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Experiments are conducted for a number of randomly generated application graphs, as well as several real application graphs to verify the energy reduction and applicability of the proposed model and algorithms.
We assess and validate our method by running extensive simulations on both random graphs and actual application graphs.
The proposed two steps technique is compared with state-of-the-art approaches through rigorous simulations with synthetic and real application graphs.
In this paper, we propose a parallel, scalable, and memory-efficient MCE algorithm for distributed and/or shared memory high performance computing architectures, whose runtime scales linearly for thousands of processors on real-world application graphs with hundreds and thousands of nodes.
Individuals sharing data can view in application graphs their A1c level plotted against the distribution in their own state.
The figure shows box plots for number of papers published subsequent to application (graphs A and D), univariate distributions of the median number of citations per paper per year published prior to application (graphs B and E) and univariate distributions of the median number of citations per paper per year published subsequent to application (graphs C and F).
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This is due to the impacts of the critical paths of the application graph.
Although VAP-planar periodic graphs are used in many practical applications, graphs obtained from opportunistic communication are usually not VAP-planar graphs.
In the classical graph theory and some applications, graphs are generally provided in advance, or can at least be defined clearly.
Since in some applications graphs become larger and larger, a research branch has emerged which is concerned with the design and analysis of so-called symbolic algorithms for classical graph problems on OBDD-represented graph instances.
The extensive comparative evaluation studies for both randomly generated and some real-world applications graphs show that our scheduling algorithms are compelling in terms of enhancement of both system reliability and energy saving.
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