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检索条件"任意字段=2nd International Symposium on Parallel and Distributed Processing and Applications"

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Improving Application Performance by Efficiently Utilizing Heterogeneous Many-core Platforms 15

Improving Application Performance by Efficiently Utilizing H...

引用

2015 15th IEEE ACM international symposium on Cluster Cloud and Grid Computing (CCGrid 2015)

作者： Shen, Jie Varbanescu, Ana Lucia Sips, Henk Delft Univ Technol Parallel & Distributed Syst Grp NL-2600 AA Delft Netherlands Univ Amsterdam Inst Informat NL-1012 WX Amsterdam Netherlands

ISBN: (纸本)9781479980062

Heterogeneous platforms integrating different types of processing units (such as multi-core CPUs and GPUs) are in high demand in high performance computing. Existing studies have shown that using heterogeneous platforms can improve application performance and hardware utilization. However, systematic methods to design, implement, and map applications to efficiently use heterogeneous computing resources are only very few. The goal of my PhD research is therefore to study such heterogeneous systems and propose systematic methods to allow many (classes of) applications to efficiently use them. After 3.5 years of PhD study, my contributions are (1) a thorough evaluation of a suitable programming model for heterogeneous computing;(2) a workload partitioning framework to accelerate parallel applications on heterogeneous platforms;(3) a modeling-based prediction method to determine the optimal workload partitioning;(4) a systematic approach to decide the best mapping between the application and the platform by choosing the best performing hardware configuration (Only-CPU, Only-GPU, or CPU+GPU with the workload partitioning). In the near future, I plan to apply my approach to large-scale applications and platforms to expand its usability and applicability.

关键词： Heterogeneous platforms Workload partitioning Hardware configuration Multi-core CPUs GPUs Accelerators

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Fast Burrows Wheeler Compression using All-Cores 29

Fast Burrows Wheeler Compression using All-Cores

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29th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Deshpande, Aditya Narayanan, P. J. Univ Illinois Champaign IL 61820 USA Int Inst Informat Technol Hyderabad Hyderabad Andhra Pradesh India

ISBN: (纸本)9781467376846

In this paper, we present an all-core implementation of Burrows Wheeler Compression algorithm that exploits all computing resources on a system. Our focus is to provide significant benefit to everyday users on common end-to-end applications by exploiting the parallelism of multiple CPU cores and additional accelerators, viz. many-core GPU, on their machines. The all-core framework is suitable for problems that process large files or buffers in blocks. We consider a system to be made up of compute stations and use a work-queue to dynamically divide the tasks among them. Each compute station uses an implementation that optimally exploits its architecture. We develop a fast GPU BWC algorithm by extending the state-of-the-art GPU string sort to efficiently perform BWT step of BWC. Our hybrid BWC with GPU acceleration achieves a 2.9x speedup over best CPU implementation. Our all-core framework allows concurrent processing of blocks by both GPU and all available CPU cores. We achieve a 3.06x speedup by using all CPU cores and a 4.87x speedup when we additionally use an accelerator i.e. GPU. Our approach will scale to the number and different types of computing resources or accelerators found on a system.

关键词： Burrows Wheeler Transform Bzip2 Heterogeneous systems

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A parallel Framework for Object Detection and Recognition for Secure Vehicle Parking 17

A Parallel Framework for Object Detection and Recognition fo...

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

作者： Mahmood, Zahid Khan, Muhammad Usman Shahid Jawad, Muhammad Khan, Samee U. Yang, Laurence T. North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58102 USA St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

ISBN: (纸本)9781479989379

Image analytics, biometrics access control, security, and surveillance applications utilize complex machine learning and computer vision algorithms, such as face detection and recognition. Speedup and accuracy are two important factors that need to be addressed in all such complex applications. parallel computing breaks down the complex tasks into discrete fragments to be solved concurrently on multiple processors. The parallel computing procedure significantly reduces the execution time with improved speedup. This paper presents a parallel framework for object detection and recognition for a secure vehicle parking. The proposed framework is divided in to three steps: (1) vehicle detection at the parking entry junction, (2) driver's face detection, and (3) identification of driver's face from the huge database of stored facial images. On successful identification of authorized person, vehicle is allowed to enter in the parking-lot. The adaptive boosting algorithm is used for vehicle and face detection, while Eigenfaces based approach is employed for face recognition. Moreover, the scalability comparison of parallely executed driver face recognition algorithm indicates high speedup compared to serial execution. Furthermore, the results of the proposed framework reveal promising performance and encourage outcomes to be deployed in real-time at entrance/exits of the public/private vehicle parking areas.

