Abstract

. We present a unified approach for expressing high performance numerical linear algebra routines for a class of dense and sparse matrix formats and shapes. As with the Standard Template Library [7], we explicitly separate algorithms from data structures through the use of generic programming techniques. We conclude that such an approach does not hinder high performance. On the contrary, writing portable high performance codes is actually enabled with such an approach because the performance critical code sections can be isolated from the algorithms and the data structures. 1 Introduction The traditional approach to writing basic linear algebra routines is a combinatorial affair. There are typically four precision types that need to be handled (single and double precision real, single and double precision complex), several dense storage types (general, banded, packed), a multitude of sparse storage types (the Sparse BLAS Standard Proposal includes 13 [1]), as well as row and column or...

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