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共 16627 条记录，以下是251-260 订阅

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OLAP Aggregation Based on Dimension-oriented Storage

OLAP Aggregation Based on Dimension-oriented Storage

引用

26th ieee international parallel and distributed processing symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Zhao Jing-hua Song Ai-mei Song Ai-bo Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9780769546766

OLAP (online analytical processing) applications are based on a variety of aggregate queries on large-scale data. As aggregation is always performed on columns, traditional row-oriented storage, in which all the columns of a data row are stored together, has seriously restricted its performance. This paper proposes a dimension-oriented storage model based on HBase, and a new parallel aggregation technique, which accomplishes aggregation operations with parallel MapReduce jobs. Finally, compared with Hive on standard TPC-H data set, our technique is demonstrated to improve performance of core aggregate operations significantly.

关键词： OLAP (online analytical processing) dimension-oriented storage aggregation MapReduce

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Best-Effort parallel Execution Framework for Recognition and Mining applications

Best-Effort Parallel Execution Framework for Recognition and...

引用

23rd ieee international parallel and distributed processing symposium

作者： Meng, Jiayuan Chakradhar, Srimat Raghunathan, Anand NEC Labs Amer Princeton NJ USA

ISBN: (纸本)9781424437511

Recognition and mining (RM) applications are an emerging class of computing workloads that will be commonly executed on future multi-core and many-core computing platforms. The explosive growth of input data and the use of more sophisticated algorithms in RM applications will ensure, for the foreseeable future, a significant gap between the computational needs of RM applications and the capabilities of rapidly evolving multi- or many-core platforms. To address this gap, we propose a new parallel programming model that inherently embodies the notion of best-effort computing, wherein the underlying parallel computing environment is not expected to be perfect. The proposed best-effort programming model leverages three key characteristics of RM applications: (1) the input data is noisy and it often contains significant redundancy, (2) computations performed on the input data are statistical in nature, and (3) some degree of imprecision in the output is acceptable. As a specific instance of the best-effort parallel programming model, we describe an "iterative-convergence" parallel template, which is used by a significant class of RM applications. We show how best-effort computing can be used to not only reduce computational workload, but to also eliminate dependencies between computations and further increase parallelism. Our experiments on an 8-core machine demonstrate a speed-up of 3.5X and 4.3X for the K-means and GLVQ algorithms, respectively, over a conventional parallel implementation. We also show that there is almost no material impact on the accuracy of results obtained from best-effort implementations in the application context of image segmentation using K-means and eye detection in images using GLVQ.

关键词： Eye protection

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A Modular Abstract Framework for Devising Optimized RSMs based on Load Distribution 15

A Modular Abstract Framework for Devising Optimized RSMs bas...

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15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Yang, Yingyi Nie, Ming Wang, Ke Guangdong Power Grid Corp Elect Power Res Inst Intelligent Robot Res Team Dept Artificial Intelligence & Robot Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538637906

Replicated state machine (RSM) is usually used for building highly available services in today's cloud systems. By analyzing typical instances and summing up their common characteristics, we introduce a modular abstract framework for devising optimized RSMs from the load distribution perspective, which is an active exploration to find a new optimization design method. The framework provides a faithful deconstruction of a class of RSMs optimized by load distribution. Through the modularization of functionalities/mechanisms, the framework decouples the design of components of a RSM to some extent and facilitates the reusability of existing and optimized functional components. By abstracting the configuration and specifying the execution procedure, the abstract skeletal protocol in this framework simplifies the description of optimized RSMs. We finally present the reconstruction of S-RSM, a RSM that is optimized based on load distribution, to illustrate the effectiveness of our framework.

关键词： replicated state machine load distribution Paxos framework abstraction modularization

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Automated rapid prototyping of regular grid-based numerical applications using generalized elemental subroutines

Automated rapid prototyping of regular grid-based numerical ...

