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Since simulation became the third pillar of scientific research, several forms of computers have become available to drive computer aided simulations, and nowadays, clusters are the most popular type of computers supporting these tasks. For instance, cluster settings, such as the so-called supercomputers, cluster of workstations (COW), cluster of desktops (COD), and cluster of virtual machines (COV) have been considered in literature to embrace a variety of scientific applications. However, those scientific applications categorized as high-performance computing (HPC) are conceptually restricted to be addressed only by supercomputers. In this aspect, we introduce the notions of cluster overhead and cluster coupling to assess the capacity of non-HPC systems to handle HPC applications. We also compare the cluster overhead with an existing measure of overhead in computing systems, the total parallel overhead, to explain the correctness of our methodology. The evaluation of capacity considers the seven dwarfs of scientific computing, which are well-known, scientific computing building blocks considered in the development of HPC applications. The evaluation of these building blocks provides insights regarding the strengths and weaknesses of non-HPC systems to deal with future HPC applications developed with one or a combination of these algorithmic building blocks. [ABSTRACT FROM AUTHOR]

Latest scientific application and simulation are requiring powerful computing resources. To get the sufficient resources, more and more computer experts are constructing high-end supercomputer, cluster computing or cloud computing. Although most corporations are starting to use high performance computing by organizing cluster, they rarely add some extra resources dynamically when computing power is not sufficient. In addition, they are also expensive to use insufficient resources as cloud computing resources. To overcome resource limitations when using cloud computing to conduct large computations, we exploit underutilized personal desktops by organizing a heterogeneous cluster environment. An open-source based scheduler, Son of Grid Engine (SGE) is provided in the integrated environment to manage or control the extra computing resources. Two kinds of groups are classified according to the individuals degree of utilization: a full-time group that provides resources at any time, and a custom-time group that provides resources only at designated times. We developed a resource manager that can turn on/off and schedule the SGE execution daemon; this manager performs as the core engine. The resource running time manager efficiently allocates and controls the members of affiliated computing resources. [ABSTRACT FROM AUTHOR]

Modeling and simulation (M&S) is a promising solution for manufacturers to verify and resolve errors in advance. Recently, M&S using high-performance computing has attracted attention as a technology trend for its efficiency in terms of the time and the cost. However, a huge amount of computing resources is still required to perform an M&S analysis workflow within a short time. For the resolution of this issue, we organize a heterogeneous cluster environment with individual nodes of the diverse performance and OS. An open source-based task scheduler named Son of Grid Engine is used for managing the heterogeneous resources for the experiment. To verify the efficiency and validity of the organization scheme, we have developed an environment for analyzing products and monitoring work through CalculiX, an open source-based finite element analysis solver. [ABSTRACT FROM AUTHOR]

Complex eScience and other sophisticated applications in the field of HPC imply new demands that queuing based resource management systems cannot meet. To guarantee Quality of Service and co-allocation in the Grid, planning based resource management systems implementing advance reservation are needed. These systems face new challenges as a planning based management system has to keep track of the jobs and reservations in the future. Additionally, during the negotiation process of incoming reservations, a good overview of the remaining, not-yet reserved capacity is needed-not only for the current allocation, but also for the whole book-ahead time. Therefore, the resource management problem becomes a two dimensional problem for advance reservations in this field. In this paper different data structures are investigated and discussed in order to fit to planning based resource management. As a result the benefits of using lists of resource allocation or free blocks are exposed. This general idea widely used to manage continuous resources is extended to cover not only the resource dimension but also the time dimension. The list of blocks approach is evaluated in a Grid level and a local resource management system for a computing cluster. The extensive simulations showed a better runtime and higher reservation success rate compared with the currently favored approach of a slotted time and the more sophisticated approach based on AVL trees. [ABSTRACT FROM AUTHOR]