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检索条件"任意字段=7th International Symposium on Parallel Architectures, Algorithms, and Programming, PAAP 2015"

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parallel genetic algorithm with social interaction for solving constrained global optimization problems 7

Parallel genetic algorithm with social interaction for solvi...

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7th international Conference of Soft Computing and Pattern Recognition, SoCPaR 2015

作者： Lisboa, Rodrigo Yasojima, Edson Koiti De Oliveira, Rodrigo M. S. Mollinetti, Marco A. Florenzano Teixeira, Otavio Noura De Oliveira, Roberto C. Limao Belém Pará Brazil Belém Pará Brazil Systems Optimization Laboratory Tsukuba University Tsukuba Ibaraki Japan Tucuruí Pará Brazil Belém Pará Brazil

ISBN: (纸本)9781467393607

the following paper introduces a parallel approach to a social variant of the Genetic Algorithm, called parallel Genetic Algorithm with Social Interaction (PSIGA). the algorithm is based on social games involving game theory, and it is implemented using the OpenMP API, which is based on the shared memory programming model for multiple processor architectures. the main contribution of this approach is the parallelization using the Shared Memory of the Social Interaction Genetic Algorithm (SIGA) in order to achieve faster and better optimality than its nonparallel counterpart for global optimization problems with restrictions. For means of performance assessment, the algorithm is tested on four instances of engineering design problems and the obtained results compared with the Genetic Algorithm with Social Interaction (SIGA) implemented in sequential programming model. © 2015 IEEE.

关键词： Genetic algorithms

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GPregel: A GPU-Based parallel Graph Processing Model 17

GPregel: A GPU-Based Parallel Graph Processing Model

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Lai, Siyan Lai, Guangda Shen, Guojun Jin, Jing Lin, Xiaola Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781479989379

With the development of information technology, graph computing becomes an increasingly important tool for information processing. Recently, GPU has been adopted to accelerate various graph processing algorithms. However, since the architecture of GPU is very different from traditional computing model, the learning threshold for developing GPU-based applications is high. In this paper, we propose a GPU-based parallel graph processing system named GPregel. GPregel harnesses a lightweight complier to hide the underlying complexity of the parallel details and simplifies programming. It greatly reduces the difficulty in utilizing the GPU to solve graph computing problems. We also design a special storage model for BSP model running on GPU, which overcomes the execution divergence and irregular memory access by coarse-grained designs. Experiments demonstrate that GPregel can achieve high performance with little work for developers.

关键词： graph processing GPGPU GPU programming parellel computing

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Realistic Task parallelization of the H.264 Decoding Algorithm for Multiprocessors 17

Realistic Task Parallelization of the H.264 Decoding Algorit...

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Lin, Xiaohao Liu, Weichen Xiao, Chunming Dai, Jie Luo, Xianlu Zhang, Dan Liu, Duo Wu, Kaijie Zhuge, Qingfeng Sha, Edwin H. M. Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781479989379

there is a phenomenon that hardware technology has developed ahead of software technology in recent years. Companies lack of software techniques that can fully utilize the modern multi-core computing resources, mainly due to the difficulty of investigating the inherent parallelism inside a software. this problem exists in products ranging from energy-sensitive smartphones to performance-eager data centers. In this paper, we present a case study on the parallelization of the complex industry standard H.264 HDTV decoder application in multi-core systems. An optimal schedule of the tasks is obtained and implemented by a carefully-defined software parallelization framework (SPF). the parallel software framework is proposed together with a set of rules to direct parallel software programming (PSPR). A pre-processing phase based on the rules is applied to the source code to make the SPF applicable. the task-level parallel version of the H.264 decoder is implemented and tested extensively on a workstation running Linux. Significant performance improvement is observed for a set of benchmarks composed of 720p videos. the SPF and the PSPR will together serve as a reference for future parallel software implementations and direct the development of automated tools.

关键词： Decoding Industries parallel processing parallel programming Software Software algorithms Videos

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Evaluation of Hybrid parallel Cell List algorithms For Monte Carlo Simulation 17

Evaluation of Hybrid Parallel Cell List Algorithms For Monte...

