Dataflow computers: a tutorial and survey

Journal ArticleThe demand for very high performance computer has encouraged some researchers in the computer science field to consider alternatives to the conventional notions of program and computer organization. The dataflow computer is one attempt to form a new collection of consistent systems ideas to improve both computer performance and to alleviate the software design problems induced by the construction of highly concurrent programs

Dataflow: Overview and simulation

The thesis project is a software simulation of the dataflow machine prototyped at the University of Manchester. It uses a dynamic token matching scheme based on the U-interpreter, and supports I-structures, an array-like data structure. An assembly language is provided for programming the simulator

Highly parallel computation

Highly parallel computing architectures are the only means to achieve the computation rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines and current research focuses on which architectures designated as multiple instruction multiple datastream (MIMD) and single instruction multiple datastream (SIMD) have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed

Simulation of a data flow computer

A data flow computer is a highly concurrent and asynchronous multiprocessor due to its fundamentally new architecture. It has no program counter and is not sequential. Instructions execute whenever their operands are available to them. Because of this data-activated instruction execution, multiple instructions can execute concurrently. The project for this thesis was the simulation of a data flow computer. A graph language and machine language were defined; then a simulator was written which reads and executes a machine language program in the asynchronous and concurrent manner of a data flow computer

Generating Data Flow Programs From Nonprocedural Specifications

Data flow is a mode of parallel computation in which parallelism in a program can be exploited at the fine grained as well as macro level. A data flow computer executes a data dependency graph rather than the program counter controlled sequence of instructions executed by conventional machines. Nonprocedural languages appear to be especially appropriate high level languages for data flow computers. Nonprocedural languages have only two statement forms: data description and assertion. The assertions enumerate the relationships among the data. A data dependency graph is also a suitable representation for a nonprocedural language program (or specification). This research is concerned with translating the dependency graph form of a specification to a program graph for a data flow machine. Specifications in the MODEL language are translated into an intermediate form, the data flow template. The template is a language-independent representation of the specification. The template is then translated into a data flow language (Manchester Dataflow) for the Manchester University machine. The translation consists of creating an array graph to represent the specification; generating the data flow program template from the array graph; and translating the template into MaD

Dynamic resource allocation in a hierarchical multiprocessor system: A preliminary study

An integrated system approach to dynamic resource allocation is proposed. Some of the problems in dynamic resource allocation and the relationship of these problems to system structures are examined. A general dynamic resource allocation scheme is presented. A hierarchial system architecture which dynamically maps between processor structure and programs at multiple levels of instantiations is described. Simulation experiments were conducted to study dynamic resource allocation on the proposed system. Preliminary evaluation based on simple dynamic resource allocation algorithms indicates that with the proposed system approach, the complexity of dynamic resource management could be significantly reduced while achieving reasonable effective dynamic resource allocation

An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine