The game of life, a cellular automation model, is based on the principles of emergence and self-organization. It is a zero-player game that plays out on the basis of its initial state only with the help of some simple...
ISBN:
(数字)9781728141428
ISBN:
(纸本)9781728141435
The game of life, a cellular automation model, is based on the principles of emergence and self-organization. It is a zero-player game that plays out on the basis of its initial state only with the help of some simple rules. It is an infinite computational process which may generate complex patterns over time. In this paper, we propose and compare 2 different methods along with their architectures to optimize the functioning of the existing method. The first method uses OpenMP which works on the fundamentals of shared memory architecture whereas the second method uses MPI which in turn uses message passing techniques on distributed memory systems.
MapReduce is with no doubt the parallel computation paradigm which has managed to interpret and serve at best the need, expressed in any field, of running fast and accurate analyses on Big Data. The strength of MapRed...
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Vectorial representations of meaning can be supported by empirical data from diverse sources and obtained with diverse embedding approaches. This paper aims at screening this experimental space and reports on an asses...
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Dimensionality reduction algorithms are often used to visualize multi-dimensional data, which are mostly non-parametric. Non-parametric methods do not provide any explicit intuition for adding new data points into an ...
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ISBN:
(纸本)9781728145358
Dimensionality reduction algorithms are often used to visualize multi-dimensional data, which are mostly non-parametric. Non-parametric methods do not provide any explicit intuition for adding new data points into an existing environment which limits the applicability of visualization for Big Data scenario. The LION-tSNE (Local Interpolation with Outlier coNtrol t-distributed Stochastic Neighbor Embedding) method was proposed to overcome the limitations of existing techniques. The LION-tSNE algorithm uses random sampling method for tSNE model design which creates an initial visual environment then new data points are added to this environment using local-IDW(Inverse Distance Weighting) interpolation method. The randomly selected sample data often suffer from non-representativeness of the whole data which creates inconsistency in the tSNE environment. To overcome this problem two new sampling methods are proposed which are based on k-NN (k-Nearest Neighbor) graph update properties. It is empirically shown that proposed methods outperform existing LION-tSNE method with 0.5 to 2% more k-NN accuracy and results are more consistent. The study is done on five differently characterized datasets with three different initial solutions of tSNE. The proposed method results are statistically significant which is done by statistical method pairwise t-test.
In this paper, a model used for predicting icing thickness of power transmission lines is proposed. An algorithm derived from parallel coordinates is applied to convert the high-dimensional source data, which includes...
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The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today's manufacturing systems and increased the growth of data generated from manufacturing. B...
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ISBN:
(数字)9781728180410
ISBN:
(纸本)9781728180427
The IoT technologies, big data analytics, cloud computing, and artificial intelligence advances fundamentally impacted today's manufacturing systems and increased the growth of data generated from manufacturing. Big data as a driver key of intelligent manufacturing empowered companies to adopt data driven approaches to enhance competitiveness, efficiency and sustainability. In this paper a historical evolution of data in manufacturing is overviewed, the smart manufacturing its key technologies and sustainability are related, and a conceptual framework from lifecycle product perspective was proposed to show potential applications of big data analytics in sustainable smart manufacturing.
The proceedings contain 77 papers. The topics discussed include: ELS: an hard real-time scheduler for homogeneous multi-core platforms;MC-RPL: a new routing approach based on multi-criteria RPL for the Internet of thi...
ISBN:
(纸本)9781728150758
The proceedings contain 77 papers. The topics discussed include: ELS: an hard real-time scheduler for homogeneous multi-core platforms;MC-RPL: a new routing approach based on multi-criteria RPL for the Internet of things;Persian sentiment lexicon expansion using unsupervised learning methods;a case study for presenting bank recommender systems based on bon card transaction data;performance evaluation of classification data mining algorithms on coronary artery disease dataset;DMap: a distributed blockchain-based framework for online mapping in smart city;and a novel parallel jobs scheduling algorithm in the cloud computing.
Grid computing offers significant platform of technologies where complete computational potential of resources could be harnessed in order to solve a complex problem. However, applying mining approach over distributed...
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File replication is widely used to reduce file transfer times and improve data availability in large distributed systems. Replication techniques are often evaluated through simulations, however, most simulation platfo...
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ISBN:
(纸本)9783030105495;9783030105488
File replication is widely used to reduce file transfer times and improve data availability in large distributed systems. Replication techniques are often evaluated through simulations, however, most simulation platform models are oversimplified, which questions the applicability of the findings to real systems. In this paper, we investigate how platform models influence the performance of file replication strategies on large heterogeneous distributed systems, based on common existing techniques such as prestaging and dynamic replication. The novelty of our study resides in our evaluation using a realistic simulator. We consider two platform models: a simple hierarchical model and a detailed model built from execution traces. Our results show that conclusions depend on the modeling of the platform and its capacity to capture the characteristics of the targeted production infrastructure. We also derive recommendations for the implementation of an optimized data management strategy in a scientific gateway for medical image analysis.
The performance of HPC clusters depends on efficient scheduling of jobs. However, modern schedulers generally lack real-time information about resource utilization and require users to provide information, which is se...
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ISBN:
(数字)9781728166773
ISBN:
(纸本)9781728166780
The performance of HPC clusters depends on efficient scheduling of jobs. However, modern schedulers generally lack real-time information about resource utilization and require users to provide information, which is seldom accurate, on job requirements. The problem is exacerbated as HPC systems become increasingly more complicated and heterogeneous, which gives rise to new resource constraints (GPU, parallel file system, network bandwidth, burst buffers, etc.) In this work, we integrated data from LDMS, the Lightweight distributed Metric Service, with Slurm, a popular job scheduler. To demonstrate the capabilities of such integration, we enabled scheduling based on the Lustre file system throughput. We demonstrated benefits of measurement of real-time utilization, prediction of applications requirements from historical data, and finer control of resources, in a preliminary evaluation of scheduling on a cluster of virtual machines. We also identified the possibility of further improving the scheduling efficiency through workload-adaptive scheduling, by adjusting the scheduling based on characteristics of the pending job. We validated the feasibility of this strategy by simulating job executions in our custom-made HPC cluster simulator.
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