Within the scope of computational science and engineering, the standard graph coloring problem, the distance-1 coloring, is typically used to select independent sets on which subsequent parallel computations can be gu...
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ISBN:
(纸本)9783030106928;9783030106911
Within the scope of computational science and engineering, the standard graph coloring problem, the distance-1 coloring, is typically used to select independent sets on which subsequent parallel computations can be guaranteed. As graph coloring is an active field of research, various algorithms are available, each offering advantages and disadvantages. We compare several serial as well as parallel shared-memory graph coloring algorithms for the standard graph coloring problem based on reference graphs. Our investigation covers well established as well as recent algorithms and their support for balanced and unbalanced approaches. An overview on speedup, used number of colors, and their respective population for different test graphs is provided. It is shown that the parallel approaches produce similar results as the serial methods, but for specific cases the serial algorithms still remain a good option, when certain properties (e.g., balancing) are of major importance.
the system of systems is the perspective of multiple systems as part of a larger, more complex system. A system of systems usually includes highly interacting, interrelated and interdependent sub-systems that form a c...
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ISBN:
(纸本)9783030227449;9783030227432
the system of systems is the perspective of multiple systems as part of a larger, more complex system. A system of systems usually includes highly interacting, interrelated and interdependent sub-systems that form a complex and unified system. Maintaining the health of such a system of systems requires constant collection and analysis of the big data from sensors installed in the sub-systems. the statistical significance for machine learning (ML) and artificial intelligence (AI) applications improves purely due to the increasing big data size. this positive impact can be a great advantage. However, other challenges arise for processing and learning from big data. Traditional data sciences, ML and AI used in small- or moderate-sized analysis typically require tight coupling of the computations, where such an algorithm often executes in a single machine or job and reads all the data at once. Making a generic case of parallel and distributedcomputing for a ML/AI algorithm using big data proves a difficult task. In this paper, we described a novel infrastructure, namely collaborative learning agents (CLA) and the application in an operational environment, namely swarm intelligence, where a swarm agent is implemented using a CLA. this infrastructure enables a collection of swarms working together for fusing heterogeneous big data sources in a parallel and distributed fashion as if they are as in a single agent. the infrastructure is especially feasible for analyzing data from internet of things (IoT) or broadly defined system of systems to maintain its well-being or health. As a use case, we described a data set from the Hack the Machine event, where data sciences and ML/AI work together to better understand Navy's engines, ships and system of systems. the sensors installed in a distributed environment collect heterogeneous big data. We show how CLA and swarm intelligence used to analyze data from system of systems and quickly examine the health and maintenance issues a
CAPE, which stands for Checkpointing-Aided parallel Execution, is a framework that automatically translates and provides runtime functions to execute OpenMP programs on distributed-memory architectures based on checkp...
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Chatbot system attract huge interest in the recent years in many different fields in an attempt to increase the efficiency and shortens the business process execution time replacing the human-human communication with ...
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We compare the performance of traditional Gaussian elimination with a solver utilizing hierarchical compression of the matrix. the test problems are obtained by Boundary Element Method (BEM) simulation of laminar flow...
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ISBN:
(纸本)9783030106928;9783030106911
We compare the performance of traditional Gaussian elimination with a solver utilizing hierarchical compression of the matrix. the test problems are obtained by Boundary Element Method (BEM) simulation of laminar flow around airfoils. the most computationally expensive part of the BEM algorithm is to solve the arising system of linear algebraic equations. the related dense matrix can be compressed using a Hierarchically Semi-Separable (HSS) representation. this significantly lowers the computational complexity of the solution method, thus allowing faster overall execution. the performance of STRUMPACK library implementation of HSS and the MKL direct solver is compared on Intel Xeon architecture. At the end, we examine the accuracy of the HSS approximation using the (exact) results of Gaussian elimination as a reference solution.
Nowadays, we produce massive digital data using smartphones and need to share them with others. However, we feel tired of sharing our personal information repeatedly since we live in the society where the excessive pe...
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To model the behavior of nonlinear dynamical systems using wireless sensor networks (WSNs), the development of computation and energy efficient distributed modeling techniques is of crucial importance. In this work, V...
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ISBN:
(纸本)9781538644300
To model the behavior of nonlinear dynamical systems using wireless sensor networks (WSNs), the development of computation and energy efficient distributed modeling techniques is of crucial importance. In this work, Volterra model is considered for the modeling of nonlinear systems because of its significant modeling capability and generalized nonlinear structure. For real-time estimation of the Volterra model parameters, a simple distributed algorithm is designed for a particular framework of WSNs having predefined bridge sensor nodes. the pertinent cost function is expressed as an unconstrained minimization problem using a decomposable augmented Lagrangian form. To facilitate the distributed convex optimization, the augmented Lagrangian form is minimized using alternating direction method of multipliers. the communication and computational complexities involved in the proposed methodology are provided to show its effectiveness in the real-time applications over centralized and non-cooperative solutions. the convergence analysis is provided to guarantee the mean stability of the proposed algorithm. Simulation results obtained under the noisy environment are plotted to demonstrate the effective performance of the proposed algorithm.
We suggest an approach to optimize data mining in modern applicationsthat work on distributed data. We formally transform a high-level functional representation of a data-mining algorithm into a parallel implementati...
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Access control is a critical security service in Wireless Sensor Networks (WSNs). To prevent malicious nodes or unauthorized entity from joining the sensor network, access control is required. It restricts the network...
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