Supporting stateful tasks in a dataflow graph

This paper introduces Atomic Dataflow Model (ADF) - a programming model for shared-memory systems that combines aspects of dataflow programming with the use of explicitly mutable state. The model provides language constructs that allow a programmer to delineate a program into a set of tasks and to explicitly define input data for each task. This information is conveyed to the ADF runtime system which constructs the task dependency graph and builds the necessary infrastructure for dataflow execution. However, the key aspect of the proposed model is that it does not require the programmer to specify all of the task's dependencies explicitly, but only those that imply logical ordering between tasks. The ADF model manages the remainder of inter-task dependencies automatically, by executing the body of the task within an implicit memory transaction. This provides an easy-to-program optimistic concurrency substrate and enables a task to safely share data with other concurrent tasks. In this paper, we describe the ADF model and show how it can increase the programmability of shared memory systems.