Both the size and the resolution of images always were key topics in the graphical computing area. Especially, they become more and more relevant in the big data era. We can observe that often a huge amount of data is...
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Both the size and the resolution of images always were key topics in the graphical computing area. Especially, they become more and more relevant in the big data era. We can observe that often a huge amount of data is exchanged by medium/low bandwidth networks or yet, they need to be stored on devices with limited space of memory. In this context, the present paper shows the use of the Fractal method for image compression. It is a lossy method known by providing higher indexes of file reduction through a highly time consuming phase. In this way, we developed a model of parallel application for exploiting the power of multiprocessor architectures in order to get the Fractal method advantages in a feasible time. The evaluation was done with different-sized images as well as by using two types of machines, one with two and another with four cores. The results demonstrated that both the speedup and efficiency are highly dependent of the number of cores. They emphasized that a large number of threads does not always represent a better performance.
The Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Problem, with two or more constraints. It is an important optimization problem with many real-life applications. To solve this NP-h...
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The Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Problem, with two or more constraints. It is an important optimization problem with many real-life applications. To solve this NP-hard problem we use a metaheuristic algorithm based on ant colony optimization (ACO). Since several steps of the algorithm can be carried out concurrently, we propose a parallel implementation under the GPGPU paradigm (General Purpose Graphics Processing Units) using CUDA. To use the algorithm presented in this paper, it is necessary to balance the number of ants, number of rounds used, and whether local search is used or not, depending on the quality of the solution desired. In other words, there is a compromise between time and quality of solution. We obtained very promising experimental results and we compared our implementation with those in the literature. The results obtained show that ant colony optimization is a viable approach to solve MKP efficiently, even for large instances, with the parallel approach.
Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall's algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more a...
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Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-Warshall's algorithms are limited to small networks due to computational complexity and cost. We propose an efficient and a more accurate approximation algorithm that is applicable to large scale networks. Our algorithm iteratively constructs levels of hierarchical networks by a node condensing procedure to construct hierarchical graphs until threshold. The shortest paths between nodes in the original network are approximated by considering their corresponding shortest paths in the highest hierarchy. Experiments on real life data show that our algorithm records high efficiency and accuracy compared with other algorithms.
The main goal of the paper is to present an analysis of the two baseband signal correlators. Correlation function is one of the fundamental tools being used in navigation radio signals processing. Among others it reso...
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ISBN:
(纸本)9781538603659
The main goal of the paper is to present an analysis of the two baseband signal correlators. Correlation function is one of the fundamental tools being used in navigation radio signals processing. Among others it resolves signals for their mutual time delay which helps measure navigation parameters as DOA, TDOA or range. Therefore the importance of the correlator and effectiveness of its implementation increases. Powerful analytical method, correlation function can be tremendously time consuming to compute if pure sequential implementation and long signals with huge number of samples meet each other. The paper tries to scrutinize process of design of the correlator from perspective of its efficient implementation. Type of the implementation depends on the purpose of the correlator and its role in the system. There are explained details of both searching and tracking correlators. Couple of numerical experiments is presented to compare sequential schema of implementation in a CPU vs its parallel form in GPU processor.
Large-scale graph analysis or also called network analysis of networks is supported by different algorithms, among the most relevant are PageRank (Web page ranking), Betweenness centrality (centrality in a graph) and ...
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ISBN:
(纸本)9781538631232
Large-scale graph analysis or also called network analysis of networks is supported by different algorithms, among the most relevant are PageRank (Web page ranking), Betweenness centrality (centrality in a graph) and Community Detection, these by of their complexity and the large amount of data that process diverse applications, increasingly need to use computational resources such as processor, memory and storage, for these reasons, it is necessary to apply high performance computing or HPC (High Performance Computing) but it would not be useful to apply HPC without having designed these algorithms in parallel programming, in this part there have been many studies on its application and methodologies to do it. The purpose of this work is to create a framework that allows computer science students to abstract a computer system based on the parallel programming paradigm, which implies that students to get acquainted with the resolution of algorithmic problems in a more natural way and away from the typical sequential thinking., The development of a graph analysis design pattern oriented to parallel programming in HPC, complemented with the design of didactic learning techniques in the network such as laboratories and/or simulators are key in the development of this framework.
Both the size and the resolution of images always were key topics in the graphical computing ***,they become more and more relevant in the big data *** can observe that often a huge amount of data is exchanged by medi...
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Both the size and the resolution of images always were key topics in the graphical computing ***,they become more and more relevant in the big data *** can observe that often a huge amount of data is exchanged by medium/low bandwidth networks or yet,they need to be stored on devices with limited space of *** this context,the present paper shows the use of the Fractal method for image *** is a lossy method known by providing higher indexes of file reduction through a highly time consuming *** this way,we developed a model of parallel application for exploiting the power of multiprocessor architectures in order to get the Fractal method advantages in a feasible *** evaluation was done with different-sized images as well as by using two types of machines,one with two and another with four *** results demonstrated that both the speedup and efficiency are highly dependent of the number of *** emphasized that a large number of threads does not always represent a better performance.
The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platf...
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The Process Networks (PNs) is a suitable parallel model of computation (MoC) used to specify embedded streaming applications in a parallel form facilitating the efficient mapping onto embedded parallel execution platforms. Unfortunately, specifying an application using a parallel MoC is a very difficult and highly error-prone task. To overcome the associated difficulties, we have developed the pn compiler, which derives specific Polyhedral Process Networks (PPN) parallel specifications from sequential static affine nested loop programs (SANLPs). However, there are many applications, for example, multimedia applications (MPEG coders/decoders, smart cameras, etc.) that have adaptive and dynamic behavior which cannot be expressed as SANLPs. Therefore, in order to handle dynamic multimedia applications, in this article we address the important question whether we can relax some of the restrictions of the SANLPs while keeping the ability to perform compile-time analysis and to derive PPNs. Achieving this would significantly extend the range of applications that can be parallelized in an automated way. The main contribution of this article is a first approach for automated translation of affine nested loop programs with dynamic loop bounds into input-output equivalent Polyhedral Process Networks. In addition, we present a method for analyzing the execution overhead introduced in the PPNs derived from programs with dynamic loop bounds. The presented automated translation approach has been evaluated by deriving a PPN parallel specification from a real-life application called Low Speed Obstacle Detection (LSOD) used in the smart cameras domain. By executing the derived PPN, we have obtained results which indicate that the approach we present in this article facilitates efficient parallel implementations of sequential nested loop programs with dynamic loop bounds. That is, our approach reveals the possible parallelism available in such applications, which allows for the utili
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