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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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An Application of GPU parallel Computing to Power Flow Calculation in HVDC Networks

An Application of GPU Parallel Computing to Power Flow Calcu...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Przemyslaw Blaskiewicz Marcin Zawada Przemyslaw Balcerek Pawel Dawidowski Wroclaw University of Technology Wroclaw Poland ABB Corporate Research Poland

Numerical computation on GPU has become easily accessible and offers good computation power for relatively little cost. Recently an application of Newton-Rap son method for analyzing power flow in multi-terminal high-voltage direct current (HVDC) networks was proposed and shown to have good results on five terminal grids. Since this method involves costly matrix operation, especially the inverse, increasing the number of terminals in the grid yields prohibitively large execution times in sequential operation. To address this issue, we adjust the algorithm so that it benefits from parallel computation and test our approach on recent GPU from NVidia. We give experimental results for grids up to few thousand terminals and show that execution time is still acceptable for real applications. We also provide some benchmarks of the GPU computation compared with other platforms.

关键词： Graphics processing units Matrix decomposition Jacobian matrices Transmission line matrix methods Heuristic algorithms parallel processing Kernel

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Test oracles based on metamorphic relations for image processing applications

Test oracles based on metamorphic relations for image proces...

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Tahir Jameel Mengxiang Lin Liu Chao State key Lab of Software Development Environment Beihang University Beijing China

Testing of image processing applications is a challenging job especially, when evaluating the correctness of output image. Generally, output images are evaluated manually by visual inspection carried out by an expert tester, which is the main hindrance in automation of testing process. Recently, statistical and metamorphic testing approaches are presented to automate output evaluation of image processing applications. The statistical method is dependent on availability of statistical distribution of output images, whereas metamorphic testing require more research efforts to make it widely used in practice. Metamorphic testing is a well-known technique to alleviate the test oracle problem and eliminates the required manual efforts by using relations of input and output images. Follow-up test cases are generated based on these relations and their expected output is evaluated. This paper addresses test oracle problem for image processing applications and demonstrates how properties of implementation under test can be adopted as metamorphic relations. We have studied general and specific metamorphic relations of morphological image operations such as dilation and erosion. Selection of metamorphic relations and their effectiveness by mutation analysis is demonstrated. The results show that metamorphic testing is useful for evaluation of output images in the absence of a perfect test oracle.

关键词： Testing Image processing Software algorithms Standards Smoothing methods Noise Software

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A Monitoring System for Runtime Adaptations of Streaming applications

A Monitoring System for Runtime Adaptations of Streaming App...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Manuel Selva Lionel Morel Kevin Marquet Stephane Frenot Universite de Lyon Inria INSA-Lyon CITI F-69621 France Bull Echirolles 1 rue de Provence Echirolles Cedex France

Streaming languages are adequate for expressing many applications quite naturally and have been proven to be a good approach for taking advantage of the intrinsic parallelism of modern CPU architectures. While numerous works focus on improving the throughput of streaming programs, we rather focus on satisfying quality-of-service requirements of streaming applications executed along-side non-streaming processes. We monitor synchronous dataflow (SDF) programs at runtime both at the application and system levels in order to identify violations of quality-of-service requirements. Our monitoring requires the programmer to provide the expected throughput of its application (e.g. 25 frames per second for a video decoder), then takes full benefit from the compilation of the SDF graph to detect bottlenecks in this graph and identify causes among processor or memory overloading. It can then be used to perform dynamic adaptations of the applications in order to optimize the use of computing and memory resources.

关键词： Monitoring Runtime Throughput Instruction sets Steady-state Computer architecture Hardware

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Experiences of Using Cassandra for Molecular Dynamics Simulations

Experiences of Using Cassandra for Molecular Dynamics Simula...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Roger Hernandez Cesare Cugnasco Yolanda Becerra Jordi Torres Eduard Ayguadé Barcelona Supercomputing Center Barcelona Spain Universitat Politècnica de Catalunya Barcelona Tech. Barcelona supercomputing Center Barcelona Spain

In response to the requirements of applications that work with large amounts of data, various NoSQL databases have appeared to deal specifically with these challenges. These systems have become popular in environments such as data analytics and OLTP, however these are not the only data-intensive applications that can benefit from these databases. In the life sciences domain, there are many applications that still use flat files as a medium to store data, and they see themselves very limited in terms of scalability and performance, as well as code complexity. We present an analysis on the viability of using these databases for applications with data demands that differ in some of the characteristics from what these systems were originally designed for. By using these databases, we can also observe that the design of the data model, queries and other configuration parameters can have a considerable impact on performance, thus we present examples of different data and system configurations to analyse their effects on performance. With the executions that are presented in this paper we can see performance gaps of a factor of up to almost 5 between using different models, queries and configuration parameters.

关键词： Data models Trajectory Time factors distributed databases Peer-to-peer computing Scalability

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Neurodynamic Optimization Approaches to parallel Data Selection in the Era of Big Data

Neurodynamic Optimization Approaches to Parallel Data Select...

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2015 2nd international conference on Modelling,Identification and Control(MIC 2015)

作者： Jun Wang Department of Mechanical & Automation Engineering The Chinese University of Hong Kong

In the present information era, huge amount of data to be processed daily. In contrast of conventional sequential data processing techniques, parallel data processing approaches can expedite the processes and more efficiently deal with big data. In the last few decades, neural computation emerged as a popular area for parallel and distributed data processing. The data processing applications of neural computation included, but not limited to, data sorting, data selection, data mining, data fusion, and data reconciliation. In this talk, neurodynamic approaches to parallel data processing will be introduced, reviewed, and compared. In particular, my talk will compare several mathematical problem formulations of well-known multiple winners-take-all problem and present several recurrent neural networks with reducing model complexity. Finally, the best one with the simplest model complexity and maximum computational efficiency will be highlighted. Analytical and Monte Carlo simulation results will be shown to demonstrate the computing characteristics and performance of the continuous-time and discrete-time models. The applications to parallel sorting, rank-order filtering, and data retrieval will be also discussed.

关键词： Neurodynamic Optimization Approaches to parallel Data Selection in the Era of Big Data

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Energy-Efficient Task Scheduling in Manycore Processors with Frequency Scaling Overhead

Energy-Efficient Task Scheduling in Manycore Processors with...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Patrick Eitschberger Jörg Keller Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany

We investigate deadline scheduling of independent tasks on parallel processors with discrete frequency levels, when the latency for frequency scaling cannot be neglected. This situation frequently occurs in applications, e.g. streaming applications with soft real-time requirements. We demonstrate that previous algorithms for energy-optimal static scheduling of independent tasks are non-optimal in this setting. We present a scheduling heuristic based on bin packing with a cost function that takes latency for frequency scaling into account. We evaluate our heuristic against previous approaches with benchmark task sets and achieve energy reductions between 3% and 13%. We further demonstrate that for a concrete embedded multicore processor, the power curves vary over the identical cores, so that the processor looks heterogeneous from a power perspective. We adapt our bin packing heuristic and demonstrate that for the benchmark task sets, further energy reductions up to 4% can be achieved.

关键词： Multicore processing Program processors Energy consumption Time-frequency analysis Schedules Processor scheduling Scheduling

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Can portability improve performance? An empirical study of parallel graph analytics 15

Can portability improve performance? An empirical study of p...

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6th ACM/SPEC international conference on Performance Engineering, ICPE 2015

作者： Varbanescu, Ana Lucia Verstraaten, Merijn De Laat, Cees Penders, Ate Iosup, Alexandru Sips, Henk University of Amsterdam Netherlands Delft University of Technology Netherlands

