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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11322 条记录，以下是3831-3840 订阅

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GPU accelerated substructuring methods for sparse linear systems

GPU accelerated substructuring methods for sparse linear sys...

引用

第十五届分布式计算及其应用国际学术研讨会

作者： Abal-Kassim Cheik Ahamed Frédéric Magoulès CUDA Research Center & Applied Mathematics and Systems Laboratory CentraleSupélec Université Paris Saclay

ISBN: (纸本)9781509035946

In this paper,we present and analyze parallel substructuring methods based on conjugate gradient method,a iterative Krylov method,for solving sparse linear systems on *** experiments performed on a set of matrices coming from the finite element analysis of large scale engineering problems,show the efficiency and robustness of substructuring methods based on iterative Krylov method for solving sparse linear systems in a context of a hybrid multi-core-GPU.

关键词： Graphics processing units Computer architecture Instruction sets Linear systems Programming Central processing Unit Hardware

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Thrifty-malloc: A HW/SW codesign for the dynamic management of hardware transactional memory in embedded multicore systems

Thrifty-malloc: A HW/SW codesign for the dynamic management ...

引用

Proceedings of the international conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)

作者： Thomas Carle Dimitra Papagiannopoulou Tali Moreshet Andrea Marongiu Maurice Herlihy R. Iris Bahar University of Toulouse Toulouse France Brown University Providence RI USA Boston University Boston Massachusetts USA University of Bologna Bologna Italy

ISBN: (纸本)9781509035892

We present thrifty-malloc: a transaction-friendly dynamic memory manager for high-end embedded multicore systems. The manager combines modularity, ease-of-use and hardware transactional memory (HTM) compatibility in a lightweight and memory-effcient design. Thrifty-malloc is easy to deploy and configure for non-expert programmers, yet provides good performance with low memory overhead for highly-parallel embedded applications running on massively parallel processor arrays (MPPAs) or many-core architectures. In addition, the transparent mechanisms that increase our manager's resilience to unpredictable dynamic situations incur a low timing overhead in comparison to established techniques.

关键词： Instruction sets Memory management Synchronization Embedded systems Hardware Multicore processing Data structures

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VH-DSI: Speeding Up Data Visualization Via A Heterogeneous distributed Storage Infrastructure 21

VH-DSI: Speeding Up Data Visualization Via A Heterogeneous D...

引用

21st IEEE international conference on parallel and distributed Systems ICPADS

作者： Zhu, Yongqing Juniarto, Samsudin Shi, Haixiang Wang, Jun ASTAR Data Storage Inst Singapore Singapore Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9780769557854

Visualizing and analyzing large-scale datasets are both critical and challenging, as they require substantial resources for data processing and storage. While the speed of supercomputers continues to set higher standard, the I/O systems have not kept in pace, resulting in a significant performance bottleneck. To alleviate the I/O bottleneck for scientific visualization applications, we propose a Visualization via a Heterogeneous distributed Storage Infrastructure (VH-DSI) solution to improve I/O speed and accelerate overall visualization performance. VH-DSI replaces the traditional parallel file system with a distributed file system to support visualization applications. A new scheduling algorithm HeterSche is proposed in VH-DSI to assign computing tasks to data nodes with the consideration of cluster heterogeneity and data locality. VH-DSI also includes a design to support POSIX-IO for distributed file system. The performance evaluation has shown that the proposed VH-DSI solution can achieve significant performance improvement for visualization applications. Compared to the traditional visualization, the VH-DSI solution reduces the response time by at least 5 times. The HeterSche scheduling algorithm is capable to speed up visualization compared to other scheduling algorithms especially for large scale datasets.