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Rushaidat, Kamel Schwiebert, Loren Jackman, Brock Mick, Jason Potoff, Jeffrey Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Wayne State Univ Dept Chem Engn & Mat Sci Detroit MI USA

ISBN: (纸本)9781479989379

this paper describes efficient, scalable parallel implementations of the conventional cell list method and a modified cell list method to calculate the total system intermolecular Lennard-Jones force interactions in the Monte Carlo Gibbs ensemble. We targeted this part of the Gibbs ensemble for optimization because it is the most computationally demanding part of the force interactions in the simulation, as it involves all the molecules in the system. the modified cell list implementation reduces the number of particles that are outside the interaction range by making the cells smaller, thus reducing the number of unnecessary distance evaluations. Evaluation of the two cell list methods is done using a hybrid MPI+OpenMP approach and a hybrid MPI+CUDA approach. the cell list methods are evaluated on a small cluster of multicore CPUs, Intel Phi coprocessors, and GPUs. the performance results are evaluated using different combinations of MPI processes, threads, and problem sizes.

关键词： Cell List Monte Carlo Simulations Hybrid parallel architectures Gibbs Ensemble

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Optimizing Tasks Assignment on Heterogeneous Multi-core Real-time Systems with Minimum Energy 17

Optimizing Tasks Assignment on Heterogeneous Multi-core Real...

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Li, Ying Niu, Jianwei Qiu, Meikang Long, Xiang Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China Pace Univ Seidenberg Sch Comp Sci & Informat Syst White Plains NY 10606 USA

ISBN: (纸本)9781479989379

the main challenge for embedded real-time systems, especially for mobile devices, is the trade-off between system performance and energy efficiency. through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures, we propose an Accelerated Search algorithm based on dynamic programming to obtain a combination of various task schemes which can be completed in a given time with a confidence probability by consuming the minimum possible energy. We adopt a DAG (Directed Acyclic Graph) to represent the precedent relation between tasks and develop a Minimum-Energy Model to find the optimal tasks assignment. the heterogeneous multi-core architectures can execute tasks under different voltage level with DVFS which leads to different execution time and different consumption energy. the experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).

关键词： heterogeneous multi-core real-time system tasks assignment probability statistics minimum energy

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JParEnt: parallel entropy decoding for JPEG decompression on heterogeneous multicore architectures 7

JParEnt: Parallel entropy decoding for JPEG decompression on...

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Euro-Par Conference / 7th international Workshop on programming Models and Applications for Multicores and Manycores (PMAM) held in conjunction with the 21st SIGPLAN symposium on Principles and Practice of parallel programming (PPoPP)

作者： Sodsong, Wasuwee Jung, Minyoung Park, Jinwoo Burgstaller, Bernd Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781450341967

the JPEG format employs Huffman codes to compress the entropy data of an image. Huffman codewords are of variable length, which makes parallel entropy decoding a difficult problem. To determine the start position of a codeword in the bitstream, the previous codeword must be decoded first. We present JParEnt, a new approach to parallel entropy decoding for JPEG decompression on heterogeneous multicores. JParEnt conducts JPEG decompression in two steps: (1)an efficient sequential scan of the entropy data on the CPU to determine the start-positions (boundaries) of coefficient blocks in the bitstream, followed by (2)a parallel entropy decoding step on the graphics processing unit (GPU). the block boundary scan constitutes a reinterpretation of the Huffman-coded entropy data to determine codeword boundaries in the bitstream. We introduce a dynamic workload partitioning scheme to account for GPUs of low compute power relative to the CPU. this configuration has become common with the advent of SoCs with integrated graphics processors (IGPs). We leverage additional parallelism through pipelined execution across CPU and GPU. For systems providing a unified address space between CPU and GPU, we employ zero-copy to completely eliminate the data transfer overhead. Our experimental evaluation of JParEnt was conducted on six heterogeneous multicore systems: one server and two desktops with dedicated GPUs, one desktop with an IGP, and two embedded systems. For a selection of more than 1000JPEG images, JParEnt outperforms the SIMD-implementation of the libjpeg-turbo library by up to a factor of 4.3x, and the previously fastest JPEG decompression method for heterogeneous multicores by up to a factor of 2.2x. JParEnt's entropy data scan consumes 45% of the entropy decoding time of libjpeg-turbo on average. Given this new ratio for the sequential part of JPEG decompression, JParEnt achieves up to97% of the maximum attainable speedup (95% on average). On the IGP-based desktop platform,

关键词： JPEG decoding entropy decoding prefix codes heterogeneous multicores GPU programming

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Cluster Scheduler on Heterogeneous Cloud 17

Cluster Scheduler on Heterogeneous Cloud

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Ling, Xiao Yang, Jiahai Wang, Dan Wang, Ye Tsinghua Univ Sch Informat Sci & Technol Inst Network Sci & Cyberspace Beijing Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China China Mobile Commun Corp Dept Network Beijing Peoples R China

