Preface.

This section provides an overview of several articles on deep computing or high-performance computing which are applied to problems that require significant computing resources in order to facilitate relatively complex decision-making in fields ranging from business to the sciences. The papers capture a few illustrations of deep computing that cut across traditional disciplines. Deep computing requires not only massive amounts of computing,but also the processing of vast amounts of data and the use of sophisticated means for the interpretation of results. New algorithmic approaches are being developed that take into account computer architecture in order to address problems of general interest and answer questions that have impact beyond just scientific inquiry. The paper by Crivelli and Head-Gordon discusses a load-balancing technique to exploit the use of parallel architectures for addressing protein structure problems. In their paper, Fann et al. investigate multiwavelet approaches and apply them to areas of computational chemistry, computational electromagnetics, and fluid dynamics. Newns et al. discuss new algorithmic techniques they have developed for improving magnetic recording simulation. The applicability of deep computing to astrophysics is illustrated by the paper by Lake et al. The authors apply new algorithms coupled with deep computing to advance our understanding of the N-body problem.