A Sparse Matrix Library in C++ for High Performance Architectures
Abstract:
We describe an object oriented sparse matrix library in C++ built upon the Level 3 Sparse BLAS proposal [5] for portability and performance across a wide class of machine architectures. The C++ library includes algorithms for various iterative methods and supports the most common sparse data storage formats used in practice. Besides simplifying the subroutine interface, the object oriented design allows the same driving code to be used for various sparse matrix formats, thus addressing many of the di culties encountered with the typical approach to sparse matrix libraries. We emphasize the fundamental design issues of the C++ sparse matrix classes and illustrate their usage with the preconditioned conjugate gradient (PCG) method as an example. Performance results illustrate that these codes are competitive with optimized Fortran 77. We discuss the impact of our design on elegance, performance, maintainability, portability, and robustness. 1
Citations
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