Numerical computations are a fundamental tool for engineers and scientists. The current practice of science and engineering demands that nontrivial computations be performed with both great speed and great accuracy. More and more, one finds that scientific insight and technologial breakthroughs are preceded by intense computational efforts such as modeling and simulation. It is clear that computing is, and will continue to be, central to the further development of science and technology.

As market forces and technological breakthroughs lowered the cost of computational power by several orders of magintude, there was a natural migration from large-scale mainframes to powerful desktop workstations. Vector processing and parallelism became possible, and this parallelism gave rise to a new collection of algorithms. Parallel architectures matured, in part driven by the demand created by the algorithms. Large computational codes were modified to take advantage of these parallel supercomputers. Of course, the term supercomputer has referred, at various times, to radically different parallel architectures. This includes vector processors, various shared memory architectures, distributed memory clusters, and even computational grids. Although the landscape of scientific computing changes frequently, there is one constant; namely, that there will always be a demand in the research community for high-performance computing.

When computations are first introduced in beginning courses, they are often straightforward “vanilla” computations, which are well understood and easily done using standard techniques and/or commercial software packages on desktop computers. However, sooner or later, a working scientist or engineer will be faced with a problem that requires advanced techniques, more specialized software (perhaps coded from scratch), and/or more powerful hardware.