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CUDA-For-Clusters: A System for Efficient Execution of CUDA Kernels on Multi-core Clusters

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18th international conference on Euro-Par parallel Processing

作者： Prabhakar, Raghu Govindarajan, R. thazhuthaveetil, Matthew J. Univ Calif Los Angeles Los Angeles CA 90095 USA Indian Inst Sci Supercomp Educ & Res Ctr Bangalore Karnataka India

ISBN: (纸本)9783642328206

Rapid advancements in multi-core processor architectures coupled with low-cost, low-latency, high-bandwidth interconnects have made clusters of multi-core machines a common computing resource. Unfortunately, writing good parallel programs that efficiently utilize all the resources in such a cluster is still a major challenge. Various programming languages have been proposed as a solution to this problem, but are yet to be adopted widely to run performance-critical code mainly due to the relatively immature software framework and the effort involved in re-writing existing code in the new language. In this paper, we motivate and describe our initial study in exploring CUDA as a programming language for a cluster of multi-cores. We develop CUDA-For-Clusters (CFC), a framework that transparently orchestrates execution of CUDA kernels on a cluster of multi-core machines. the well-structured nature of a CUDA kernel, the growing popularity, support and stability of the CUDA software stack collectively make CUDA a good candidate to be considered as a programming language for a cluster. CFC uses a mixture of source-to-source compiler transformations, a work distribution run-time and a light-weight software distributed shared memory to manage parallel executions. Initial results on running several standard CUDA benchmark programs achieve impressive speedups of up to 7.5X on a cluster with 8 nodes, thereby opening up an interesting direction of research for further investigation.

关键词： CUDA Multi-Cores distributed Programming distributed systems Clusters Software distributed Shared Memory

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Measuring distributed Applications through MapReduce and Traffic Analysis

Measuring Distributed Applications through MapReduce and Tra...

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international conference on parallel and distributed systems (ICPADS)

作者： thiago Vieira Paulo Soares Marco Machado Rodrigo Assad Vinicius Garcia Federal University of Pernambuco (UFPE) Recife Pernambuco Brazil

Monitoring and evaluating distributed systems at runtime is a difficult effort. Network traffic analysis may be used to analyse distributed systems, but the capacity to analyse large amount of traffic is a challenge. this work proposes the use of Map Reduce for deeply inspecting distributed systems network traffic, evaluating the effectiveness and the processing capacity of Map Reduce for this inspection.

关键词： Peer to peer computing Inspection Scalability Monitoring Runtime Sockets Hardware

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Using Load Information in Work-Stealing on distributed systems with Non-uniform Communication Latencies

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18th international conference on Euro-Par parallel Processing

作者： Janjic, Vladimir Hammond, Kevin Univ St Andrews Sch Comp Sci St Andrews KY16 9AJ Fife Scotland

ISBN: (纸本)9783642328206

We evaluate four state-of-the-art work-stealing algorithms for distributed systems with non-uniform communication latenices (Random Stealing, Hierarchical Stealing, Cluster-aware Random Stealing and Adaptive Cluster-aware Random Stealing) on a set of irregular Divide-and-Conquer (D&C) parallel applications. We also investigate the extent to which these algorithms could be improved if dynamic load information is available, and how accurate this information needs to be. We show that, for highly-irregular D&C applications, the use of load information can significantly improve application speedups, whereas there is little improvement for less irregular ones. Furthermore, we show that when load information is used, Cluster-aware Random Stealing gives the best speedups for both regular and irregular D&C applications.

