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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

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parallel Programming and Optimization Based on TMS320C6678

Parallel Programming and Optimization Based on TMS320C6678

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3rd international conference on Automatic Control and Mechatronic Engineering (ICACME)

作者： Mou, Xin-gang Wei, Guo-hua Zhou, Xiao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

ISBN: (纸本)9783038351993

The development of multi-core processors has provided a good solution to applications that require real-time processing and a large number of calculations. However, simply exploiting parallelism in software is hard to make full use of the hardware performance. This paper studies the parallel programming and optimization techniques on TMS320C6678 multicore digital signal processors. We firstly illustrate an implementation of a selected parallel image convolution algorithm by OpenMP. Then several optimization techniques such as compiler intrinsics, cache, DMA are used to further enhance the application performance and achieve a good execution time according to the test results.

关键词： parallel processing OpenMP TMS320C6678 image convolution optimization compiler intrinsics DMA

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Continuous queries over distributed streams of heterogeneous monitoring data in cloud datacenters 9

Continuous queries over distributed streams of heterogeneous...

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9th international conference on Software Engineering and applications, ICSOFT-EA 2014

作者： Tovarňák, Daniel Pitner, Tomáš Masaryk University Faculty of Informatics Botanická 68a Brno60200 Czech Republic

ISBN: (纸本)9789897580369

The use of stream processing for state monitoring of distributed infrastructures has been advocated by some in order to overcome the issues of traditional monitoring solutions when tasked with complex continuous queries. However, in the domain of behavior monitoring the situation gets more complicated. It is mainly because of the low-quality source of behavior-related monitoring information (natural language computer logs). Existing approaches prevalently rely on indexing and real-time data-mining of the behavior-related data rather than on using event/stream processing techniques and the many corresponding benefits. The goal of this paper is to present a general notion of distributed Event-Driven Monitoring Architecture that will enable an easy definition of expressive continuous queries over many distributed and heterogeneous streams of behavior-related (and state-related) monitoring data.

关键词： Monitoring

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Incremental, distributed Single-Linkage Hierarchical Clustering Algorithm Using MapReduce 15

Incremental, Distributed Single-Linkage Hierarchical Cluster...

引用

Simulation Multi-conference

作者： Chen Jin Zhengzhang Chen William Hendrix Ankit Agrawal Alok Choudhary Northwestern University Evanston IL

ISBN: (纸本)9781510801011

Single-linkage hierarchical clustering is one of the prominent and widely-used data mining techniques for its informative representation of clustering results. However, the parallelization of this algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. Moreover, in many modern applications, new data is continuously added into the already huge datasets. It would be impractical to reapply the clustering algorithm on the augmented datasets from scratch. In this paper, we propose a unified algorithm which can not only cluster the large dataset, but also incorporate the newly arrived data incrementally. More specifically, we formulate the single-linkage hierarchical clustering problem as a Minimum Spanning Tree (MST) construction problem on a complete graph. The algorithm decomposes the complete graph into a set of non-overlapped subgraphs, computes the intermediate sub-MSTs for each subgraph in parallel, and merges all the sub-MSTs to achieve the final solution. In addition, the same framework can treat the incremental data insertion as a separate data subset and integrate it nicely with the existing solution. We implement the unified algorithm by employing MapReduce framework. Using both synthetic and real-world datasets containing up to millions of high-dimensional points, we show that the proposed algorithm achieves a scalable speedup up to 200 on 300 computer cores for the base dataset and a speedup up to 120 for the dataset with maximum 5% random insertion.

关键词： Hierarchical Clustering Incremental Hierarchical Clustering MapReduce

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distributed complex event processing for mobile large-scale video applications 14

Distributed complex event processing for mobile large-scale ...

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2014 international conference on Middleware, Middleware 2014

作者： Ottenwälder, Beate Mayer, Ruben Koldehofe, Boris Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781450332200

A recent trend in communication networks - sometimes referred to as fog computing - offers to execute computational tasks close to the access points of the networks. This enables mobile Complex Event processing (CEP) middlewares to significantly reduce end-to-end latencies and bandwidth usage by migrating operators when event sources and consumers change their location. This demonstration shows the impact of proactive migration strategies on the perceived end user quality in the context of a video monitoring application. Copyright is held by the owner/author(s).

关键词： Fog computing

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Improving I/O Performance with Adaptive Data Compression for Big Data applications 28

Improving I/O Performance with Adaptive Data Compression for...

