A data flow architecture or language performs a computation when all the operands are available. Data flow is one kind of data driven architecture, the other is demand driven. It is a technique for specifying parallel computation at a fine-grain level, usually in the form of two-dimensional graphs in which instructions that are available for concurrent execution are written alongside each other while those that must be executed in sequence are written one under the other. Data dependencies between instructions are indicated by directed arcs. Instructions do not reference memory since the data dependence arcs allow data to be transmitted directly from the producing instruction to the consuming one. Data flow schemes differ chiefly in the way that they handle re-entrant code. Static schemes disallow it, dynamic schemes use either "code copying" or "tagging" at every point of reentry. An example of a data flow architecture is MIT's VAL machine.

In computer programming, dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. Dataflow programming languages share some features of functional languages, and were generally developed in order to bring some functional concepts to a language more suitable for numeric processing.

Dataflow architecture is a computer architecture that directly contrasts the traditional von Neumann architecture or control flow architecture. Dataflow architectures have no program counter, in concept: the executability and execution of instructions is solely determined based on the availability of input arguments to the instructions, so that the order of instruction execution is unpredictable, i.