate to scale up OpenCL environments. By using Hybrid OpenCL, applications written in OpenCL can be easily ported to high performance cluster computers, thus, Hybrid OpenCL can provide more various distributed and parallel computing platforms. Hybrid OpenCL consists of two elements, a runtime system that provides the abstraction of OpenCL implementations and a bridge program that connects multiple OpenCL runtime systems over the network. This paper describes the design and implementation Hybrid OpenCL. Its performance for distributed processing is evaluated by using multiple nodes. The experimentation results show that the overhead introduced by Hybrid OpenCL is minimum and comparable with the combination of MPI and OpenCL.

关键词： Runtime Kernel Bridges Context Performance evaluation distributed processing Resource management

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GLive: The Gradient Overlay as a Market Maker for Mesh-Based P2P Live Streaming

GLive: The Gradient Overlay as a Market Maker for Mesh-Based...

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international symposium on parallel and distributed computing

作者： Amir H. Payberah Jim Dowling Seif Haridi KTH Royal Institute of Technology Sweden Swedish Institute of Computer Science (SICS) Sweden

Peer-to-Peer (P2P) live video streaming over the Internet is becoming increasingly popular, but it is still plagued by problems of high playback latency and intermittent playback streams. This paper presents GLive, a distributed market-based solution that builds a mesh overlay for P2P live streaming. The mesh overlay is constructed such that (i) nodes with increasing upload bandwidth are located closer to the media source, and (ii) nodes with similar upload bandwidth become neighbours. We introduce a market-based approach that matches nodes willing and able to share the stream with one another. However, market-based approaches converge slowly on random overlay networks, and we improve the rate of convergence by adapting our market-based algorithm to exploit the clustering of nodes with similar upload bandwidths in our mesh overlay. We address the problem of free-riding through nodes preferentially uploading more of the stream to the best up loaders. We compare GLive with our previous tree-based streaming protocol, Sepidar, and New Cools treaming in simulation, and our results show significantly improved playback continuity and playback latency.

关键词： Peer to peer computing Bandwidth Media Convergence Streaming media Topology DSL

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