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Consistency and fault tolerance for erasure-coded distributed storage systems

Consistency and fault tolerance for erasure-coded distribute...

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5th international Workshop on Data-Intensive distributed computing, DIDC'12

作者： Peter, Kathrin Reinefeld, Alexander Humboldt-Universität zu Berlin Zuse-Institut Berlin Germany

ISBN: (纸本)9781450313414

One challenge in applying erasure codes (or error-correcting codes) to distributed storage systems is to maintain consistency between data and redundancy blocks in the face of crashing servers. We present two access protocols that provide sequential consistency and maximum distance separable fault tolerance at the same time. the protocols use sequence numbers to recover a consistent version in the presence of failures or partial writes. the first (pessimistic) PSW protocol uses a master per stripe to execute updates in sequence. the second (optimistic) OCW protocol allows concurrent writes to blocks in the same stripe to happen in parallel at the cost of additional buffer space. We present empirical performance results for PSW and OCW and compare them to other protocols. Our results show that OCW is as fast as simple replication while providing better fault tolerance and/or reduced storage overhead. this demonstrates that erasure coding can be used as a space-efficient alternative to replication in distributed storage systems. Copyright 2012 ACM.

关键词： Fault tolerance

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Auto-tuning Mapping Strategy for parallel CFD Program

Auto-tuning Mapping Strategy for Parallel CFD Program

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5th international symposium on Computational Intelligence and Design (ISCID)

作者： Liu Fang Wang Zhenghua Che Yonggang Natl Univ Def & Technol Sch Comp Changsha Hunan Peoples R China

ISBN: (纸本)9780769548111;9781467326469

We present an auto-tuning mapping strategy for mapping grid blocks to multi-processors and multi-nodes in a parallel CFD program. We first calculate the communication matrices from the topology construction of the grids, then use two heuristics in tuning the possible partitions, which largely shrinks the searching space and obtains the optimal mapping under these searching constraints, making out a compromise between tuning cost and system performance. Experiments are carried out for a CFD calculation case on a high performance computing platform. Compared with general block mapping, cycle mapping and random mapping strategies, our strategy has an extraordinary advantage over the others in load balance and communication overhead.

关键词： auto-tuning mapping strategy heuristic

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Research of Flexible Deformation Algorithm Based on Collaborative Architecture of CPU and GPU

Research of Flexible Deformation Algorithm Based on Collabor...

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5th international symposium on Computational Intelligence and Design (ISCID)

作者： Yan, Shumin Yang, Guang Chou, Wusheng Liu, Yong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Inst Robot Beijing Peoples R China

ISBN: (纸本)9780769548111

In virtual reality, creating suitable deformation / force model and accelerating the efficiency of data processing are effective means to real-time response of flexible deformation. A processing algorithm of flexible deformation using GPU's massively parallel computing power and collaborative architecture of CPU and GPU based on the mechanical model of the spring-mass is presented. As a result of pretreatment on the collision detection algorithm for optimization, solving the problem of time-consuming when large amounts of data transferring between CPU and GPU. this algorithm improves the efficiency and real-time performance of the flexible deformation simulation, which is proved by experiments.

关键词： deformation of flexible Bodies GPU Communication streaming computing model

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HiFlow3: A hardware-aware parallel finite element package 1

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5th international Workshop on parallel Tools for High Performance computing, HPC 2011

作者： Anzt, H. Augustin, W. Baumann, M. Gengenbach, T. Hahn, T. Helfrich-Schkarbanenko, A. Heuveline, V. Ketelaer, E. Lukarski, D. Nestler, A. Ritterbusch, S. Ronnas, S. Schick, M. Schmidtobreick, M. Subramanian, C. Weiss, J.-P. Wilhelm, F. Wlotzka, M. Karlsruhe Germany

ISBN: (数字)9783642314766

ISBN: (纸本)9783642314759

the goal of this paper is to describe the hardware-aware parallel C++ finite element package HiFlow3. HiFlow3 aims at providing a powerful platform for simulating processes modelled by partial differential equations. Our vision is to solve boundary value problems in an appropriate way by coupling numerical simulations with modern software design and state-of-the-art hardware technologies. the main functionalities for mapping the mathematical model into parallel software are implemented in the three core modules Mesh, DoF/FEM and Linear Algebra (LA). parallelism is realized on two levels. the modules provide efficient MPI-based distributed data structures to achieve performance on large HPC systems but also on stand-alone workstations. Additionally, the hardwareaware cross-platform approach in the LA module accelerates the solution process by exploiting the computing power from emerging technologies like multi-core CPUs and GPUs. In this context performance evaluation on different hardwarearchitectures will be demonstrated. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Linear algebra

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Fast and Efficient Graph Traversal Algorithm for CPUs: Maximizing Single-Node Efficiency

Fast and Efficient Graph Traversal Algorithm for CPUs: Maxim...

