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Abstract. In recent years computers have been incorporated into large scale systems such as nuclear plant, flight control, and manufacturing systems. Such Computer Integrated Systems (CIS) normally consist of heterogeneous subsystems. The integration of heterogeneous subsystems requires that the subsystems be portable, inter-operable, and integrable at both software and hardware levels so that the integrated system should function properly. Objects and nets are proposed as the atomic elements of CIS's. An object is defined as a computational model of an arbitrary entity. Then three representation schemes of an object are introduced: algebraic, modular, and graphical. Two operations on objects, Composition and Union, are introduced as means of combining two objects into a larger one. As an application of this approach, a Computer Integrated Manufacturing (CIM) system is represented as a network of objects.

Abstract-For some classes of problems, NVIDIA CUDA abstraction and hardware properties combine with problem characteristics to limit the specific problem instances that can be effectively accelerated. As a real-world example, a twodimensional correlation-based template-matching MATLAB application is considered. While this problem has a well known solution for the common case of linear image filtering-small fixed templates of a known size applied to a much larger image-the application considered here uses large arbitrarilysized templates, up to 156-by-116 pixels, with small search spaces containing no more than 703 window positions per template. Our CUDA implementation approach employs template tiling and problem-specific kernel compilation to achieve speedups of up to 15 when compared to an optimized multi-threaded implementation running on a 3.33 GHz four core Intel Nehalem processor. Tiling the template enables exploiting the parallelism within the computation and shared memory usage. At the same time, problem-specific kernel compilation allows greater levels of adaptability than would otherwise be possible.