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The goal of this project is to improve the environment for computational
science through research in fundamental mathematical algorithms and development
of well-engineered general-purpose scientific software. This research combines
development and analysis of algorithms for particular mathematical problem
domains with the application and advancement of underlying methodologies such
as computer arithmetic, parallel computing, languages, software design, user
interfaces, documentation, testing, performance evaluation, and information
dissemination. The customers for our work in algorithms and in the
dissemination of software and related information include both NIST scientists
and engineers, as well as the science and engineering community at large. Our
work in testing and evaluation methodology is of particular interest to the
math software research community, as well as to developers of math software
products in the commercial sector.
Highlights of this year's activities include the following.
- A new joint project with the NIST Statistical Engineering Division on
Tools for Evaluating Mathematical and Statistical Software has been
initiated. Particular tasks in the areas of numerical linear algebra,
special function evaluation, and statistical software are being
undertaken. The Matrix Market, a visual database of test data for large
sparse matrix algorithms, is the first visible output of this project. Has
already generated positive feedback on its usefulness to the research
community; collaborations have begun with users of sparse matrix
technology, such as the Boeing Company, to include additional large-scale
test data in the collection. Work on the development of a Web-based
software testing service for special functions has also begun.
- Significant strides applying object-oriented software design to improve
portability and reuse of mathematical software have been taken. Several
demonstration software packages for core linear algebra operations in C++
have been released are are seeing widespread attention. This work has also
spawned interest in the development of a standardized set of basic linear
algebra software for elementary sparse matrix operations. This is being
undertaken in collaboration with Cray Research.
- We are undertaking several projects to develop algorithms, software and
tools for distributed parallel scientific computing. On scalar processors
algorithms exemplified by our highly successful MGGHAT package, which
solves partial differential equations using high order hierarchical-basis
adaptive multigrid methods, have proven extremely effective. Moving
computations such as these to distributed parallel achitectures is quite
difficult due to complex load balancing and data communications issues.
This year, the prototype of a new software package named PHAML was
completed which parallelizes such computations based upon a new
multigrid-based domain decomposition and refinement-tree-based load
balancing strategies. We continue to contribute to the development of
software development tools, such as the Parallel Applications Development
Environment (PADE), a joint project with the High Performance Systems and
Services Division and the NIST Physics Laboratory. In addition we provide
specialized support for use of the IBM SP2 parallel computer with
educational materials and utilities such as xllcreate.
- The Guide to Available Mathematical Software (GAMS) continues to see high
use by the science and engineering community. With more than 9,000 users
per month, we saw our one millionth and two millionth Web hits this
year. Digital's AltaVista search engine identifies more than 3,000 external
Web pages that link to the GAMS server, and the McKinley Group (a
commercial venture than rates Web pages for presentation and content)
awarded GAMS its 4-star (highest rating). We continue to investigate new
technologies for increasing the usefulness of scientific software
repositories, and this year we released a prototype of HotGAMS!, a
Java-enabled GAMS client which provides a new interface which is
more capable, portable and efficient.
Next: Compression Algorithms
Up: Technical Highlights
Previous: Modeling in the
Generated by [email protected] on Mon Aug 19 10:08:42 EDT 1996