Many algorithms for analyzing parallel programs, for example to detect deadlocks or data races or to calculate the execution cost, are based on a model variously known as a cost graph, computation graph or dependency graph, which captures the parallel structure of threads in a program. In modern parallel programs, computation graphs are highly dynamic and depend greatly on the program inputs and execution details. As such, most analyses that use these graphs are either dynamic analyses or are specialized static analyses that gather a subset of dependency information for a specific purpose. This paper introduces graph types, which compactly represent all of the graphs that could arise from program execution. Graph types are inferred from a parallel program using a graph type system and inference algorithm, which we present drawing on ideas from Hindley-Milner type inference, affine logic and region type systems. We have implemented the inference algorithm over a subset of OCaml, extended with parallelism primitives, and we demonstrate how graph types can be used to accelerate the development of new graph-based static analyses by presenting proof-of-concept analyses for deadlock detection and cost analysis.

@article{paperbot1549,
  title = {Static prediction of parallel computation graphs},
  author = {Stefan K. Muller},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {6},
  number = {POPL},
  year = {2022},
  doi = {10.1145/3498708}
}