ISBN: (纸本)9781450332484

Due to increasingly large datasets, graph analytics-traversals, allpairs shortest path computations, centrality measures, etc.-are becoming the focus of high-performance computing (HPC). Because HPC is currently dominated by many-core architectures (both CPUs and GPUs), new graph processing solutions have to be defined to efficiently use such computing resources. Prior work focuses on platform-specific performance studies and on platform-specific algorithm development, successfully proving that algorithms highly tuned to GPUs or multi-core CPUs can provide high performance graph analytics. However, the portability of such algorithms remains an important concern for many users, especially the many companies without the resources to invest in HPC or concerned about lock-in in single-use parallel techniques. In this work, we investigate the functional portability and performance of graph analytics algorithms. We conduct an empirical study measuring the performance of 3 graph analytics algorithms (a single code implemented in OpenCL and targeted at many-core CPUs and GPUs), on 3 different platforms, using 11 real-world and synthetic datasets. Our results show that the code is functionally portable, that is, the applications can run unchanged on both CPUs and GPUs. The large variation in their observed performance indicates that portability is necessary not only for productivity, but, surprisingly, also for performance. We conjecture that the impact of datasets on performance is too high to allow platform-specific algorithms to outperform the portable algorithms by large margins, in all cases. Our conclusion is that portable parallel graph analytics is feasible without significant performance loss, and provides a productive alternative to the expensive trial-and-error selection of one algorithm for each (graph,platform) pair. Copyright © 2015 ACM.

关键词： Program processors

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Keynote Speech: Neurodynamics-based parallel Data Selection in the Era of Big Data

Keynote Speech: Neurodynamics-based Parallel Data Selection ...

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2015 3rd international conference on Software Engineering,Knowledge Engineering and Information Engineering(SEKEIE 2015)

作者： Jun Wang Department of Mechanical & Automation Engineering The Chinese University of Hong Kong Shatin

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High-Performance parallel Location-Aware Algorithms for Approximate String Matching on GPUs

High-Performance Parallel Location-Aware Algorithms for Appr...

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international conference on parallel and distributed Systems (ICPADS)

作者： Cheng-Hung Lin Chun-Cheng Huang Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9781467386692

Approximate string matching has been widely used in many applications, including deoxyribonucleic acid sequence searching, spell checking, text mining, and spam filters. The method is designed to find all locations of strings that approximately match a pattern in accordance with the number of insertion, deletion, and substitution operations. Among the proposed algorithms, the bit-parallel algorithms are considered to be the best and highly efficient algorithms. However, the traditional bit-parallel algorithms lacks the ability of identifying the start and end positions of a matched pattern. Furthermore, acceleration of the bit-parallel algorithms has become a crucial issue for processing big data nowadays. In this paper, we propose two kinds of parallel location-aware algorithms called data-segmented parallelism and high-degree parallelism as means to accelerate approximate string matching using graphic processing units. Experimental results show that the high-degree parallelism on GPUs achieves significant improvement in system and kernel throughputs compared to CPU counterparts. Compared to state-of-the-art approaches, the proposed high-degree parallelism achieves 11 to 105 times improvement.

关键词： Pattern matching Approximation algorithms Algorithm design and analysis parallel processing Registers Business process re-engineering Digital signal processing

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FRAME: Fair Resource Allocation in Multi-process Environments

FRAME: Fair Resource Allocation in Multi-process Environment...

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international conference on parallel and distributed Systems (ICPADS)

作者： Amin Mohtasham Ricardo Filipe João Barreto INESC-ID Lisboa/lnstituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (纸本)9781467386692

As the technology trend moves toward manufacturing many-core systems with hundreds of processing cores, the problem of efficiently managing multiple parallel jobs on such massively parallel systems becomes increasingly important. With traditional time-sharing each process assumes it is the only running process. This assumption can easily lead to system oversubscription and thereby, losing overall performance due to frequent context switches. Space-sharing techniques allocate a certain number of hardware cores to each process and by that malleable processes can set their parallelism level to their allocated number of cores, hence avoiding oversubscription. However, finding the optimal spatial allocation is not a trivial task. In this paper we propose FRAME, a resource allocation technique to maximize a system's overall utility and fairness, running multiple malleable processes with CPU-bound workloads. First, we formalize the resource allocation problem as an NP-hard problem. Then, we use approximation techniques and convex optimization theory to find the optimal solution to the formulated problem, in pseudo-polynomial time. Our evaluation results show that our method is very fast and efficient in finding the optimal solution to the resource allocation problem. Also, the results suggests that the found solution increases the system's overall utility by 48%, in average, with regard to the best alternative allocation policy.

关键词： Resource management Hardware Instruction sets parallel processing Convex functions Throughput Multicore processing

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