Browsing by Subject "abstraction"
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Item Design, Implementation, and Formal Verification of On-demand Connection Establishment Scheme for TCP Module of MPICH2 Library(2012-10-19) Muthukrishnan, Sankara SubbiahMessage Passing Interface (MPI) is a standard library interface for writing parallel programs. The MPI specification is broadly used for solving engineering and scientific problems on parallel computers, and MPICH2 is a popular MPI implementation developed at Argonne National Laboratory. The scalability of MPI implementations is very important for building high performance parallel computing applications. The initial TCP (Transmission Control Protocol) network module developed for Nemesis communication sub-system in the MPICH2 library, however, was not scalable in how it established connections: pairwise connections between all of an application's processes were established during the initialization of the application (the library call to MPI_Init), regardless of whether the connections were eventually needed or not. In this work, we have developed a new TCP network module for Nemesis that establishes connections on-demand. The on-demand connection establishment scheme is designed to improve the scalability of the TCP network module in MPICH2 library, aiming to reduce the initialization time and the use of operating system resources of MPI applications. Our performance benchmark results show that MPI_Init in the on-demand connection establishment scheme becomes a fast constant time operation, and the additional cost of establishing connections later is negligible. The on-demand connection establishment between two processes, especially when two processes attempt to connect to each other simultaneously, is a complex task due to race-conditions and thus prone to hard-to-reproduce defects. To assure ourselves of the correctness of the TCP network module, we modeled its design using the SPIN model checker, and verified safety and liveness properties stated as Linear Temporal Logic claims.Item Technology Characterization Models and Their Use in Designing Complex Systems(2011-08-08) Parker, Robert ReedWhen systems designers are making decisions about which components or technologies to select for a design, they often use experience or intuition to select one technology over another. Additionally, developers of new technologies rarely provide more information about their inventions than discrete data points attained in testing, usually in a laboratory. This makes it difficult for system designers to select newer technologies in favor of proven ones. They lack the knowledge about these new technologies to consider them equally with existing technologies. Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project, both within and across organizational boundaries. However, existing set-based methods are limited in terms of how the sets are constructed and in terms of the representational capability of the sets. The goal of this research is to introduce and demonstrate new, more general set-based design methods that are effective for characterizing and comparing competing technologies in a utility-based decision framework. To demonstrate the new methods and compare their relative strengths and weaknesses, different technologies for a power plant condenser are compared. The capabilities of different condenser technologies are characterized in terms of sets defined over the space of common condenser attributes (cross sectional area, heat exchange effectiveness, pressure drop, etc.). It is shown that systems designers can use the resulting sets to explore the space of possible condenser designs quickly and effectively. It is expected that this technique will be a useful tool for system designers to evaluate new technologies and compare them to existing ones, while also encouraging the use of new technologies by providing a more accurate representation of their capabilities. I compare four representational methods by measuring the solution accuracy (compared to a more comprehensive optimization procedure's solution), computation time, and scalability (how a model changes with different data sizes). My results demonstrate that a support vector domain description-based method provides the best combination of these traits for this example. When combined with recent research on reducing its computation time, this method becomes even more favorable.