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INV-ASKIT: A parallel Fast Direct Solver for Kernel Matrices 30

INV-ASKIT: A Parallel Fast Direct Solver for Kernel Matrices

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Yu, Chenhan D. March, William B. Xiao, Bo Biros, George Univ Texas Austin Inst Computat Engn & Sci Dept Comp Sci Austin TX 78712 USA Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA

ISBN: (纸本)9781509021406

We present a parallel algorithm for computing the approximate factorization of an N-by-N kernel matrix. Once this factorization has been constructed (with N log(2) N work), we can solve linear systems with this matrix with N logN work. Kernel matrices represent pairwise interactions of points in metric spaces. they appear in machine learning, approximation theory, and computational physics. Kernel matrices are typically dense (matrix multiplication scales quadratically with N) and ill-conditioned (solves can require 100s of Krylov iterations). thus, fast algorithms for matrix multiplication and factorization are critical for scalability. Recently we introduced ASKIT, a new method, which resembles N-body methods, for approximating a kernel matrix. Here we introduce INV-ASKIT, a factorization scheme based on ASKIT. We describe the new method, derive complexity estimates, and conduct an empirical study of its accuracy and scalability. We report results on real-world datasets including "COVTYPE" (0.5M points in 54 dimensions), "SUSY" (4.5M points in 8 dimensions) and "MNIST" (2M points in 784 dimensions) using shared and distributed memory parallelism. In our largest run we approximately factorize a dense matrix of size 32M x 32M (generated from points in 64 dimensions) on 4,096 Sandy-Bridge cores. To our knowledge these results improve the state of the art by several orders of magnitude.

关键词： Kernel methods Linear solvers Machine leadning parallel algorithms Treecodes

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An Efficient Implementation of Fuzzy C-Means and Watershed Algorithms for MRI Segmentation 8

An Efficient Implementation of Fuzzy C-Means and Watershed A...

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8th International symposium on Telecommunications (IST)

作者： Hoseini, Farnaz Shahbahrami, Asadollah Islamic Azad Univ Rasht Branch Fac Engn Rasht Iran Univ Guilan Fac Engn Dept Comp Engn Rasht Iran

ISBN: (纸本)9781509034352

Image segmentation is one of the most common steps in digital image processing. It classifies a digital image into different segments. there are many algorithms for image segmentation such as thresholding, edge detection, and region growing, which finding a suitable algorithm for medical image segmentation is a challenging task. this is due to noise, low contrast, and steep light variations of medical images. the main goal of this paper is improving the performance of fuzzy c-means clustering. Improving is achieved using parallel implementation of this algorithm. Fuzzy c-means clustering is an important iterative clustering algorithm, but it is computationally intensive and it uses the same data between the iterations. the center of the clusters changes in each iteration, which requires considerable amount of time for large data sets. the parallel fuzzy c-means clustering is implemented by using task pipeline concept in CUDA technology. the experimental results show that the performance is improved up to 23.35x. After that watershed algorithm is applied for the final segmentation. the implementation results show that the accuracy of diagnosis in magnetic resonance imaging 97/33% is improved. this improvement is achieved using enhancing edges and reducing noises in images.

关键词： parallel Fuzzy C-Means Clustering Watershed Algorithm parallel Computing CUDA GPU

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A parallel Frequent Item Counting Algorithm 8

A Parallel Frequent Item Counting Algorithm

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8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Yang, Xun Liu, Jun Zhou, Wenli Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst Architecture & Conve Beijing Peoples R China Beijing Univ Posts & Telecommun Ctr Data Sci Beijing Peoples R China HAOHAN Data Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781509007684

Frequent items in high-speed streaming data are important to many applications like network monitoring and anomaly detecting. To deal with high arrival rate of streaming data, it is desirable that such systems be capable of supporting high processing throughput with tight guarantees on errors. In this paper, we address the problem of finding frequent and top-k items, and present a parallel version of the Space Saving algorithm in the context of the open source distributed computing system. Based on the theoretical analysis, the errors are restrictively bounded in our algorithm, and our parallel design could achieve high throughput. Taking advantage of the distributed computing resources, our evaluation reveals that such design delivers linear speedup with remarkable scalability.

关键词： frequent items parallel algorithms stream processing

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On the Development of Variable Size Batched Computation for Heterogeneous parallel Architectures 30

On the Development of Variable Size Batched Computation for ...

