Dataflow process networks

Computer, 2000

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1986

We present the language SIGNAL which is a data floworiented real-time, synchronous, side effect-free language suited to the expression and recovery of the parallelism in signal or image processing algorithms. The language is intended to be, at the same time, an executable simulation language, and a specification of a virtual machine implementing the algorithm. The language is semantically sound, and is suitable to perform program transforms-a major requirement when the ultimate goal is an aid to the architecture design.

1995

Dataflow has proven to be an attractive computational model for graphical DSP design environments that support the automatic conversion of hierarchical signal flow diagrams into implementations onprogrammable processors. The synchronous dataflow (SDF) model is partic ularly well-suited to dataflow-basedgraphical programming because its restricted semantics offer strong formal properties and significant compile-time predictability, while capturing the behavior of a large class of important signal processing applications. When synthesizing software for embedded signal processing applications, critical constraints arise due to the limited amounts of memory. In this paper, we propose a solution to the problem of jointly optimizing the code and data size when converting SDF programs into software implementations. We compare two approaches. The first is a customization to acyclic graphs of a bottom-up technique, called pairwise grouping ofadjacent nodes (PGAN), that was proposed earlier for gen eral SDF graphs. We show that our customization to acyclic graphs significantly reduces the com plexity of the general PGAN algorithm, and we present a formal study of our modified PGAN technique that rigorously establishes its optimality for a certain class of applications. The second approach that we consider is a top-down technique, based on a generalized minimum-cut opera tion, that was introduced recently in [14]. We present the results of an extensive experimental investigation on the performance of our modified PGAN technique and the top-down approach and on the trade-offs between them. Based on these results, we conclude that these two techniques complement each other, and thus, they should both be incorporated into SDF-based software implementation environments in which the minimization of memory requirements is important.

IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2001

| W e describe a system, developed as part of the Cameron project, which compiles programs written in a single-assignment subset of C called SA-C into data ow graphs, and then into VHDL. The primary application domain is image processing. The system consists of an optimizing compiler which produces data ow graphs, and a data ow graph to VHDL translator. The method used for the translation is described here, along with some results on an application. The objective is not to produce yet another design entry tool, but rather to shift the programming paradigm from HDLs to an algorithmic level, thereby extending the realm of hardware design to the application programmer. Keywords| Adaptive-computing, Con gurable, Imageprocessing, Recon gurable-components, Recon gurablecomputing, Recon gurable-systems

2016

We consider dataflow architecture for two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. We improve the earlier technique of almost continuous program transformations by adopting a discipline of bipartite graphs linking nodes obtained via general transformations and nodes obtained via linear transformations which makes it possible to develop and evolve dataflow programs over these classes of computations by continuous program transformations. The use of bipartite graphs allows us to represent the dataflow programs from this class as matrices of real numbers and evolve and modify programs by continuous change of these numbers. We develop a formalism for higher-order dataflow programming for this class of dataflow graphs based on the higher-order matrix elements. Some of our software experiments are briefly discussed.

Computer Languages, 1984

Many novel computer architectures like array and multiprocessors which achieve high performance through the use of concurrency exploit variations of the von Neumann model of computation. The effective utilization of the machines makes special demands on programmers and their programming languages, such as the structuring of data into vectors or the partitioning of programs into concurrent processes. In comparison, the data flow model of computation demands only that the principle of structured programming be followed. A data flow program, often represented as a data flow graph, is a program that expresses a computation by indicating the data dependencies among operators. A data flow computer is a machine designed to take advantage of concurrency in data flow graphs by executing data independent operations in parallel. In this paper, we discuss the design of a high level language (DFL: Data Flow Language) suitable for data flow computers. Some sample procedures in DFL are presented. The implementation aspects have not been discussed in detail since there are no new problems encountered. The language DFL embodies the concepts of functional programming, but in appearance closely resembles Pascal. The language is a better vehicle than the data flow graph for expressing a parallel algorithm. The compiler has been implemented on a DEC 1090 system in Pascal.

SN Applied Sciences, 2021

The dataflow-model of computation is widely used in design and implementation of signal processing systems. In dataflow-based design processes, scheduling—the assignment and coordination of computational modules across processing resources—is a critical task that affects practical measures of performance, including latency, throughput, energy consumption, and memory requirements. Dataflow schedule graphs (DSGs) provide a formal abstraction for representing schedules in dataflow-based design processes. The DSG abstraction allows designers to model a schedule as a separate dataflow graph, thereby providing a formal, abstract (platform- and language-independent) representation for the schedule. In this paper, we introduce a design methodology that is based on explicit specifications of application graphs and schedules as cooperating dataflow models. We also develop new techniques and tools for automatically synthesizing efficient implementations on multicore platforms from these couple...

Parallel Computing, 1999

The data¯ow program graph execution model, or data¯ow for short, is an alternative to the stored-program (von Neumann) execution model. Because it relies on a graph representation of programs, the strengths of the data¯ow model are very much the complements of those of the stored-program one. In the last thirty or so years since it was proposed, the data¯ow model of computation has been used and developed in very many areas of computing research: from programming languages to processor design, and from signal processing to recon®gurable computing. This paper is a review of the current state-of-the-art in the applications of the data¯ow model of computation. It focuses on three areas: multithreaded computing, signal processing and recon®gurable computing. Ó

Journal of Parallel and Distributed Computing, 1991

Are imperative languages tied inseparably to the von Neumann model or can they be implemented in some natural way on dataflow architectures? In this paper, we show how imperative language programs can be translated into dataflow graphs and executed on a dataflow machine like Monsoon. This translation can exploit both fine-grain and coarsegrain parallelism in imperative language programs. More importantly, we establish a close connection between our work and current research in the imperative languages community on data dependencies, control dependencies, program dependence graphs, and static single assignment form. These results suggest that dataflow graphs can serve as an executable intermediate representation in *

Adaptive Systems in Control and Signal Processing 1986, 1987

We present the language SIQW., \Ohich is a data-flow oriente:! real tiDE, synchronous, side effect-free language suite:! to the expression an:! recovery of the parallelism in signal or imIge processing algoritlms. n-e language is interned to be at the SIIIII! tiDE an executable simllation language, an:! a specification of a virtual 1IBChine ~lementing the algoritbn. n-e language is SEIIIIIltically sourrl, an:! is suitable to perform program transforms, a _jor requirement wh!n the ultiDBte goal is an aid to the architecture design. 1. INTRODUCTION. P. Le Guernic, et al. to serve as a high level language for programming ne tworks of data-now Digital Signal Processors, such as the NEC J.LPD7281. Such a design msthadology is interestingly illustrated in the paper by M. Sorin2 from INRIA in this conference; the cooperation with M. Sorine' s group strongly in1l.uenced the design of the language SIGNAL.