ISBN: (纸本)9781479989379

With the increasingly widespread adoption of cloud computing and tenants growing needs for large-scale data processing, cluster scheduling frameworks (e.g. MapReduce, Spark, etc.) have emerged as important programming models that works for distributed and parallel computing on cloud systems. While several recent researches proposed some solutions to optimize the MapReduce-like scheduler, they hardly consider the significant impact of external factors caused by heterogeneity of cloud systems, especially I/O contention and instance types selection. In this paper, we present a simplified abstraction of cluster scheduling problem and formulate it as an optimization problem. To minimize the overall task weighted completion times, which is NP-complete, we propose a novel 7-approximation heuristic algorithm MRS. By comparing our algorithm with other classical scheduling strategies on Amazon EC2, we demonstrates that MRS consistently outperforms these algorithms under different scenarios.

关键词： Cloud Heterogeneous MRS NP-complete Scheduler

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CUDA Grid-Level Task Progression algorithms

CUDA Grid-Level Task Progression Algorithms

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IEEE international symposium on Cyberspace Safety and Security

作者： Christos Kartsaklis Wayne Joubert Oscar R. Hernandez Markus Eisenbach Wael R. Elwasif David E. Bernholdt Oak Ridge National Laboratory

ISBN: (纸本)9781479989386

Tasking is a prominent parallel programming model. In this paper we conduct a first study into the feasibility of task-parallel execution at the CUDA grid, rather than the stream/kernel level, for regular, fixed in-out dependency task graphs, similar to those found in wavefront computational patterns, making the findings broadly applicable. We propose and evaluate three CUDA task progression algorithms, where threadblocks cooperatively process the task graph, and argue about their performance in terms of tasking throughput, atomics and memory IO overheads. Our initial results demonstrate a throughput of 38 million tasks/second on a Kepler K20X architecture.

关键词： task graphs throughput progressions Regular parallel execution parallel programming IO Kepler algorithms

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Autotuning GPU-accelerated QAP Solvers for Power and Performance 17

Autotuning GPU-accelerated QAP Solvers for Power and Perform...

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2015 IEEE 17th international Conference on High Performance Computing and Communications (HPCC)

作者： Chaparala, Abhilash Novoa, Clara Qasem, Apan Texas State Univ Dept Comp Sci San Marcos TX 78666 USA Texas State Univ Ingram Sch Engn San Marcos TX USA

ISBN: (纸本)9781479989379

the Quadratic Assignment Problem (QAP) is an important combinatorial optimization problem with applications in many areas including operations research and chip design. QAP is known to be NP-hard and requires heuristic approaches for most real data sets. Although many algorithms have been proposed for solving QAPs, few have attempted to exploit the fine-grain data parallelism available on accelerator architectures and none have considered aspects of energy efficiency. In this paper, we present GPU-accelerated implementations of two QAP algorithms. We parameterize each algorithmic variant along several dimensions including thread granularity, occupancy, shared memory usage and register pressure. We develop an autotuning framework that explores this space of execution parameters and yields CUDA binaries that are efficient in terms of both performance and power consumption. We demonstrate the effectiveness of our tuning framework on an Nvidia Tesla K20c. On a series of experiments on the well-known QAPLIB data sets, our autotuned solutions run at least an order-of-magnitude faster than baseline implementations. the experimental results also provide key insight into the performance characteristics of accelerated QAP solvers. In particular, the results reveal that both algorithmic choice and the shape of the input data sets are key factors in finding an efficient implementation.

关键词： autotuning energy efficiency parallel performance

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How to parallelize cellular neural networks on cluster architectures

How to parallelize cellular neural networks on cluster archi...

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international symposium on parallel architectures, algorithms and Networks (ISPAN)

作者： T. Weishaupl E. Schikuta Department of Computer Science and Business Informatics University of Technology Vienna Vienna Austria

In this paper, we present "rules of thumb" for the efficient and straight-forward parallelization of cellular neural networks (CNNs) processing image data on cluster architectures. the rules result from the application and optimization of the simple but effective structural data parallel approach, which is based on the SPMD model. Digital gray-scale images were used to evaluate the optimized parallel cellular neural network program. the process of parallelizing the algorithm employs HPF to generate an MPI-based program.

关键词： Cellular neural networks Neural networks Computer architecture Computational modeling Clustering algorithms Signal processing algorithms parallel processing Image edge detection parallel programming Computer science

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