关键词： Eigenfaces Face Recognition Haar Classifiers parallel processing

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CUDAGA: A portable parallel programming model for GPU cluster 2nd

CUDAGA: A portable parallel programming model for GPU cluste...

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2nd international Conference on Cloud Computing and Big Data, CloudCom-Asia 2015

作者： Chen, Yong Jin, Hai Xu, Dechao Zheng, Ran Liu, Haocheng Zeng, Jingxiang Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9783319284293

GPU cluster is important for high performance computing with its high performance/cost ratio. However, it is still very hard for application developers to write parallel codes on GPU. MPI is mostly used for parallel programming, and data locality and communication must be specified explicitly by developers. Moreover, data transmission between CPU and GPU must also be processed with CUDA codes. CUDAGA, a new parallel programming model for GPU cluster with CUDA, is presented to provide portable interfaces for communication on GPUs. GA (Global Arrays), a portable shared-memory programming model for distributed memory computers, is the base to facilitate parallel programming and maintain transparent global arrays on GPUs. Experiments show that CUDAGA can decrease parallel programming difficulties, but ensures better performance for some specific applications. © Springer international Publishing Switzerland 2015.

关键词： Graphics processing unit

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Metaprogramming dense linear algebra solvers applications to multi and many-core architectures 14

Metaprogramming dense linear algebra solvers Applications to...

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13th IEEE international symposium on parallel and distributed processing with applications

作者： Masliah, Ian Baboulin, Marc Falcou, Joel Univ Paris 11 F-91405 Orsay France Inria F-91405 Orsay France

ISBN: (纸本)9781467379526

The increasing complexity of new parallel architectures has widened the gap between adaptability and efficiency of the codes. As high performance numerical libraries tend to focus more on performance, we wish to address this issue using a C++ library called NT2. By analyzing the properties of the linear algebra domain that can be extracted from numerical libraries and combining them with architectural features, we developed a generic approach to solve dense linear systems on various architectures including CPU and GPU. We have then extended our work with an example of a least squares solver based on semi-normal equations in mixed precision that cannot be found in current libraries. For the automatically generated solvers, we report performance comparisons with state-of-the-art codes, and show that it is possible to obtain a generic code with a high-level interface (similar to MATLAB) which runs either on CPU or GPU without generating a significant overhead.

关键词： GPU computing Metaprogramming Numerical libraries active libraries dense linear systems mixed precision algorithms Solvers Linear system core construction Linear algebra Libraries central processing units ARCHITECTURE metaprogram Codes GRAPPER PICK UP Graphics processing Unit parallel architectures Dense MATLAB

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Large-scale distributed dependent nonparametric trees 32

Large-scale distributed dependent nonparametric trees

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32nd international Conference on Machine Learning, ICML 2015

作者： Hu, Zhiting Ho, Qirong Dubey, Avinava Xing, Eric P. Language Technologies Institute Carnegie Mellon University United States Institute for Infocomm Research ASTAR Singapore Machine Learning Department Carnegie Mellon University United States

ISBN: (纸本)9781510810587

Practical applications of Bayesian nonparametric (BNP) models have been limited, due to their high computational complexity and poor scaling on large data. In this paper, we consider dependent nonparametric trees (DNTs), a powerful infinite model that captures time-evolving hierarchies, and develop a large-scale distributed training system. Our major contributions include: (1) an effective memoized variational inference for DNTs, with a novel birth-merge s-trategy for exploring the unbounded tree space;(2) a model-parallel scheme for concurrent tree growing/pruning and efficient model alignmen-t, through conflict-free model partitioning and lightweight synchronization;(3) a data-parallel scheme for variational parameter updates that allows distributed processing of massive data. Using 64 cores in 36 hours, our system learns a lOK-node DNT topic model on 8M documents that captures both high-frequency and long-tail topics. Our data and model scales are orders-of-magnitude larger than recent results on the hierarchical Dirichlet process, and the near-linear s-calability indicates great potential for even bigger problem sizes. Copyright © 2015 by the author(s).