关键词： Dynamic loads

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Enabling Cloud Interoperability with COMPSs

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18th international conference on Euro-Par parallel Processing

作者： Marozzo, Fabrizio Lordan, Francesc Rafanell, Roger Lezzi, Daniele Talia, Domenico Badia, Rosa M. Univ Calabria DEIS I-87036 Arcavacata Di Rende CS Italy Spanish Council Sci Res CSIC Artificial Intelligence Res Inst IIIA Madrid Spain Univ Calabria DEIS Arcavacata Di Rende Italy CNR ICAR Arcavacata Di Rende Italy

ISBN: (纸本)9783642328206

the advent of Cloud computing has given to researchers the ability to access resources that satisfy their growing needs, which could not be satisfied by traditional computing resources such as PCs and locally managed clusters. On the other side, such ability, has opened new challenges for the execution of their computational work and the managing of massive amounts of data into resources provided by different private and public infrastructures. COMP Superscalar (COMPSs) is a programming framework that provides a programming model and a runtime that ease the development of applications for distributed environments and their execution on a wide range of computational infrastructures. COMPSs has been recently extended in order to be interoperable with several cloud technologies like Amazon, OpenNebula, Emotive and other OCCI compliant offerings. this paper presents the extensions of this interoperability layer to support the execution of COMPSs applications into the Windows Azure Platform. the framework has been evaluated through the porting of a data mining workflow to COMPSs and the execution on an hybrid testbed.

关键词： parallel programming models Cloud computing Data mining PaaS

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Data Value Chain as a Service Framework: For Enabling Data Handling, Data Security and Data Analysis in the Cloud

Data Value Chain as a Service Framework: For Enabling Data H...

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international conference on parallel and distributed systems (ICPADS)

作者： Henry Kasim Terence Hung Xiaorong Li A*STAR Institute of High Performance Computing Singapore

the concept of Data Value Chain (DVC) involves the chain of activities to collect, manage, share, integrate, harmonize and analyze data for scientific or enterprise insight. For some applications, it also entails the leverage of visualization and simulation. However, the curse of big data (volume, velocity, variety) makes it difficult to efficiently handle and understand the data in near real-time. To address these challenges, this paper proposed the Data Value Chain as a Service (DVCaaS) framework, a data-oriented approach for data handling, data security and analytics in the cloud environment.

关键词： Data analysis Data visualization Data privacy distributed databases Computational modeling Analytical models Data models

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Multicore computing for SIFT Algorithm in MATLAB® parallel Environment

Multicore Computing for SIFT Algorithm in MATLAB® Parallel ...

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international conference on parallel and distributed systems (ICPADS)

作者： Hua Cao Jiazhong Chen School of Software Engineer Huzhong University of Science and Technology Wuhan China

An important processing stage in computer vision such as recognizes an object is feature extraction. Among those feature extraction algorithms, Lowe proposed the scale-invariant feature transform (SIFT) algorithm has been considered as one of the robust approaches. But due to the implementation of the convolution operation, the computing of SIFT algorithm is highly time-consuming. there are such problems as consuming too much power, sacrifice accuracy and lacking scalability for hardware-based acceleration scheme, it is still necessary to find the software-based acceleration approaches, especially for some applications and researches which need high-precision matched. Matlab is a general algorithm development environment with powerful image processing and other supporting toolboxes. With the rapid development of multicore CPU technology, using multicore computer and Matlab is an intuitive and simple way to speed up the computing for SIFT algorithm. In this paper, we try to have a view for using the Matlab parallel toolbox to accelerate the SIFT algorithm by two schemes of task-parallelism and data-parallelism modal. the results show that the parallel versions of former sequential algorithm with simple modifications achieve the speedup up to 6.6 times.

关键词： MATLAB parallel processing Multicore processing Feature extraction Acceleration Computers Histograms

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An Optimal Fully distributed Algorithm to Minimize the Resource Consumption of Cloud Applications

An Optimal Fully Distributed Algorithm to Minimize the Resou...

