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A parallel query engine for interactive spatiotemporal analysis 14

A parallel query engine for interactive spatiotemporal analy...

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22nd ACM SIGSPATIAL international Conference on Advances in Geographic Information systems, ACM SIGSPATIAL GIS 2014

作者： Sathe, Mihir Knoblock, Craig A. Chiang, Yao-Yi Harris, Aaron Dept. of Computer Science Univ. of Southern California United States Information Sciences Institute Univ. of Southern California United States Spatial Sciences Institute Univ. of Southern California United States Dept. of Mech. Engineering Univ. of Southern California United States

ISBN: (纸本)9781450331319

Given the increasing popularity and availability of location tracking devices, large quantities of spatiotemporal data are available from many different sources. Quick interactive analysis of such data is important in order to understand the data, identify patterns, and eventually make a marketable product. Since the data do not necessarily follow the relational model and may require flexible processing possibly using advanced machine learning techniques, spatial databases or similar query tools do not make the best means for such analysis. Moreover, the high complexity of geometric operations makes the quick interactive analysis very difficult. In this paper, we present a highly flexible functional query engine that 1) works with multiple schema types, 2) provides fast response times by spatiotemporal indexing and parallelization, 3) helps understand the data using visualizations and 4) is highly extensible to easily add complex functionality. To demonstrate its usefulness, we use our tool to solve a real world problem of crime pattern analysis in Los Angeles County and compare the process with other well known tools. Copyright 2014 ACM.

关键词： Visualization

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Development of a marked structure for traces of parallel and distributed systems

Development of a marked structure for traces of parallel and...

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47th Annual Simulation symposium, ANSS 2014, Part of the 2014 Summer Simulation Multiconference, SummerSim 2014

作者： Da Silva, Diogo T. Manacero, Aleardo Lobato, Renata S. Menezes, Denison Spolon, Roberta Computer Science and Statistics Dept. Paulista State University - UNESP Rio Preto Brazil

ISBN: (纸本)9781632662132

The evaluation of high-performance systems, including grids, depends strongly of the workload applied during benchmarks or simulations. This is more evident with simulations, where the workloads may be created either by random loads or trace files. Although several models for generating random workloads have been proposed, trace files are the only form to assure reproducible simulations. Unfortunately there are very few trace files available in workload databases, and most of them have not been well maintained. Other problems include missing data fields and a structure for the data that is not easy to read and collect. Here we present a framework that allows the creation of trace files in which data is marked through XML tags, making easy their reading, and also provides front-end converters for some of the trace patterns found in the literature. It also can be used to collect traces from grid simulations performed in iSPD, a grid simulator based on iconic modeling, allowing for the reuse of the simulated workload and the filling of missing data. We present results with an implementation of this framework for iSPD, where we achieved smaller trace files and computing costs, even with the addition of markups. These results indicate that this approach could create a stronger pattern for workload trace files.

关键词： Benchmarking

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MTracer: A Trace-Oriented Monitoring Framework for Medium-Scale distributed systems

MTracer: A Trace-Oriented Monitoring Framework for Medium-Sc...

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2014 IEEE 8th international symposium on Service Oriented System Engineering

作者： Jingwen Zhou Zhenbang Chen Haibo Mi Ji Wang Science and Technology on Parallel and Distributed Processing Laboratory Changsha China

Trace-oriented runtime monitoring is a very effective method to improve the reliability of distributed systems. However, for medium-scale distributed systems, existing trace-oriented monitoring frameworks are either not powerful or efficient enough, or too complex and expensive to deploy and maintain. In this paper, we present MTracer, which is a lightweight trace-oriented monitoring system for medium-scale distributed systems. We have proposed and implemented several optimizations to improve the efficiency of the monitor server in MTracer. A web-based frontend is also provided to visualize a monitored system from different perspectives. We have validated MTracer in a real medium-scale environment. The results indicate that MTracer has a very lower overhead, and can handle more than 4000 events per second.

关键词： Monitoring Servers Optimization databases Runtime Data mining Reliability

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On the scheduling of fork-join parallel/distributed real-time tasks

On the scheduling of fork-join parallel/distributed real-tim...