关键词： Data handling

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Pillow talks: Bits over communications in WLAN 2

Pillow talks: Bits over communications in WLAN

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2nd Workshop for Mobile Sensing, Computing and Communication, MSCC 2015

作者： Dong, Xuan Zhu, Chunsheng Lv, Shaohe Wang, Lei Wang, Xiaodong Leung, Victor C.M. National Laboratory of Parallel and Distributed Processing National University of Defense Technology United States Department of Electrical and Computer Engineering University of British Columbia Canada Department of Network Engineering Dalian University of Technology China

ISBN: (纸本)9781450335188

Improving the channel utilization is a significant issue to enhance the performance in WLAN. This paper presents, Pillow Talks MAC (PT-MAC), a novel spectrum sharing mechanism to create an extra channel (pt-channel) for client- to-client transmission in WLAN. Obviously, The basic chal- lenge in our proposal is how pt-channel works concurrently and transparently with AP transmissions. To address the issue, in PT-MAC, selected clients are allowed to contend for pt-channel during uplink transmissions and talk freely under the ongoing downlink transmissions. Based on ad- vanced physical techniques, pt-channel transmitter receives and forwards the AP signals simultaneously within OFDM cyclic prefix, resulting in mixed signals are overheard by re- ceiver in pt-channel. However, the mixed message can be re- covered by pt-decoding system. Simulations and analytical results show that, PT-MAC reuses the spectrum in WLAN and achieves a throughput gain as large as 92% with similar fairness, when the ratio of downlink traffic is about 80%. © 2015 ACM.

关键词： Wireless local area networks (WLAN)

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Fast Epistasis Detection in Large-Scale GWAS for Intel Xeon Phi Clusters 14

Fast Epistasis Detection in Large-Scale GWAS for Intel Xeon ...

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13th IEEE international symposium on parallel and distributed processing with applications

作者： Luecke, Glenn R. Weeks, Nathan T. Groth, Brandon M. Kraeva, Marina Ma, Li Kramer, Luke M. Koltes, James E. Reecy, James M. Iowa State Univ Dept Math Ames IA 50011 USA Iowa State Univ IT Serv Acad Technol Ames IA 50011 USA Univ Maryland Dept Anim & Avian Sci College Pk MD 20742 USA Iowa State Univ Dept Anim Sci Ames IA 50011 USA

ISBN: (纸本)9781467379526

epiSNP is a program for identifying pairwise single nucleotide polymorphism (SNP) interactions (epistasis) that affect quantitative traits in genome-wide association studies (GWAS). A parallel MPI version (EPISNPmpi) was created in 2008 to address this computationally-expensive analysis on data sets with many quantitative traits and markers. However, the explosion in genome sequencing will lead to the creation of large-scale data sets that will overwhelm EPISNPmpi's ability to compute results in a reasonable amount of time. Thus, epiSNP was rewritten to efficiently handle these large data sets. This was accomplished by performing serial optimizations, improving MPI load balancing, and introducing parallel OpenMP directives to further enhance load balancing and allow execution on the Intel Xeon Phi coprocessor (MIC). These additions resulted in new scalable versions of epiSNP using MPI, MPI+ OpenMP, and MPI+ OpenMP with one or two MICs. For a large 774,660 SNP data set with 1,634 individuals, the runtime on 126 nodes of TACC's Stampede Supercomputer was 10.61 minutes without MICs, and 5.13 minutes with 2 MICs. This translated to speedups over EPISNPmpi of 17X without MICs, and 36X with 2 MICs.

关键词： Xeon Phi coprocessor epistasis GWAS MPI OpenMP parallel Computing

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2W-FD: A Failure Detector Algorithm with QoS

2W-FD: A Failure Detector Algorithm with QoS

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international symposium on parallel and distributed processing (IPDPS)

作者： Alejandro Tomsic Pierre Sens João Garcia Luciana Arantes Julien Sopena Sorbonne Universites UPMC Univ Inria Paris France Instituto Superior Técnico Universidade de Lisboa

Failure detection plays a central role in the engineering of distributed systems. Furthermore, many applications have timing constraints and require failure detectors that provide quality of service (QoS) with some quantitative timeliness guarantees. Therefore, they need failure detectors that are fast and accurate. We introduce the Two-Windows Failure Detector (2W-FD), an algorithm able to react to sudden changes in network conditions, property that currently existing algorithms do not satisfy. We ran tests on real traces and compared the 2W-FD to state-of-art algorithms. Our results show that our algorithm presents the best performance in terms of speed and accuracy in unstable scenarios.

关键词： Detectors Heart beat Quality of service Estimation Computers Delays

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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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