While object-oriented programming is being embraced in industry, particularly in the form of C++ and to an increasing extent Java and Python, its acceptance by the parallel scientific programming community is still tentative. In this latter domain performance is invariably of paramount importance, where even the transition from FORTRAN~77 to C is incomplete, primarily because of real or perceived loss of performance. On the other hand, various factors practically dictate the use of language features that provide better paradigms for abstraction: increasingly complex numerical algorithms, application requirements, and hardware (e.g. deep memory hierarchies, numbers of processors, communication and I/O); and the need for user-level fault tolerance.
This workshop seeks to bring together practitioners and researchers in this growing field to `compare notes' on their work. The emphasis is on identifying specific problems impeding greater acceptance and widespread use of object-oriented programming in scientific computing; proposed and implemented solutions to these problems; and new or novel approaches, techniques or idioms for scientific and/or parallel computing. Presentations of work in progress are welcome.
Specific areas of interest include, but are not limited to:
* frameworks and tools for scientific object-oriented computing;
* tried or proposed programming language alternatives to C++
* performance issues and their realized or proposed resolution;
* issues specific to handling or abstracting parallelism;
* specific points of concern for progress and acceptance of
object-oriented scientific computing;
* existing, developing, or proposed software;
* schemes for user-level fault tolerance;
* grand visions (of relevance and substance).