Introduction to High Performance Scientific Computing

The field of high performance scientific computing lies at the crossroads of a number of disciplines and skill sets, and correspondingly, for someone to be successful at using high performance computing in science requires at least elementary knowledge of and skills in all these areas. Computations stem from an application context, so some acquaintance with physics and engineering sciences is desirable. Then, problems in these application areas are typically translated into linear algebraic, and sometimes combinatorial, problems, so a computational scientist needs knowledge of several aspects of numerical analysis, linear algebra, and discrete mathematics. An efficient implementation of the practical formulations of the application problems requires some understanding of computer architecture, both on the CPU level and on the level of parallel computing. Finally, in addition to mastering all these sciences, a computational scientist needs some specific skills of software management.

While good texts exist on numerical modeling, numerical linear algebra, computer architecture, parallel computing, performance optimization, no book brings together these strands in a unified manner. The need for a book such as the present became apparent to the author working at a computing center: users are domain experts who not necessarily have mastery of all the background that would make them efficient computational scientists. This book, then, teaches those topics that seem indispensible for scientists engaging in large-scale computations.

The contents of this book are a combination of theoretical material and self-guided tutorials on various practical skills. The theory chapters have exercises that can be assigned in a classroom, however, their placement in the text is such that a reader not inclined to do exercises can simply take them as statement of fact.