Developing Scientific Applications with Loosely-Coupled Sub-tasks
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This paper presents the design and implementation of a grid-computing environment for mining sequential patterns. An Apriori-like algorithm for mining sequential patterns is deployed in the proposed grid-computing environment. Apriori-like algorithm is not of very high performance in comparison to others but it is more convenient to be realized for distributed processing in a grid computing environment due to its loosely coupled processes. Two types of grids are designed, the computing grid and data grid, in the proposed environment. All grids are installed with full functions, each of which is wrapped by Globus toolkit. Grid services are invoked by the users or other grids and able to respond to the invoking side. There are 10 computers serving as grid nodes each of which is equipped with different hardware components and is distributed on two campuses. The experimental results show that the proposed grid-computing environment provides a flexible and efficient platform for mining sequential patterns from large datasets.
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Grid computing enables massive scale computation with resources that are geographically dispersed over large area. With the advent of Grid Computing, computer resources of different administrative domains can be connected towards a common goal, such as performing high computing. In addition, it is useful for students, educators, and researchers to perform their experiments on the grid and use its tremendous computation potential to a great extent. One such effort regards creating a computational grid to benefit the users of the scientific and distributed computing community at BGSU. This paper presents the architectural details and the steps needed for the development of a working grid for educational purposes. It presents the actions needed to setup the infrastructure for the grid creation. It explains how to create virtual clusters, configure them, and add them to the grid. It also explains the steps needed to install the Globus toolkit which provides the communication channel between all the clusters in the grid. Finally, the paper provides suggestions to make this grid even more flexible and improved high computation tasks.
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Collaborative research has facilitated the development of distributed systems that provide users nontrivial access to geographically dispersed resources that are administered in multiple computer domains. The term grid computing is popularly used to refer to such distributed systems. Scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates grid technology in the context of Commercial Simulation Packages (CSPs). Towards this end, the paper identifies (a) six CSP-specific grid services, (b) identifies grid middleware that could be used to provide the CSP-specific grid services, and (c) list CSPs that include vendor-specific solutions for these grid services. The authors hope that this research will lead to an increased awareness of the potential of grid computing among simulation end users and CSP vendors.
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Collaborative research has facilitated the development of distributed systems that provide users nontrivial access to geographically dispersed resources that are administered in multiple computer domains. The term grid computing is popularly used to refer to such distributed systems. Scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates grid technology in the context of Commercial Simulation Packages (CSPs). Towards this end, the paper identifies (a) six CSP-specific grid services, (b) identifies grid middleware that could be used to provide the CSP-specific grid services, and (c) list CSPs that include vendor-specific solutions for these grid services. The authors hope that this research will lead to an increased awareness of the potential of grid computing among simulation end users and CSP vendors.
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Grid computing has been developed extensive in recently years and is becoming an important platform for high performance computing in scientific areas. Grid performance evaluation is an important approach to improve the performance of grid systems and applications. However, few work has been conducted in grid performance evaluation due to a lot of reasons like lack, of appropriate grid performance metrics, complexity of the grids, etc. In this paper, we analyze the performance metrics like response time and system utilization in the computational grid environment. We argue that instead of calculating the system utilization in the traditional, a concept of relative grid utilization, which describes how close is a single grid application performance to the performance of the same application submitted without grid middleware. We also discuss the utilization for the grid systems processing a number of jobs. In our experiments, we use NPB/NGB to evaluate the APSTC cluster grid and NTU campus grid performance, especially the overhead of SGE and Globus. Our results show that the overhead of grid middleware turns to be ignorable when the job size grows, and the characteristics of the grid applications affect a lot on utilization of computational grids.
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