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ieee 27th international parallel and distributed processing symposium (IPDPS)

作者： Situ, Yingchong Wang, Ye Li, Zhiyuan Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9780769549712

Computational scientists and engineers commonly rely on established software libraries to achieve high performance and reliability in their numerical applications. Unfortunately, this approach does not work well if the desired functionality is absent in existing libraries or if the integration is difficult. In such scenarios, one is often forced to explore alternative algorithms and in-house implementations. Such exploration can be a challenging task for computational scientists and engineers without sufficient computer science background. To address this issue, we design and build an automated rapid prototyping tool for regular grid-based numerical applications. This new tool allows programmers to specify algorithms as composition of familiar computation patterns such as those easily found in open literature expressed as generalized elemental subroutines. The tool then automatically transforms such subroutines into code which adapts to the prescribed data structures and delivers performance expected from the underlying algorithms. We demonstrate the tool in use cases including a production-grade computational fluid dynamic application.

关键词： compiler analysis and optimization program transformation programming models

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Exploring the Effect of Compiler Optimizations on the Reliability of HPC applications 31

Exploring the Effect of Compiler Optimizations on the Reliab...

引用

31st ieee international parallel and distributed processing symposium Workshops (IPDPS)

作者： Ashraf, Rizwan A. Gioiosa, Roberto Kestor, Gokcen DeMara, Ronald F. Oak Ridge Natl Lab Oak Ridge TN 37831 USA Pacific Northwest Natl Lab Richland WA 99352 USA Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9780769561493

The strict power efficiency constraints required to achieve exascale systems will dramatically increase the number of detected and undetected transient errors in future high performance computing (HPC) systems. Among the various factors that effect system resiliency, the impact of compiler optimizations on the vulnerability of scientific applications executed on HPC systems has not been widely explored. In this work, we analyze whether and how most common compiler optimizations impact the vulnerability of several mission-critical applications, what are the trade-offs between performance and vulnerability and the causal relations between compiler optimization and application vulnerability. We show that highly-optimized code is generally more vulnerable than unoptimized code. We also show that, while increasing optimization level can drastically improve application performance as expected. However, certain cases of optimization may provide only marginal benefits, but considerably increase application vulnerability.

关键词： compiler Exascale faults HPC llvm resilience

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Application of Graph Sparsification in Developing parallel Algorithms for Updating Connected Components 30

Application of Graph Sparsification in Developing Parallel A...

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

作者： Srinivasan, Sriram Bhowmick, Sanjukta Das, Sajal Univ Nebraska Dept Comp Sci Omaha NE 68182 USA Missouri Univ Sci & Technol Dept Comp Sci Rolla MO USA

ISBN: (纸本)9781509036820

Analyzing large dynamic networks is an important problem with applications in a wide range of disciplines. A key operation is updating the network properties as its topology changes. In this paper we present graph sparsification as an efficient abstraction for updating the properties of dynamic networks. We demonstrate the applicability of graph sparsification in updating the connected components in random and scale-free networks on shared memory systems. Our results show that the updating is scalable (10X on 16 processors for larger networks). To the best of our knowledge this is the first parallel implementation of graph sparsification. Based on these initial results, we discuss how the current implementation can be further improved and how graph sparsification can be applied to updating other network properties.

关键词： dynamic networks graph sparsification

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Reinforcement Learning Control for Water-Efficient Agricultural Irrigation 15

Reinforcement Learning Control for Water-Efficient Agricultu...

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15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Sun, Lijia Yang, Yanxiang Hu, Jiang Porter, Dana Marek, Thomas Hillyer, Charles Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M AgriLife Res Amarillo TX USA

ISBN: (纸本)9781538637906

Modern sensor technologies, internet and advanced irrigation equipment allow a relative precise control of agricultural irrigation that leads to high water-use efficiency. However, the core control algorithms that make use of these technologies have not been well studied. In this work, a reinforcement learning based irrigation control technique is investigated. The delayed reward of crop yield is handled by the temporal difference technique. The learning process can be based on both off-line simulation and real data from sensors and crop yield. Neural network based fast models for soil water level and crop yield are developed to improve the scalability of learning. Simulations for various geographic locations and crop types show that the proposed method can significantly increase net return considering both crop yield and water expense.