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international conference on parallel and distributed systems (ICPADS)

作者： Nikos Tziritas Samee Ullah Khan Cheng-Zhong Xu Jue Hong Chinese Academy of Sciences China North Dakota State University USA

According to the pay-per-use model adopted in clouds, the more the resources consumed by an application running in a cloud computing environment, the greater the amount of money the owner of the corresponding application will be charged. therefore, applying intelligent solutions to minimize the resource consumption is of great importance. Because centralized solutions are deemed unsuitable for large-distributed systems or large-scale applications, we propose a fully distributed algorithm (called DRA) to overcome the scalability issues. the aforementioned problem can be solved by identifying an assignment scheme between the interacting components of an application, such as processes and virtual machines, and the computing nodes of a cloud system, such that the total amount of resources consumed by the respective application is minimized. the decisions for the transition from one assignment scheme to another one are made in a dynamic way and based only on local information. It should be stressed that DRA achieves convergence and always results in the optimal solution. We also show, through an experimental evaluation, that DRA achieves up to 55% network cost reduction when compared to the most recent algorithm in the literature.

关键词： Gravity distributed algorithms Virtual machining Computational modeling Minimization Algorithm design and analysis Measurement

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parallel computations in Java with PCJ library

Parallel computations in Java with PCJ library

引用

2012 10th Annual international conference on High Performance computing and Simulation, HPCS 2012

作者： Nowicki, Marek Bala, Piotr Faculty of Mathematics and Computer Science Nicolaus Copernicus University Chopina 12/18 87-100 Toruń Poland Interdisciplinary Centre for Mathematical and Computational Modelling University of Warsaw Pawińskiego 5a 02-106 Warsaw Poland

ISBN: (纸本)9781467323598

In this paper we present PCJ - a new library for parallel computations in Java inspired by the partitioned global address space approach. We present design details together with the examples of usage for basic operations such as a point-point communication, synchronization or broadcast. the PCJ library is easy to use and allows for a fast development of the parallel programs. It allows to develop distributed applications in easy way, hiding communication details which allows user to focus on the implementation of the algorithm rather than on network or threads programming. the objects and variables are local to the program threads however some variables can be marked shared which allows to access them from different nodes. For shared variables PCJ offers one-sided communication which allows for easy access to the data stored at the different nodes. the parallel programs developed with PCJ can be run on the distributed systems with different Java VM running on the nodes. In the paper we present evaluation of the performance of the PCJ communication on the state of art hardware. the results are compared with the native MPI implementation showing good performance and scalability of the PCJ. © 2012 IEEE.

关键词： Java programming language

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Pipelined parallel LZSS for Streaming Data Compression on GPGPUs

Pipelined Parallel LZSS for Streaming Data Compression on GP...

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international conference on parallel and distributed systems (ICPADS)

作者： Adnan Ozsoy Martin Swany Arun Chauhan School of Informatics and Computing Indiana University Bloomington

In this paper, we present an algorithm and provide design improvements needed to port the serial Lempel-Ziv-Storer-Szymanski (LZSS), lossless data compression algorithm, to a parallelized version suitable for general purpose graphic processor units (GPGPU), specifically for NVIDIA's CUDA Framework. the two main stages of the algorithm, substring matching and encoding, are studied in detail to fit into the GPU architecture. We conducted detailed analysis of our performance results and compared them to serial and parallel CPU implementations of LZSS algorithm. We also benchmarked our algorithm in comparison with well known, widely used programs, GZIP and ZLIB. We achieved up to 34x better throughput than the serial CPU implementation of LZSS algorithm and up to 2.21x better than the parallelized version.

关键词： Graphics processing units Instruction sets History Data compression Algorithm design and analysis Encoding Software algorithms

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Multicast Congestion Control Using Communicating Realtime State Machines and Dynamical systems for distributed Iptv Scenarios

Multicast Congestion Control Using Communicating Realtime St...

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international conference on parallel and distributed systems (ICPADS)

作者： Oscar M. Bonastre Jose Valero Angel Gimenez Jose Maria Amigo Operations Research Centre Miguel Hernandez University Elche Spain

In this poster, the authors describe original work of a developed real time multicast approach for distributed multimedia systems. the poster focuses especially ongoing research work on several aspects of distributed ... 详细信息

关键词： Real-time systems IPTV Multimedia communication Protocols Context

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