Toward a dataflow/von Neumann hybrid architecture

Abstract

Dataflow architectures offer the ability to trade program level parallelism in order to overcome machine level latency. Dataflow further offers a uniform synchronization paradigm, representing one end of a spectrum wherein the unit of scheduling is a single instruction. At the opposite extreme are the von Neumann architectures which schedule on a task, or process, basis. This paper examines the spectrum by proposing a new architecture which is a hybrid of dataflow and von Neumann organizations. The analysis attempts to discover those features of the dataflow architecture, lacking in a von Neumann machine, which are essential for tolerating latency and synchronization costs. These features are captured in the concept of a parallel machine language which can be grafted on top of an otherwise traditional von Neumann base. In such an architecture, the units of scheduling, called scheduling quanta , are bound at compile time rather than at instruction set design time. The parallel machine language supports this notion via a large synchronization name space. A prototypical architecture is described, and results of simulation studies are presented. A comparison is made between the MIT Tagged-Token Dataflow machine and the subject machine which presents a model for understanding the cost of synchronization in a parallel environment.