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international symposium on Industrial Embedded systems

作者： Ricardo Garibay-Martínez Geoffrey Nelissen Luis Lino Ferreira Luís Miguel Pinho CISTER/INESC-TEC ISEP Polytechnic Institute of Porto Portugal

Modern real-time embedded applications present high computation requirements which need to be realized within strict time constraints. The current trend towards parallel processing in the embedded domain allows providing higher processing power. However, in some embedded applications, the use of powerful enough multi-core processors, may not be possible due to energy, space or cost constraints. A solution for this problem is to extend the parallel execution of the applications, allowing them to distribute their workload among networked nodes, on peak situations, to remote neighbour nodes in the system. In this context, we present the Partitioned-distributed-Deadline Monotonic Scheduling algorithm for fork-join parallel/distributed fixed-priority tasks. We study the problem of scheduling fork-join tasks that execute in a distributed system, where the inherent transmission delay of tasks must be considered and cannot be deemed negligible, as in the case of multicore systems. Our scheduling algorithm is shown to have a resource augmentation bound of 4, which implies that any task set that is feasible on m unit-speed processors and a single shared real-time network, can be scheduled by our algorithm on m processors and a single real-time network that are 4 times faster. We confirm through simulations our analytical results.

关键词： Message systems Program processors Real-time systems Multicore processing Processor scheduling Synchronization Partitioning algorithms

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HiPGA: A High Performance Genome Assembler for Short Read Sequence Data

HiPGA: A High Performance Genome Assembler for Short Read Se...

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

作者： Xiaohui Duan Kun Zhao Weiguo Liu Ministry of Education Shandong University Jinan City China

ISBN: (纸本)9781479941155

Emerging next-generation sequencing technologies have opened up exciting new opportunities for genome sequencing by generating read data with a massive throughput. However, the generated reads are significantly shorter compared to the traditional Sanger shotgun sequencing method. This poses challenges for de novo assembly algorithms in terms of both accuracy and efficiency. And due to the continuing explosive growth of short read databases, there is a high demand to accelerate the often repeated long-runtime assembly task. In this paper, we present a scalable parallel algorithm - HiPGA to accelerate the de Bruijn graph-based genome assembly for high-throughput short read data. In order to make full use of the compute power of both shared-memory multi-core CPUs and distributed-memory systems, we have used a parallelized file I/O scheme as well as a hybrid parallelism for the whole assembly pipeline. Evaluations using three real paired-end datasets and the Yoruba individual dataset show that compared to two other well parallelized assemblers: ABySS and PASHA, HiPGA achieves speedups up to 7 while delivering comparable accuracy on 64 CPU cores of a compute cluster.

关键词： Assembly Genomics Bioinformatics Vectors Pipelines parallel processing Couplings

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SkewControl: Gini Out of the Bottle

SkewControl: Gini Out of the Bottle

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

作者： Si Zheng Yunhuai Liu Tian He Li Shanshan Xiangke Liao School of Computer National University of Defense Technology Third Research Institute of Ministry of Public Security University of Minnesota Twin Cities

In the age of big data, MapReduce plays an important role in the extreme-scale data processing system. Among all the hot issues, the data skew weights heavily for the MapReduce system performance. In traditional approaches, researchers attempt to leave the users to address the issue which requires the user to possess the application-dependent domain knowledge. Other approaches address the issue automatically but in an open-loop manner which lacks of sufficient adaptivity for different applications. To well address these issues, we conduct trace-driven empirical studies and show that the skew has strong stable and predictable characteristics, which allows us to design a closed-loop automatic mechanism for task partitioning and scheduling, called SkewControl. We implement SkewControl on top of a Hadoop 1.0.4 production system. The experimental results show that compared with the state-of-art LATE and SkewTune systems, SkewControl can consistently improve the system response time by 23.8% and 17% respectively.