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28th IEEE international parallel & distributed processing Symposium Workshops (IPDPSW)

作者： Zou, Hongbo Yu, Yongen Tang, Wei Chen, Hsuanwei Michelle Georgia Inst Technol Atlanta GA 30318 USA IIT Chicago IL 60616 USA Argonne Natl Lab Argonne IL 60439 USA San Jose State Univ San Jose CA 95192 USA

ISBN: (纸本)9781479941162

Increasingly larger scale simulations are generating an unprecedented amount of data. However, the increasing gap between computation and I/O capacity on High End Computing machines makes a severe bottleneck for data analysis. As a solution, in-situ analytics processes output data while simulations are running and before placing data on disk. Data movement between simulation and analytics, however, incurs overheads of in-situ analytics at scale. This paper tries to answer the following question: can we use compression technology to reduce the data movement cost and improve the performance of in-situ analytics for peta-scale applications? In particular, we explore when, where, how to use the compression techniques to reduce data movement cost between simulation and analytics. To find out the best algorithm and place to compress data in given situation, we introduce an adaptive data compression algorithm in this paper. The adaptive compression service is developed and analyzed for the in-situ analytics middleware. Experimental results demonstrate that compression service increases data transition bandwidth and improve the application End-to-End transfer performance.

关键词： I/O Bottlenecks In-situ Analytics Compression Big Data High-end Computing

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Adaptive Heterogeneous Scheduling for Integrated GPUs 14

Adaptive Heterogeneous Scheduling for Integrated GPUs

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23rd international conference on parallel Architectures and Compilation techniques (PACT)

作者： Kaleem, Rashid Barik, Rajkishore Shpeisman, Tatiana Lewis, Brian T. Hu, Chunling Pingali, Keshav Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Intel Labs Santa Clara CA USA

ISBN: (纸本)9781450328098

Many processors today integrate a CPU and GPU on the same die, which allows them to share resources like physical memory and lowers the cost of CPU-GPU communication. As a consequence, programmers can effectively utilize both the CPU and GPU to execute a single application. This paper presents novel adaptive scheduling techniques for integrated CPU-GPU processors. We present two online profiling-based scheduling algorithms: naiive and asymmetric. Our asymmetric scheduling algorithm uses low-overhead online profiling to automatically partition the work of data-parallel kernels between the CPU and GPU without input from application developers. It does profiling on the CPU and GPU in a way that doesn't penalize GPU-centric workloads that run significantly faster on the GPU. It adapts to application characteristics by addressing: 1) load imbalance via irregularity caused by, e.g., data-dependent control flow, 2) different amounts of work on each kernel call, and 3) multiple kernels with different characteristics. Unlike many existing approaches primarily targeting NVIDIA discrete GPUs, our scheduling algorithm does not require offline processing. We evaluate our asymmetric scheduling algorithm on a desktop system with an Intel 4th Generation Core Processor using a set of sixteen regular and irregular workloads from diverse application areas. On average, our asymmetric scheduling algorithm performs within 3.2% of the maximum throughput with a CPU-and-GPU oracle that always chooses the best work partitioning between the CPU and GPU. These results underscore the feasibility of online profile-based heterogeneous scheduling on integrated CPU-GPU processors.

关键词： Heterogeneous computing integrated GPUs scheduling load balancing Irregular applications

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Prototyping Framework for parallel Numerical Computations 1

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10th international conference on parallel processing and Applied Mathematics (PPAM)

作者： Meca, Ondrej Boehm, Stanislav Behalek, Marek Surkovsky, Martin FEI VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic

ISBN: (数字)9783642551956

ISBN: (纸本)9783642551956

Our research is focused on the simplification of parallel programming for distributed memory systems. Our goal is to build a unifying framework for creating, debugging, profiling, and verifying parallel applications. The result of this effort is an open source tool Kaira. In this paper, we focus on prototyping of parallel applications. We have extended Kaira by the ability to generate parallel libraries. More precisely, we present a framework for fast prototyping of parallel numerical computations. We demonstrate our idea on a combination of parallel libraries generated by our tool Kaira and GNU Octave. Hence, a user can verify the idea in a short time, create a real running program and verify its performance and scalability.

关键词： Prototyping parallel computing Visual programming Libraries

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High-Performance Inverse Modeling with Reverse Monte Carlo Simulations 43

High-Performance Inverse Modeling with Reverse Monte Carlo S...

