Abstract We present case studies that apply generic programming to the development of high‐performance parallel code for solving two archetypal partial differential equations (PDEs). We examine the overall structure of the example scientific codes and consider their generic implementation. With a generic approach it is a straightforward matter to reuse software components from different sources; implementations with components from the Iterative Template Library (ITL), the Matrix Template Library (MTL), Blitz++, A++/P++, and Fortran BLAS are presented. Our newly developed Generic Message Passing library is used for communication. We compare the generic implementations with equivalent implementations developed with alternative libraries and languages and discuss performance as well as software engineering issues. Copyright © 2005 John Wiley & Sons, Ltd.

Summary form only given. We present a case study of solving very large sparse linear systems in end-to-end accelerator structure simulations. Both direct solvers and iterative solvers are investigated. A parallel multilevel preconditioner based on hierarchical finite element basis functions is considered and has been implemented to accelerate the convergence of iterative solvers. A linear system with matrix size 93,147,736 and with 3,964,961,944 nonzeros from 3D electromagnetic finite element discretization has been solved in less than 8 minutes with 1024 CPUs on the NERSC IBM SP. The resource utilization as well as the application performance for these solvers is discussed.

In this paper we present the Generic Graph Component Library (GGCL), a generic programming framework for graph data structures and graph algorithms. Following the theme of the Standard Template Library (STL), the graph algorithms in GGCL do not depend on the particular data structures upon which they operate, meaning a single algorithm can operate on arbitrary concrete representations of graphs. To attain this type of flexibility for graph data structures, which are more complicated than the containers in STL, we introduce several concepts to form the generic interface between the algorithms and the data structures: Vertex, Edge, Visitor, andDecorator. We describe the principal abstractions comprising the GGCL, the algorithms and data structures that it provides, and provide examples that demonstrate the use of GGCL to implement some common graph algorithms. Performance results are presented which demonstrate that the use of novel lightweight implementation techniques and static polymorphism in GGCL results in code which is significantly more efficient than similar libraries written using the objectoriented paradigm. Submitted as a Research Paper to OOPSLA99. Subject Areas: frameworks, design patterns