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26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Chhugani, Jatin Satish, Nadathur Kim, Changkyu Sewall, Jason Dubey, Pradeep Intel Corp Parallel Comp Lab Santa Clara CA 95051 USA

ISBN: (纸本)9780769546759

Graph-based structures are being increasingly used to model data and relations among data in a number of fields. Graph-based databases are becoming more popular as a means to better represent such data. Graph traversal is a key component in graph algorithms such as reachability and graph matching. Since the scale of data stored and queried in these databases is increasing, it is important to obtain high performing implementations of graph traversal that can efficiently utilize the processing power of modern processors. In this work, we present a scalable Breadth-First Search Traversal algorithm for modern multi-socket, multi-core CPUs. Our algorithm uses lock- and atomic-free operations on a cache-resident structure for arbitrary sized graphs to filter out expensive main memory accesses, and completely and efficiently utilizes all available bandwidth resources. We propose a work distribution approach for multi-socket platforms that ensures load-balancing while keeping cross-socket communication low. We provide a detailed analytical model that accurately projects the performance of our single- and multi-socket traversal algorithms to within 5-10% of obtained performance. Our analytical model serves as a useful tool to analyze performance bottlenecks on modern CPUs. When measured on various synthetic and real-world graphs with a wide range of graph sizes, vertex degrees and graph diameters, our implementation on a dual-socket Intel (R) Xeon (R) X5570 (Intel microarchitecture code name Nehalem) system achieves 1.5X-13.2X performance speedup over the best reported numbers. We achieve around 1 Billion traversed edges per second on a scale-free R-MAT graph with 64M vertices and 2 Billion edges on a dual-socket Nehalem system. Our optimized algorithm is useful as a building block for efficient multi-node implementations and future exascale systems, thereby allowing them to ride the trend of increasing per-node compute and bandwidth resources.

关键词： bandwidth efficient Graph traversal multi-socket single node

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High Performance Non-uniform FFT on Modern x86-based Multi-core Systems

High Performance Non-uniform FFT on Modern x86-based Multi-c...

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26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Kalamkar, Dhiraj D. Trzasko, Joshua D. Sridharan, Srinivas Smelyanskiy, Mikhail Kim, Daehyun Manduca, Armando Shu, Yunhong Bernstein, Matt A. Kaul, Bharat Dubey, Pradeep Intel Labs Parallel Comp Lab Bangalore Karnataka India Intel Labs Parallel Comp Lab Santa Clara CA 95052 USA Mayo Clin Dept Physiol & Biomed Engn Rochester MN 55905 USA Mayo Clin Dept Radiol Rochester MN 55905 USA

ISBN: (纸本)9780769546759

the Non-Uniform Fast Fourier Transform (NUFFT) is a generalization of FFT to non-equidistant samples. It has many applications which vary from medical imaging to radio astronomy to the numerical solution of partial differential equations. Despite recent advances in speeding up NUFFT on various platforms, its practical applications are still limited, due to its high computational cost, which is significantly dominated by the convolution of a signal between a non-uniform and uniform grids. the computational cost of the NUFFT is particularly detrimental in cases which require fast reconstruction times, such as iterative 3D non-Cartesian MRI reconstruction. We propose novel and highly scalable parallel algorithm for performing NUFFT on x86-based multi-core CPUs. the high performance of our algorithm relies on good SIMD utilization and high parallel efficiency. On convolution, we demonstrate on average 90% SIMD efficiency using SSE, as well up to linear scalability using a quad-socket 40-core Intel (R) Xeon (R) E7-4870 Processors based system. As a result, on dual socket Intel (R) Xeon (R) X5670 based server, our NUFFT implementation is more than 4x faster compared to the best available NUFFT3D implementation, when run on the same hardware. On Intel (R) Xeon (R) E5-2670 processor based server, our NUFFT implementation is 1.5X faster than any published NUFFT implementation today. Such speed improvement opens new usages for NUFFT. For example, iterative multichannel reconstruction of a 240x240x240 image could execute in just over 3 minutes, which is on the same order as contemporary non-iterative (and thus less-accurate) 3D NUFFT-based MRI reconstructions.

