Torus network has become increasingly important to multicomputer design because of its many desirable properties including low bandwidth and fixed degree of nodes. Also, torus networks can be partitioned into mesh net...
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Torus network has become increasingly important to multicomputer design because of its many desirable properties including low bandwidth and fixed degree of nodes. Also, torus networks can be partitioned into mesh networks. The multicast pattern, in which one source node sends the same message to multiple destination nodes, is the essential pattern in a wide variety of applications. This paper proposes a multicast routing scheme in 2D torus networks. The proposed scheme is a tree-based Algorithm which Splits torus Networks into two Equally Meshes, hence it is called TASNEM. TASNEM algorithm is a tree-based technique, in which the router simultaneously sends incoming flits on more than one outgoing channel. It requires at most two start-up times, one for each mesh subnetwork. For each mesh subnetwork, the source node delivers a message to the destination nodes along one main path and different horizontal paths branched from the main path. TASNEM algorithm can achieve high degree of parallelism and low communication latency over a wide range of traffic loads. Performance results of a simulation study on torus networks are discussed to compare TASNEM algorithm with some previous algorithms. (C) 2015 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license
Undersaturated oil viscosity is a dominant fluid parameter to be measured in oil reservoirs due to its direct involvement in flow calculations. Since PVT experimental work is expensive and time costly, prediction meth...
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Undersaturated oil viscosity is a dominant fluid parameter to be measured in oil reservoirs due to its direct involvement in flow calculations. Since PVT experimental work is expensive and time costly, prediction methods are essential. In this work, viscosity data from in-house and literature measurements (500+ reports, 20,000+ data points) has been utilized for the first time to develop machine learning models predicting undersaturated oil viscosity using easy-to-get measurements. Several popular statistical metrics are used to judge the accuracy of each algorithm. Our goal is to introduce a complete workflow that demonstrates the integrity of the steps followed and guides in further research in predicting similar PVT properties. The workflow showcases the advantages of combining engineers expertise to the art of data driven models development, specifically on accuracy and ease of implementation, as well as their limitations.
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