An apply-to-all construct is the key mechanism for expressing data-parallelism, but data-parallel programming languages like HPF and C* significantly restrict which operations can appear in the construct. Allowing arbitrary operations substantially simplifies the expression of irregular and nested data-parallel computations. The technique of flattening nested parallelism introduced by Blelloch, compiles data-parallel programs with unrestricted apply-to-all constructs into vector operations, and has achieved notable success, particularly with irregular data-parallel programs. However, these programs must be carefully constructed so that flattening them does not lead to suboptimal work complexity due to unnecessary replication in index operations. We present new flattening transformations that generate programs with correct work complexity. Because these transformations may introduce concurrent reads in parallel indexing, we developed a randomized indexing that reduces concurrent reads w...

This paper describes the integration of nested data parallelism into Fortran 90. Unlike flat data parallelism, nested data parallelism directly provides means for handling irregular data structures and certain forms of control parallelism, such as divideand -conquer algorithms, thus enabling the programmer to express such algorithms far more naturally. Existing work deals with nested data parallelism in a functional environment, which does help avoid a set of problems, but makes efficient implementations more complicated. Moreover, functional languages are not readily accepted by programmers used to languages, such as Fortran and C, which are currently predominant in programming parallel machines. In this paper, we introduce the imperative data-parallel language Fortran 90V and give an overview of its implementation. 1 Introduction Vector computers are one of the most successful architectures for high-performance computing. They offer fine-grain data parallelism, enabling one operati...