关键词： Data Intensive Scientific Computing Scientific Visualization parallel Computing distributed Computing I/O Heterogeneous Cluster

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Compiler Support for Software Cache Coherence

Compiler Support for Software Cache Coherence

引用

international conference on High Performance Computing

作者： Sanket Tavarageri Wooil Kim Josep Torrellas P. Sadayappan The Ohio State University University of Illinois at Urbana-Champaign

ISBN: (纸本)9781509054121

The advent of multi-core processors with a large number of cores and heterogeneous architecture poses challenges for achieving scalable cache coherence. Several recent research efforts have focused on simplifying or abandoning hardware cache coherence protocols. However, this adds a significant burden on the programmer, unless automated compiler support is developed. In this paper, we develop compiler support for parallel systems that delegate the task of maintaining cache coherence to software. Algorithms to automatically insert software cache coherence instructions into parallel applications are presented. This frees the programmer from having to manually insert coherence annotations, which can be tedious and error-prone. Experimental evaluation over a number of benchmarks demonstrates that effective compiler techniques can make software cache coherence competitive with hardware coherence schemes both in terms of energy and performance.

关键词： Coherence Hardware Arrays Protocols Program processors Multicore processing

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Efficient Snapshot KNN Join processing for Large Data Using MapReduce

Efficient Snapshot KNN Join Processing for Large Data Using ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Yupeng Hu Chong Yang Cun Ji Yang Xu Xueqing Li State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Computer Science and Technology Shandong University P.R. China

ISBN: (纸本)9781509053827

The kNN join problem, denoted by R × KNN S, is to find the k nearest neighbors from a given dataset S for each point in the query set R. It is an operation required by many big data applications. As large volume of data are continuously generated in more and more real-life cases, we address the problem of monitoring kNN join results on data streams. Specifically, we are concerned with answering kNN join periodically at each snapshot which is called snapshot kNN join. Existing kNN join solutions mainly solve the problem on static datasets, or on a single centralized machine, which are difficult to scale to large data on data streams. In this paper, we propose to incrementally calculate the kNN join results of time t i from the results of snapshot t i-1 . Typically, for the data continuously generated on the data stream, we can get S i = S i-1 + ΔS i for the valid datasets of adjacent snapshots, where ΔS i denotes the updated points between time t i-1 and t i . Our basic idea is to first find the queries in R whose kNN results can be affected by the updated points in ΔS i , and then update the kNN results of these small part of queries respectively. In this way, we can avoid calculating the kNN join results on the whole dataset S i in time t i . We propose an implementation of searching for affected query points in MapReduce to scale to large volume of data. In brief, the mappers partition the datasets into groups, and the reducers search for affected queries separately on each group of points. Furthermore, we present the enhanced strategies of data partitioning and grouping to reduce the shuffling cost and computational cost. Extensive experiments on real-world datasets demonstrate that proposed methods are efficient, robust, and scalable.

关键词： Measurement distributed databases Computational efficiency Robustness Servers Indexes Algorithm design and analysis

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MACRON: The NoC-based Many-Core parallel processing Platform and its applications in 4G Communication Systems 23

MACRON: The NoC-based Many-Core Parallel Processing Platform...

引用

23rd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： Ling, Xiang Chen, Yiou Yu, Zhiliang Chen, Shihua Wang, Xiaodong Liang, Gui Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Peoples R China

ISBN: (纸本)9781479984909

The increasing demand of computation capacity has made many-core parallel processing (MPP) a compelling choice for computation-intensive applications. The networks-on-chip (NoC) architecture is an effective way to interconnect dozens of processing cores, while the logic circuits and the actual performance need to be verified in specific platform. We proposed and implemented the MACRON platform to provide verification for complicated applications based on NoC architecture by coordinating the software tool and the hardware devices closely. In MACRON, the virtual output queue with look-ahead routing is proposed to reduce the transmission delay through the NoC router. The heterogeneous processing elements: vector processor core, scalar processor core and accelerator core are designed, thus a thorough 'soft' signal processing can be approached. A real-time 4G wireless communication system based on NoC is demonstrated on this MACRON platform.

关键词： networks-on-chip many-core architecture FPGA digital signal process wireless communications vector processor

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High-Performance parallel Location-Aware Algorithms for Approximate String Matching on GPUs 21

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

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21st IEEE international conference on parallel and distributed Systems ICPADS

作者： Lin, Cheng-Hung Huang, Chun-Cheng Natl Taiwan Normal Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9780769557854

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 stateo-f-the-art approaches, the proposed high-degree parallelism achieves 11 to 105 times improvement.

