This 4-Volume-Set, CCIS 0251 - CCIS 0254, constitutes the refereed proceedings of the internationalconference on Informatics Engineering and Information Science, ICIEIS 2011, held in Kuala Lumpur, Malaysia, in Novemb...
ISBN:
(数字)9783642253270
ISBN:
(纸本)9783642253263
This 4-Volume-Set, CCIS 0251 - CCIS 0254, constitutes the refereed proceedings of the internationalconference on Informatics Engineering and Information Science, ICIEIS 2011, held in Kuala Lumpur, Malaysia, in November 2011. The 210 revised full papers presented together with invited papers in the 4 volumes were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on e-learning, information security, software engineering, image processing, algorithms, artificial intelligence and soft computing, e-commerce, data mining, neural networks, social networks, grid computing, biometric technologies, networks, distributed and parallel computing, wireless networks, information and data management, web applications and software systems, multimedia, ad hoc networks, mobile computing, as well as miscellaneous topics in digital information and communications.
Large computing systems such as data centers are becoming the mainstream infrastructures for big data processing. As one of the key data operators in such scenarios, distributed joins is still challenging current tech...
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ISBN:
(纸本)9783319969831;9783319969824
Large computing systems such as data centers are becoming the mainstream infrastructures for big data processing. As one of the key data operators in such scenarios, distributed joins is still challenging current techniques since it always incurs a significant cost on network communication. Various advanced approaches have been proposed to improve the performance, however, most of them just focus on data skew handling, and algorithms designed specifically for communication reduction have received less attention. Moreover, although the state-of-the-art technique can minimize network traffic, it provides fine-grained optimal schedules for all individual join keys, which could result in obvious overhead. In this paper, we propose a new approach called LAS (Lightweight Locality-Aware Scheduling), which targets reducing network communication for large distributed joins in an efficient and effective manner. We present the detailed design and implementation of LAS, and conduct an experimental evaluation using large data joins. Our results show that LAS can effectively reduce scheduling overhead and achieve comparable performance on network reduction compared to the state-of-the-art.
In our paper we present an abstract object oriented runtime system that helps to develop scientific applications for new her erogenous architectures based on multi-node of multi-core processors enhanced with accelerat...
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This paper studies load balancing issues for classes of problems with certain bisection properties. A class of problems has alpha-bisectors if every problem in the class can be subdivided into two subproblems whose we...
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ISBN:
(纸本)3540649522
This paper studies load balancing issues for classes of problems with certain bisection properties. A class of problems has alpha-bisectors if every problem in the class can be subdivided into two subproblems whose weight (i.e. workload) is not smaller than an alpha-fraction of the original problem. It is shown that the maximum weight of a subproblem produced by Algorithm HF, which partitions a given problem into N subproblems by always subdividing the problem with maximum weight, is at most a factor of [1/alpha] . (1 - alpha)([1/alpha]-2) greater than the theoretical optimum (uniform partition). This bound is proved to be asymptotically tight. Two strategies to use Algorithm HF for load balancing distributed hierarchical finite element simulations are presented. For this purpose, a certain class of weighted binary trees representing the load of such applications is shown to have 1/4-bisectors. The maximum resulting load is at most a factor of 9/4 larger than in a perfectly uniform distribution in this case.
Practice shows that increasing the amount of instruction level parallelism (ILP) offered by an architecture (like adding instruction slots to VLIW instructions) does not necessary lead to significant performance gains...
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Practice shows that increasing the amount of instruction level parallelism (ILP) offered by an architecture (like adding instruction slots to VLIW instructions) does not necessary lead to significant performance gains. Instead, high hardware costs and inefficient use of this hardware may occur. Mapping embedded applications onto multiprocessor systems forms a very interesting extension to ILP. In this paper we describe our approach to the mapping of embedded programs written in ANSI C onto a pipeline of application specific processors. An efficient algorithm for functional pipelining of loops is presented. To validate its applicability the frequency tracking system is used as a case study. This typical embedded application is mapped onto a two-processor system delivering speedup of 1.88 in comparison with a highly optimized single core solution.
Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within...
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ISBN:
(纸本)9783030856656;9783030856649
Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual -Agent-, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models;thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs;thus, developers can efficiently code applications of any of the three envisaged computing scenarios - sense-process-actuate, streaming and batch processing - throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them.
distributed SPARQL query processing frameworks are categorized on the bases of query computation into relation, graph and hybrid based distributed query computing. By exploring the historical achievements under these ...
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In this paper we present some techniques for construction of two dimensional mapping arrays, Appropriate for signal processing, called systolic arrays. These type of array algorithms are implemented on VLSI arrays. Ta...
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Adaptive mesh refinement (AMR) applications to solve partial differential equations (PDE) are very challenging to scale efficiently to the petascale regime. We describe optimizations to the Chombo AMR framework that e...
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ISBN:
(纸本)9783642233975
Adaptive mesh refinement (AMR) applications to solve partial differential equations (PDE) are very challenging to scale efficiently to the petascale regime. We describe optimizations to the Chombo AMR framework that enable it to scale efficiently to petascale on the Cray XT5. We describe an example of a hyperbolic solver (inviscid gas dynamics) and an matrix-free geometric multigrid elliptic solver. Both show good weak scaling to 131K processors without any thread-level or SIMD vector parallelism. This paper describes the algorithms used to compress the Chombo metadata and the optimizations of the Chombo infrastructure that are necessary for this scaling result. That we are able to achieve petascale performance without distribution of the metadata is a significant advance which allows for much simpler and faster AMR codes.
The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third...
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