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43rd Annual international conference on parallel processing (ICPP)

作者： Sarje, Abhinav Li, Xiaoye S. Hexemer, Alexander Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Adv Light Souce Berkeley CA 94720 USA

ISBN: (纸本)9781479956180

In the field of nanoparticle material science, Xray scattering techniques are widely used for characterization of macromolecules and particle systems (ordered, partially-ordered or custom) based on their structural properties at the micro-and nano-scales. Numerous applications utilize these, including design and fabrication of energy-relevant nanodevices such as photovoltaic and energy storage devices. Due to its size, analysis of raw data obtained through present ultrafast light beamlines and X-ray scattering detectors has been a primary bottleneck in such characterization processes. To address this hurdle, we are developing high-performance parallel algorithms and codes for analysis of X-ray scattering data for several of the scattering methods, such as the Small Angle X-ray Scattering (SAXS), which we talk about in this paper. As an inverse modeling problem, structural fitting of the raw data obtained through SAXS experiments is a method used for extracting meaningful information on the structural properties of materials. Such fitting processes involve a large number of variable parameters and, hence, require a large amount of computational power. In this paper, we focus on this problem and present a high-performance and scalable parallel solution based on the Reverse Monte Carlo simulation algorithm, on highly-parallel systems such as clusters of multicore CPUs and graphics processors. We have implemented and optimized our algorithm on generic multi-core CPUs as well as the Nvidia GPU architectures with C++ and CUDA. We also present detailed performance results and computational analysis of our code.

关键词： C++ language Monte Carlo methods X-ray scattering graphics processing units macromolecules nanoparticles parallel algorithms parallel architectures C++ Nvidia GPU architectures SAXS X-ray scattering detectors X-ray scattering techniques energy storage devices energy-relevant nanodevice fabrication graphics processors high-performance inverse modeling high-performance parallel algorithms inverse modeling problem macromolecule characterization material structural properties multicore CPUs nanoparticle material science reverse Monte Carlo simulation algorithm small angle X-ray scattering structural fitting structural properties Computational modeling Data models Kernel Materials Program processors Scattering

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processing Probabilistic K-Nearest Neighbor Query Using R^lsd-tree

Processing Probabilistic <i>K</i>-Nearest Neighbor Query Usi...

引用

28th IEEE international conference on Advanced Information Networking and applications Workshops (IEEE WAINA)

作者： Huang, Yuan-Ko Lin, Lien-Fa Kao Yuan Univ Dept Informat Commun Kaohsiung Country Taiwan

ISBN: (纸本)9781479936298

One of the important queries in spatio-temporal databases is the probabilistic K-nearest neighbor query (or PKNNQ for short). Given a query object q, a set S-mo of moving objects with uncertain moving speed and direction, a time point t(q), and a value of K, a PKNNQ finds the objects with highest probability of being the K-nearest neighbor of q at time t(q). Most of the existing approaches focus on designing an index structure for managing moving objects so as to facilitate processing the PKNNQ. However, they do not properly consider the moving speeds and directions of objects to build the index, and thus lead to poor query performance. In this paper, we use an efficient R-lsd-tree, which is built by applying space-filling curves to determine which objects are better to be grouped, to index moving objects. We develop pruning criteria combined with the R-lsd-tree to efficiently answer the PKNNQ. In addition, a probability model is designed to reasonably quantify the possibility of each object being the query result. Finally, a thorough experimental evaluation is conducted to show the merits of the proposed techniques.

关键词： spatio-temporal databases probabilistic K-nearest neighbor query PKNNQ R-lsd-tree probability model

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Time Series Join on Subsequence Correlation 14

Time Series Join on Subsequence Correlation

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14th IEEE international conference on Data Mining (IEEE ICDM)

作者： Mueen, Abdullah Hamooni, Hossein Estrada, Trilce Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA

ISBN: (纸本)9781479943036

We consider the problem of joining two long time series based on their most correlated segments. Two time series can be joined at any locations and for arbitrary length. Such join locations and length provide useful knowledge about the synchrony of the two time series and have applications in many domains including environmental monitoring, patient monitoring and power monitoring. However, join on correlation is a computationally expensive task, specially when the time series are large. The naive algorithm requires O(n(4)) computation where n is the length of the time series. We propose an algorithm, named Jocor, that uses two algorithmic techniques to tackle the complexity. First, the algorithm reuses the computation by caching sufficient statistics and second, the algorithm prunes unnecessary correlation computation by admissible heuristics. The algorithm runs orders of magnitude faster than the naive algorithm and enables us to join long time series as well as many small time series. We propose a variant of Jocor for fast approximation and an extension to a GPU-based parallel method to bring down the running-time to interactive level for analytics applications. We show three independent uses of time series join on correlation which are made possible by our algorithm.

关键词： approximation theory graphics processing units mathematics computing parallel processing statistical analysis time series GPU-based parallel method Jocor approximation arbitrary length environmental monitoring naive algorithm patient monitoring power monitoring statistics subsequence correlation time series joining Computer science Correlation Educational institutions Equations Euclidean distance Standards Time series analysis Alignment Matching Similarity Time Series Time series mathematics computing Patient Monitoring Educational institutions computer science environmental monitoring Approximation Theory time series analysis parallel processing (COMPUTERS) Euclidean distance approximation

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