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30th ieee International parallel and distributed processing symposium (IPDPS)

作者： Abdelfattah, Ahmad Haidar, Azzam Tomov, Stanimire Dongarra, Jack Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA Univ Manchester Manchester Lancs England

ISBN: (纸本)9781509036820

Many scientific applications, ranging from national security to medical advances, require solving a number of relatively small-size independent problems. As the size of each individual problem does not provide sufficient parallelism for the underlying hardware, especially accelerators, these problems must be solved concurrently as a batch in order to saturate the hardware with enough work, hence the name batched computation. A possible simplification is to assume a uniform size for all problems. However, real applications do not necessarily satisfy such assumption. Consequently, an efficient solution for variable-size batched computations is required. this paper proposes a foundation for high performance variable-size batched matrix computation based on Graphics processing Units (GPUs). Being throughput-oriented processors, GPUs favor regular computation and less divergence among threads, in order to achieve high performance. therefore, the development of high performance numerical software for this kind of problems is challenging. As a case study, we developed efficient batched Cholesky factorization algorithms for relatively small matrices of different sizes. However, most of the strategies and the software developed, and in particular a set of variable size batched BLAS kernels, can be used in many other dense matrix factorizations, large scale sparse direct multifrontal solvers, and applications. We propose new interfaces and mechanisms to handle the irregular computation pattern on the GPU. According to the authors' knowledge, this is the first attempt to develop high performance software for this class of problems. Using a K40c GPU, our performance tests show speedups of up to 2.5x against two Sandy Bridge CPUs (8-core each) running Intel MKL library.

关键词： batched computation GPUs variable small sizes

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Exploring Controlled RDF Distribution 8

Exploring Controlled RDF Distribution

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8th ieee International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Penteado, Raqueline R. M. Schroeder, Rebeca Hara, Carmem S. Univ Estadual Maringa BR-87020900 Maringa Parana Brazil Univ Estado Santa Catarina BR-89219710 Joinville SC Brazil Univ Fed Parana BR-81531990 Curitiba Parana Brazil

ISBN: (纸本)9781509014453

RDF datasets have increased rapidly over the last few years. In order to process SPARQL queries on these large datasets, much effort has been spent on developing horizontally scalable techniques, which involve data partitioning and parallel query processing. While distribution may provide storage scalability, it may also incur high communication costs for processing queries. In this paper, we present a parallel and distributed query processing approach that explores the existence of data allocation patterns, provided by a controlled data distribution, that determine how RDF triples should be grouped and stored on the same server. Fragments of the RDF datastore follow a given allocation pattern and correspond also to units of communication among servers. Based on this distribution model, we define two communication strategies for query processing: get-frag, which requests remote servers to send fragments that contain data required by a query, and send-result, which forwards intermediate results. these strategies are combined on a method, called 2ways, that chooses the adequate communication strategy whenever queries traverse fragment boundaries. We provide a cost function used to determine this choice and present experimental results. they show that our proposed technique effectively reduces the communication cost and improves the response time for processing SPARQL queries on a distributed RDF datastore.

关键词： data distribution distributed query execution RDF SPARQL

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Operational Reliability Evaluation Method Based on Big Data Technology 8

Operational Reliability Evaluation Method Based on Big Data ...

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8th International Conference on Cyber-Enabled distributed Computing and Knowledge Discovery (CyberC)

作者： Hu, Lijuan Liu, Ke-yan Diao, Yinglong Meng, Xiaoli Sheng, Wanxing China Elect Power Res Inst Beijing Peoples R China

ISBN: (纸本)9781509051540

this paper focuses on using big data technology to solve the operational reliability evaluation problem in the power distribution system. Operational reliability evaluation means the capability to forecast the future reliability from the current system. this becomes a challenge problem as the power distribution network has been even more complex. Meanwhile, the safety and reliability are considered more and more critical for the network. therefore, it needs to new approach to evaluate the operational state of power distribution system. For such a purpose, this paper proposes an operational reliability evaluation method based on the big data of distribution network and big data processing technology. Based on the operational reliability index system of distribution network, this paper firstly analyzed the main influencing factors of each reliability indexes by using parallel association rules mining method. then, by using these factors as input variables, an evaluation model was established based on artificial neural network. Based on the evaluation model and real-time data, the operational reliability can be obtained.

关键词： operational reliability association rules mining parallel computing artificial neural network

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enerGyPU and enerGyPhi Monitor for Power Consumption and Performance Evaluation on Nvidia Tesla GPU and Intel Xeon Phi 16

enerGyPU and enerGyPhi Monitor for Power Consumption and Per...

