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# Introduction

Which of the following statements is true?

• Users want ``black box'' software that they can use with complete confidence for general problem classes without having to understand the fine algorithmic details.
• Users want to be able to tune data structures for a particular application, even if the software is not as reliable as that provided for general methods.
It turns out both are true, for different groups of users.

Traditionally, users have asked for and been provided with black box software in the form of mathematical libraries such as LAPACK, LINPACK, NAG, and IMSL. More recently, the high-performance community has discovered that they must write custom software for their problem. Their reasons include inadequate functionality of existing software libraries, data structures that are not natural or convenient for a particular problem, and overly general software that sacrifices too much performance when applied to a special case of interest.

Can we meet the needs of both groups of users? We believe we can. Accordingly, in this book, we introduce the use of templates. A template is a description of a general algorithm rather than the executable object code or the source code more commonly found in a conventional software library. Nevertheless, although templates are general descriptions of key algorithms, they offer whatever degree of customization the user may desire. For example, they can be configured for the specific data structure of a problem or for the specific computing system on which the problem is to run.

We focus on the use of iterative methods for solving large sparse systems of linear equations.

Many methods exist for solving such problems. The trick is to find the most effective method for the problem at hand. Unfortunately, a method that works well for one problem type may not work as well for another. Indeed, it may not work at all.

Thus, besides providing templates, we suggest how to choose and implement an effective method, and how to specialize a method to specific matrix types. We restrict ourselves to iterative methods, which work by repeatedly improving an approximate solution until it is accurate enough. These methods access the coefficient matrix of the linear system only via the matrix-vector product (and perhaps ). Thus the user need only supply a subroutine for computing (and perhaps ) given , which permits full exploitation of the sparsity or other special structure of .

We believe that after reading this book, applications developers will be able to use templates to get their program running on a parallel machine quickly. Nonspecialists will know how to choose and implement an approach to solve a particular problem. Specialists will be able to assemble and modify their codes-without having to make the huge investment that has, up to now, been required to tune large-scale applications for each particular machine. Finally, we hope that all users will gain a better understanding of the algorithms employed. While education has not been one of the traditional goals of mathematical software, we believe that our approach will go a long way in providing such a valuable service.

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