关键词： approximate string matching bit-parallel algorithm graphic processing units Levenshtein distance nondeterministic finite automaton parallel algorithm

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A High Performance Computing Framework for Data Mining

A High Performance Computing Framework for Data Mining

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IEEE international conference on High Performance Computing Workshops (HiPCW)

作者： Navneet Goyal Sundar Balasubramaniam Poonam Goyal Saiyedul Islam Mohit Sati Department of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Pilani India

ISBN: (纸本)9781509057740

Mining large data sets is no longer the prerogative of computer scientists - specialists in a wide variety of domains are performing analytics as a day-to-day activity. Often such analyses are specific to the domain and analysts are required to devise new algorithms or techniques. For such scenarios, providing a high-level programming environment that delivers high performance on clusters is a challenge. We propose a framework that supports high-level programming using domain abstractions in data mining while delivering scalable performance on commodity clusters i.e. clusters of multi-core workstations. This framework includes a domain specific programming language, DWARF, to enable data mining specialists to rapidly prototype algorithms. DWARF is supported by a compiler that automatically parallelizes code by identifying domain specific patterns and translating them to parallel code that exploits data parallelism and task parallelism. The compiler generates code for a hybrid virtual machine supporting distributed memory model at the top level and shared memory model nested within. The code generated by the compiler can be scheduled on commodity clusters. We compare the proposed framework with other frameworks commonly used for data mining on distributed platforms.

关键词： Data mining DSL Clustering algorithms parallel processing Programming Algorithm design and analysis Sparks

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LCIndex: A Local and Clustering Index on distributed Ordered Tables for Flexible Multi-Dimensional Range Queries 44

LCIndex: A Local and Clustering Index on Distributed Ordered...

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44th Annual international conference on parallel processing Workshops (ICPPW)

作者： Feng, Chen Yang, Xi Liang, Fan Sun, Xian-He Xu, Zhiwei Chinese Acad Sci ICT SKL Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781467375887

A lot of Not Only SQL (NoSQL) databases have been proposed in the era of big data. distributed Ordered Table (DOT) is one kind of NoSQL database that has attracted lots of attention. It horizontally partitions table into regions and distributes regions to region servers according to the keys. Multi-Dimensional Range Query (MDRQ) is a common operation over DOTs. Many indexing techniques have been proposed to improve the performance of MDRQ, but they cannot guarantee high performance on both insert and flexible MDRQ at the same time. In this paper, we propose a novel indexing technique named LCIndex, short for Local and Clustering Index, to solve this issue. Experimental results confirm that LCIndex can achieve high performance on both insert operations and flexible MDRQ.

关键词： distributed database Indexing method Multidimensional range query

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Spark based distributed Deep Learning framework for Big Data applications

Spark based distributed Deep Learning framework for Big Data...

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international conference on Information Science and Communications Technologies (ICISCT)

作者： Akhmedov Khumoyun Yun Cui Lee Hanku Department of Internet&Multimedia Engineering Konkuk University Seoul Korea

ISBN: (纸本)9781509035472

Deep Learning architectures, such as deep neural networks, are currently the hottest emerging areas of data science, especially in Big Data. Deep Learning could be effectively exploited to address some major issues of Big Data, including withdrawing complex patterns from huge volumes of data, fast information retrieval, data classification, semantic indexing and so on. In this work, we designed and implemented a framework to train deep neural networks using Spark, fast and general data flow engine for large scale data processing. The design is similar to Google software framework called DistBelief which can utilize computing clusters with thousands of machines to train large scale deep networks. Training Deep Learning models requires extensive data and computation. Our proposed framework can accelerate the training time by distributing the model replicas, via stochastic gradient descent, among cluster nodes for data resided on HDFS.

关键词： Computational modeling Speech recognition Lead Speech parallel processing Sparks Neural networks

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