While object-oriented programming has been embraced in industry, particularly in the form of C++, Java, and Python, its acceptance by the parallel scientific programming community is for various reasons incomplete. Nonetheless, various factors practically dictate the use of language features that provide higher level abstractions than do C or older FORTRAN standards. These include increasingly complex physics models, numerical algorithms, and hardware (e.g. deep memory hierarchies, ever-increasing numbers of processors, and the advent of multi- and many-core processors and heterogeneous architectures). Our emphases are 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 frameworks, approaches, techniques, or idioms for parallel/high-performance object-oriented scientific computing.
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