Fully-parallel execution of a high-level data-parallel language based on nested sequences, higher order functions and generalized iterators can be realized in the vector model using a suitable representation of nested sequences and a small set of transformational rules to distribute iterators through the constructs of the language.

This paper describes our experiences developing high-performance code for astrophysical N-body simulations. Recent N-body methods are based on an adaptive tree structure. The tree must be built and maintained across physically distributed memory; moreover, the communication requirements are irregular and adaptive. Together with the need to balance the computational work-load among processors, these issues pose interesting challenges and tradeoffs for high-performance implementation. Our implementation was guided by the need to keep solutions simple and general. We use a technique for implicitly representing a dynamic global tree across multiple processors which substantially reduces the programming complexity as well as the performance overheads of distributed memory architectures. The contributions include methods to vectorize the computation and minimize communication time which are theoretically and experimentally justified. The code has been tested by varying the number and distribution of bodies on different configurations of the Connection Machine CM-5. The overall performance on instances with 10 million bodies is typically over 48 percent of the peak machine rate, which compares favorably with other approaches.

Target Language In our methodology we have identified a small set of specifications that comprise the abstract target language (ATL) of the refinement system. These are specifications of types such as arrays, lists, tuples, integers, characters, etc., that commonly appear in programming languages. The refinement expresses a system as a definitional extension the ATL specs. Thus by associating a model---a concrete type in a specific programming language---with each ATL specification the complete system specification is compiled. 5.2.3 Proteus to DPL Translation The translation of Proteus to DPL consists of a series of major steps: 1. Expansion of iterator expressions into image and filter expressions. 2. Conversion to data-parallel form. 3. An interpretation of sequences into the nested sequence vocabulary of DPL. 4. Addition of storage management code. 5. Conversion into C. Source Mediating Target CORE-SEQ SEQ-AS-ARRAY ARRAY SEQ Component 1 System Component 2 CORE-SEQ SEQ-AS-ARRAY ...