关键词： Non-uniform FFT parallelization Scalability Vectorization

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distributed Multimedia Content Analysis with MapReduce

Distributed Multimedia Content Analysis with MapReduce

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IEEE international symposium on Personal, Indoor and Mobile Radio Communications

作者： Arto Heikkinen Jouni Sarvanko Mika Rautiainen Mika Ylianttila Department of Communications Engineering University of Oulu Finland Center for Internet Excellence University of Oulu Finland Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9781467362337

this paper introduces a scalable solution for distributing content-based video analysis tasks using the emerging MapReduce programming model. Scalable and efficient solutions are needed for this type of tasks, as the number of multimedia content is growing at an increasing rate. We present a novel implementation utilizing the popular Apache Hadoop MapReduce framework for both analysis job scheduling and video data distribution. We employ face detection as a case example because it represents a popular visual content analysis task. the main contribution of this paper is the performance evaluation of distribution models for video content processing in various configurations. In our experiments, we have compared the performance of our video data distribution method against two alternatives solutions on a seven node cluster. Hadoop's performance overhead in video content analysis was also evaluated. We found Hadoop to be a data efficient solution with minimal computational overhead for the face detection task.

关键词： MapReduce distributed video analysis Face detection parallel computing

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Topology-aware deployment of scientific applications in cloud computing

Topology-aware deployment of scientific applications in clou...

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2012 IEEE 5th international Conference on Cloud computing, CLOUD 2012

作者： Fan, Pei Chen, Zhenbang Wang, Ji Zheng, Zibin Lyu, Michael R. National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Dept. of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9780769547558

Nowadays, more and more scientific applications are moving to cloud computing. the optimal deployment of scientific applications is critical for providing good services to users. Scientific applications are usually topology-aware applications. therefore, considering the topology of a scientific application during the development will benefit the performance of the application. However, it is challenging to automatically discover and make use of the communication pattern of a scientific application while deploying the application on cloud. To attack this challenge, in this paper, we propose a framework to discover the communication topology of a scientific application by pre-execution and multi-scale graph clustering, based on which the deployment can be optimized. Comprehensive experiments are conducted by employing a well-known MPI benchmark and comparing the performance of our method with those of other methods. the experimental results show the effectiveness of our topology-aware deployment method. © 2012 IEEE.

关键词： Cloud computing

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parallel Clustering Based on Partitions of Local Minimal-Spanning-Trees

Parallel Clustering Based on Partitions of Local Minimal-Spa...

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5th international symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Tsui, Shiau-Rung Wang, Wei-Jen Chen, Shi-Shan Chen, Lee Shu-Teng Wang, Chilung Natl Cent Univ Dept Comp Sci & Informat Engn Jhongli Taiwan Ind Technol Res Inst Clouding Comp Ctr Mobile Applicat Hsinchu Taiwan Natl Cent Univ Software Res Ctr Taoyuan Taiwan

ISBN: (纸本)9780769548982;9781467345668

Many traditional clustering algorithms have the scalability problem while dealing with large data sets. One common strategy to handle the problem is to parallelize the algorithms and execute them along with the input data on high-performance computers. However, many graph-based clustering algorithms are hard to be parallelized since they need to calculate the similarity of all-pairs of all data nodes. In this paper, we propose a new parallel clustering algorithm, called the Para-CPLM (parallel Clustering based on Partitions of Local Minimal-spanning-trees), which is based on three strategies - graph-based clustering, granular computing, and partition-and-merge. the Para-CPLM partitions the data domain into several regions for parallel execution, and then establishes a local minimal spanning tree in each region. After being established, the Para-CPLM combines those local minimal spanning trees and applies a method, namely the GBC method, to determine the best number of clusters. After the first phase of clustering, it repeatedly finds better pairs (edges) of the inter-clusters to reform the merged tree structure, such that the tree becomes closer to a global minimal spanning tree. Consequently, it uses the GBC method again to find the best number of clusters. From our experimental results, the Para-CPLM achieves significantly shorter execution time and better scalability while compared with the sequential GBC method. In addition, the clustering results are almost identical to those produced by the sequential GBC method.

关键词： clustering parallel computing graph-based clustering

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A flexible implementation method of distributed ANN

A flexible implementation method of distributed ANN

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5th international Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012

作者： Pi, Yuzhen Yuan, Quande Meng, Xiangping School of Electrical Engineering and Information Technology Changchun Institute of Technology Changchun 130012 China Research Center of Power Distribution Automation of Jilin Province Changchun 130012 China

ISBN: (纸本)9783642315602

the implementation methods of Artificial Neural Network (ANN) can be classified into two types: hardware implementation methods and software implementation methods. the former can build truly distributed, parallel, high-speed ANN, but it is complicated and expensive. the latter can build ANNs on classical computer using software technology. It's necessary in some cases, although the performance of ANNs built by this type of methods is limited because they are not parallel computing. In this paper, we propose a distributed implementation method based on multi-agent theory. Neurons in ANN are distributed, and the ANN can be easily to extend. © 2012 Springer-Verlag.

关键词： Neural networks

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