关键词： Irrigation Learning (artificial intelligence) Soil moisture Meteorology Wireless sensor networks

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A Dynamic Placement Policy of Virtual Machine Based on MOGA in Cloud Environment 15

A Dynamic Placement Policy of Virtual Machine Based on MOGA ...

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15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Zhang, Mohan Ren, Honglin Xia, Chunhe Beihang Univ Sch Comp Sci Beijing Key Lab Network Technol Beijing Peoples R China Guangxi Normal Univ Sch Comp Sci & Informat Technol Guangxi Peoples R China

ISBN: (纸本)9781538637906

Currently loud data centers exist problems of load imbalance and high power consumption. This paper studies the virtual machine placement policy in cloud environment by applying live migration techniques, and proposes a target host selection algorithm MOGA-THSA based on MOGA. As a heuristic algorithm, through designing excellent genetic operators and fitness functions, it optimizes the load balance and power consumption of the data center with smaller SLA violation rate. The algorithm is implemented on simulation platform CloudSim, and experiments show that it can improve the load balance of cloud data center and decrease total power consumption effectively, thus having a certain guiding significance for researching the virtual machine placement policy.

关键词： Cloud Datacenter Placement Policy Load Balance Power Consumption MOGA

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Job Scheduling Optimization of High Performance Computing in Biological Gene Sequencing Based on Workload Analysis 15

Job Scheduling Optimization of High Performance Computing in...

引用

15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Wu, Xiaofei Dong, Shoubin Sun, Yizhen Zuo, Liyun South China Univ Technol Guangdong Key Lab Commun & Comp Network Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538637906

Mining job scheduling features based on extraction and analysis of workload trace in high performance computing clusters can be used to optimize scheduling strategy and enhance system performance. Based on detailed analysis of workload trace from a gene sequencing high performance computing system, this paper proposes a multi-queue backfilling scheduling algorithm, which is based on traditional backfilling scheduling. While optimizing for memory resource demands, this algorithm provides queue level load balancing to deal with the innate load imbalance characteristics of high performance systems. Experimental results based on practical gene sequencing workload trace clearly demonstrate that compared with traditional scheduling algorithms, the algorithm proposed in this paper is a good strategy to reduce the job waiting time and improve resource utilization.

关键词： high performance computing job scheduling backfilling load imbalance

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Dynamic Fine-Grained Workload Partitioning for Irregular applications on Discrete CPU-GPU Systems 19

Dynamic Fine-Grained Workload Partitioning for Irregular App...

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19th ieee international symposium on parallel and distributed processing with applications (ieee ISPA)

作者： Xiao, Chunhua Ran, Wei Lin, Fangzhu Zhang, Lin Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781665435741

As the scale of big data continues to grow and the complexity of data analysis algorithms increases, the desire for greater computing power is increasingly evident. A popular approach is to utilize heterogeneous systems for computation. Discrete CPU-GPU system, which has CPU and GPU on different chips connected through a PCI-e bus, is a typical heterogeneous system. There have been a lot of schemes proposed to improve performance on the discrete CPU-GPU system through partitioning. However, most of them are for regular applications and far from ideal in terms of resource utilization and performance for irregular applications. Heterogeneous computing receives much attention due to its performance potential, and many regular applications are greatly benefited from it, but the acceleration of irregular applications is still a problem to be solved. In this paper, we propose a dynamic fine-grained workload partitioning approach for irregular applications that boosts resource utilization to achieve a better load balance on heterogeneous platforms. The approach monitors the kernel execution of the CPU and GPU at runtime, and finely partitions the workload according to their processing speed, assigning relatively regular data to the GPU and the rest to the CPU. Evaluated with various irregular workloads, our scheme achieves up to 20% performance improvement over the state-of-the-art coarse-grained scheme, and the performance gap is less than 5% in most cases compared to oracle-based partitioning.

关键词： heterogeneous system load balancing irregular application workload partitioning

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