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16th ieee/ACM International symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Garcia H, John A. Hernandez B, Esteban Montenegro, Carlos E. Navaux, Philippe O. Barrios H, Carlos J. Univ Ind Santander HIGH Performance & Sci Comp Ctr SC3 Bucaramanga Santander Colombia Univ Fed Rio Grande do Sul Parallel & Distributed Proc Grp GPPD BR-90046900 Porto Alegre RS Brazil Univ Dist Francisco Jose de Caldas UD Interoperabil Acad Networks Grp GIIRA Bogota Colombia

ISBN: (纸本)9781509024537

the evaluation of performance and power consumption is a key step in the design of applications for large computational systems as supercomputers and clusters (multicore and accelerator nodes, multicore and coprocessor nodes, manycore and accelerator nodes). In these systems the developers must design several experiments for workload characterization observing the architectural implications when using different combinations of computational resources such as number of GPU, number of cores for processing, number of cores for administration of GPU, number of MPI processes and thread affinity policy. It should also engage factors as the clock frequency and memory usage as well select the combination of computational resources that increases the performance and minimizes the power consumption. this research proposes an integrated energy-aware scheme called efficiently energetic acceleration (EEA) for large-scale scientific applications running on heterogeneous architectures. this paper shows the use of a monitoring tool with two components called enerGyPU and enerGyPhi to recording EEA control factors in runtime on two environments: one cluster with multicore and accelerator nodes (2-CPU/8-GPU) and one server with multiple cores and one coprocessor (2-CPU/1-MIC). these monitors allow to analyze multiple testing results under different parameter combinations to observe the EEA control factors that determine the energy efficiency.

关键词： Energy efficiency Energy-aware EEA scheme enerGyPU enerGyPhi Power capping technique Performance evaluation

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A Java-Based Real-Time Reactive Stream Framework

A Java-Based Real-Time Reactive Stream Framework

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International symposium on Object-Oriented Real-Time distributed Computing

作者： HaiTao Mei Ian Gray Andy Wellings University of York UK

ISBN: (纸本)9781467390330

this paper presents a framework for real-time reactive stream processing. the approach is to extend the proposed Java 9 Reactive Streams model and integrate it with the Real-Time Specification for Java. the approach leverages a real-time version of the Java 8 Stream processing framework. Our approach addresses the major issue when using Reactive Streams in real-time: there is no way to set the timeout. Our evaluation shows there is significant improvement in the predictability of stream processing with our framework over that of one implemented using regular Java.

关键词： Java Real-time systems Instruction sets Data models Standards parallel processing

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Query Optimization of distributed Database Based on parallel Genetic Algorithm and Max-Min Ant System 8

Query Optimization of Distributed Database Based on Parallel...

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8th International symposium on Computational Intelligence and Design (ISCID)

作者： Ban, Wenjiao Lin, Jiming Tong, Jichao Li, Shiwen Guilin Univ Elect Technol Coll Informat & Commun Guilin 541004 Peoples R China

ISBN: (纸本)9781467395878

Since the era of big data is coming, the first important problem is how to enhance the speed of database query. For the query optimization of distributed database, the speed of query depends on the data transfer and order of join. the cost model minimizing communication cost is the emphasis of research. parallel Genetic Algorithm-Max-Min Ant System was proposed to seek a best query execution plan, which combines faster convergence of Genetic Algorithm, globally search ability of Max-Min Ant System and parallel property of both them. the experiment results show that the proposed algorithm is effective for query processing of multi-join, and plays important role in improving the performance of distributed database.

关键词： distributed database Genetic Algorithm Max-Min Ant System query execution plan parallel

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Decentralized Runtime Verification of LTL Specifications in distributed Systems 29

Decentralized Runtime Verification of LTL Specifications in ...

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29th ieee International parallel and distributed processing symposium (IPDPS)

作者： Mostafa, Menna Bonakdarpour, Borzoo Univ Waterloo Sch Comp Sci Waterloo ON N2L 3G1 Canada McMaster Univ Dept Comp & Software Hamilton ON L8S 4L8 Canada

ISBN: (纸本)9781479986484

Runtime verification is a lightweight automated formal method for specification-based runtime monitoring as well as testing of large real-world systems. While numerous techniques exist for runtime verification of sequential programs, there has been very little work on specification-based monitoring of distributed systems. In this paper, we propose the first sound and complete method for runtime verification of asynchronous distributed programs for the 3-valued semantics of LTL specifications defined over the global state of the program. Our technique for evaluating LTL properties is inspired by distributed computation slicing, an approach for abstracting distributed computations with respect to a given predicate. Our monitoring technique is fully decentralized in that each process in the distributed program under inspection maintains a replica of the monitor automaton. Each monitor may maintain a set of possible verification verdicts based upon existence of concurrent events. Our experiments on runtime monitoring of a simulated swarm of flying drones show that due to the design of our Algorithm, monitoring overhead grows only in the linear order of the number of processes and events that need to be monitored.

关键词： Runtime monitoring Asynchronous distributed systems Formal methods

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