关键词： Time factors Data processing Cellular phones distributed databases Manuals History Educational institutions

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SiftD: A CPU & GPU distributed hybrid system for SIFT

SiftD: A CPU & GPU distributed hybrid system for SIFT

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international symposium on Telecommunications, IST

作者： Mahdi Soltan Mohammadi Mehdi Rezaeian Electrical and Computer Engineering Department Yazd University Yazd Iran

Using distributed and parallel computing systems have become a de facto for implementing scientific and industrial applications, which require tremendous amount of computing resources. As a widely used approach, general purpose distributed frameworks, like Hadoop, have provided us with many facilities to develop a distributed computing system for our applications. These General-purpose frameworks are flexible but their flexibility can only take us so far. There are many applications, which not all of their requirements can be met by these frameworks. Image matching using SIFT algorithm can be a good example of these applications. SIFT is a highly complex algorithm for extracting robust features from pictures. This paper outlines most important motivations and challenges for implementing specialized distributed systems. We present siftD, an application for distributing and parallelizing SIFT algorithm. It uses networked computers to distribute the algorithm. Inside each system, multi-core processors and Graphical Processing Units (GPUs) are used to parallelize execution. SiftD's performance and capability for utilizing different computing resources has been evaluated. Results show its performance is generally higher than 93%, which is a fairly appropriate performance. Furthermore, it can utilize broad range of hardware platforms.

关键词： Hardware Algorithm design and analysis Programming distributed databases Graphics processing units Computer architecture Computers

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Efficient pseudorecursive evaluation schemes for non-adaptive sparse grids 1

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2nd Workshop on Sparse Grids and Applications, SGA 2012

作者： Buse, Gerrit Pflüger, Dirk Jacob, Riko TU München München Germany Institute for Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany ETH Zurich Zurich Switzerland

ISBN: (数字)9783319045375

ISBN: (纸本)9783319045368

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (proceedings of the 16th ACM symposium on Principles and Practice of parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (proceedings of the international Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation. © Springer international Publishing Switzerland 2014.

关键词： parallel programming

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Genetic lateral tuning of membership functions as post-processing for hybrid fuzzy genetics-based machine learning

Genetic lateral tuning of membership functions as post-proce...

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international symposium on Soft Computing and Intelligent systems (SCIS)

作者： Yusuke Nojima Yuji Takahashi Hisao Ishibuchi Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

Genetic fuzzy systems (GFS) have been actively studied in the field of fuzzy classifier design. GFS can generate simple and accurate classifiers with a number of fuzzy if-then rules by evolutionary computation (EC). One general concern is how to discretize numerical attributes into fuzzy partitions. Most GFS use homogeneous fuzzy partitions without considering the class distribution of each attribute. This is the simplest idea, but more accurate classifiers could be obtained by optimizing fuzzy partitions. There are three approaches. One is pre-processing where inhomogeneous fuzzy partitions are specified according to the class distribution before applying EC. Another approach is to simultaneously optimize both a set of fuzzy if-then rules and fuzzy partitions by EC. The other is post-processing where fuzzy partitions used in the obtained classifier are optimized afterward. In this paper, we examine the effect of post-processing where fuzzy partition optimization is applied to the obtained classifier by GFS. In computational experiments, we first use our hybrid fuzzy genetics-based machine learning with homogeneous fuzzy partitions for standard data sets and its parallel distributed implementation for large data sets. Then we apply genetic lateral tuning as post-processing to shift the positions of membership functions according to the pattern distribution.

关键词： Genetics Tuning distributed databases Training Sociology Statistics Standards

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The importance and characteristics of communication in high performance data analytics

The importance and characteristics of communication in high ...

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IEEE international Workshop/symposium on Workload Characterization

作者： Andreea Anghel German Rodriguez Bogdan Prisacari IBM Research Zurich

Social networks and business analytics typically need to process vast amounts of data that are often modeled as graphs. The scale of the data that such applications have to handle requires large-scale distributed computing systems, together with scalable parallel algorithms, to efficiently process the graphs. Representative of the graph-based analytics class of applications is the Graph 500 benchmark (Murphy, ***., 2010), which is designed to assess the performance of supercomputing systems by solving the Breadth-First Search (BFS) graph traversal problem. In this work, we analyze the network data motion of a Graph 500 MPI version of the graph traversal problem, using a large-scale high-performance computing system, i.e., the MareNostrum III supercomputer (http://***/marenostrum-support-services/mn3). We focus our analysis on the node-to-node communication and show that the application runtime is communication-bound, the communication making up as much as 80% of the execution time of each BFS iteration. We also show that the dominating communication pattern is an overall all-to-all exchange (every process communicates to every other process and roughly the same amount of data is exchanged between any two processes), thus providing preliminary guidance for future application or network design optimization efforts.

关键词： Runtime Instruments Benchmark testing Electric breakdown Data analysis Computational modeling distributed databases

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