parallel programming

The map-reduce model is a method for executing large data applications. It is also a parallel programming model for writing applications that can be executed on the cloud. Organizations are increasingly producing data that is generated by business processes, user activities, website tracking, sensors, finance, accounting, and more. Data clustering algorithms are used as tools for analyzing large volumes of data. The main purpose of these algorithms is to categorize data into clusters so that the data objects in each cluster are more similar. In this paper, a dense hierarchical clustering algorithm, one of the data mining techniques, is implemented using map-reduce design and then the results of this algorithm are compared with the usual one. Experiments show that runtime decreases with increasing input data size. The runtime of the algorithm improved by 16.80% for the 200 data-point dataset, and 29.26% for the dataset with 1000 data points. The percentage of CPU usage in the parallel system also increased from 22% to 94%.

The era of big data is approaching with the rapid growth in development of complex information and communication technologies like internet and 3rd and 4th generation of Mobile Networks (3G/4G). These days more than ever, real-time and concurrent access to heterogeneous data from different sources and with different formats is made possible. Meanwhile, with recent advances in sensor technology and in order to monitor, explore and visualize complex spatial systems, a large amount of data in different Spatio-temporal scales is being generated every day. For example, according to the estimation by United Nations Initiative on Global Geospatial Information Management (UN-GGIM), 2.5 quintillion bytes of data are generated every day which large portion of the data is location-aware. This unprecedented trend of spatial data generation provides new opportunities for information and knowledge extraction, industrial development and business decision making. Although the big data brings new chances for scientific, business and engineering fields, it presents some challenges. To be more specific, storage, management, process and analysis of the spatial big data on traditional spatial information platforms is difficult and expensive. Such challenges affect modeling, analysis, simulation and concurrent access to spatial data. The need of real-time analysis in some applications like dynamic routing, fleet management or emergency management is also influenced by such limitations. In order to face spatial big data challenges, cloud computing technology to support spatial information applications appears to be very promising. Emergence of cloud computing technology provides an effective, scalable and affordable solution to big data challenges in spatial information application. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software. In this paper, we discussed fundamental theories and key technologies of cloud computing in storage, process and analysis of spatial big data. We have surveyed storage and management of big data using distributed file systems and NoSQL databases and made a comparison between different types of this databases. We also discussed recent trends and methods in parallel processing and big data analysis. MapReduce as a prominent parallel programming method and Hadoop as the most popular implementation of MapReduce are surveyed. We reviewed and made a comparison of spatial tools which is developed on cloud platforms. One of the most important challenges in spatial cloud computing that geospatial scientists is facing is spatial indexing and query processing. Due to distributed systems limits, developing dedicated spatial indexing and query processing techniques is needed. So we focused on novel spatial indexing methods and query processing technologies. As the case study, this paper surveys usage of cloud computing technologies in transportation, traffic and Intelligent Transportation Systems (ITS) and remote sensing and earth observations. The aim of this paper is reviewing and introducing fundamental theories, new technologies and recent trends of spatial big data to researchers of geospatial sciences.

Three dimensional turbulent flow in a stirred tank with moving blades has been numerically studied. For the numerical simulation, a 3D parallel flow solver has been developed based on the lattice Boltzmann method. The large eddy simulation method has been used for the turbulence modeling. To improve the accuracy of the flow simulation in stirred tanks, a commonly used boundary condition for moving walls as well as a proposed alternative boundary condition have been investigated in great details. The present investigations show that the proposed improved bounce back method generates accurate results compared with those obtained using commonly used force field method. However, the implementation of the proposed boundary condition is more difficult for cases with complex moving walls.