The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.

With the increasing availability of high-performance massively parallel computer systems, the prevalence of sophisticated scientific simulation has grown rapidly. The complexity of the scientific models being simulated has also evolved, leading to a variety of coupled multi-physics simulation codes. Such cooperating parallel programs require fundamentally new interaction capabilities, to efficiently exchange parallel data structures and collectively invoke methods across programs. So-called "M×N" research, as part of the Common Component Architecture (CCA) effort, addresses these special and challenging needs, to provide generalized interfaces and tools that support flexible parallel data redistribution and parallel remote method invocation. Using this technology, distinct simulation codes with disparate distributed data decompositions can work together to achieve greater scientific discoveries.

No abstract

High performance scientific applications are frequently multiphysics codes composed from single-physics programs, or have a functional decomposition based on physics as well as a domain decomposition for parallelism. In either case, dividing the application into independent components that can be developed and tested separately is useful. This requires a fast and efficient mechanism to share the large parallel data structures that are used in scientific applications. The "M × N problem" is the transfer of data between two scientific parallel programs with different numbers of processes on each side. We present a solution to the M × N problem based on an I/O message passing interface (MPI). In this solution, the client application reads and writes data using the standard MPI I/O, but instead of going to hard drives the data are streamed in parallel between module programs working together on an integrated simulation. This approach builds on existing technology, emphasizes the easy migration of current applications, and simplifies the unit testing of the components while maintaining parallel high performance throughout the application. It also does not require a component to know how many processes another communicating component has.

No abstract