Intelligence in Scientific Computing

Combining numerical techniques with ideas from symbolic computation and with methods incorporating knowledge of science and mathematics leads to a new category of intelligent computational tools for scientists and engineers. These tools autonomously prepare simulation experiments from high-level specifications of physical models. For computationally intensive experiments, they automatically design special-purpose numerical engines optimized to perform the necessary computations. They actively monitor numerical and physical experiments. They interpret experimental data and formulate numerical results in qualitative terms. They enable their human users to control computational experiments in terms of high-level behavioral descriptions.

Compiling Scientific Code Using Partial Evaluation

Scientists are faced with a dilemma: either they can write abstract programs that express their understanding of a problem, but which do not execute efficiently; or they can write programs that computers can execute efficiently, but which are difficult to write and difficult to understand. We have developed a compiler that uses partial evaluation and scheduling techniques to provide a solution to this dilemma.

Partial Evaluation for Scientific Computing: The Supercomputer Toolkit Experience

We describe the key role played by partial evaluation in the Supercomputer Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputer Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at M.I.T., and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.