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The increasing importance of graph-data based applications is fueling the need for highly efficient and parallel implementations of graph analysis software. In this paper we describe Green-Marl, a domain-specific language (DSL) whose high level language constructs allow developers to describe their graph analysis algorithms intuitively, but expose the data-level parallelism inherent in the algorithms. We also present our Green-Marl compiler which translates high-level algorithmic description written in Green-Marl into an efficient C++ implementation by exploiting this exposed data-level parallelism. Furthermore, our Green-Marl compiler applies a set of optimizations that take advantage of the high-level semantic knowledge encoded in the Green-Marl DSL. We demonstrate that graph analysis algorithms can be written very intuitively with Green-Marl through some examples, and our experimental results show that the compiler-generated implementation out of such descriptions performs as well as or better than highly-tuned hand-coded implementations.
Hong et al. (Sat,) studied this question.