Simulation and Improvement of the Processing Subsystem of the Manchester Dataflow Computer

The Manchester dataflow computer is a famous dynamic dataflow computer.It is centralized in architecture and simple in organization.Its overhead for communication and scheduling is very small.Its efficiency comes down,when processing elements in the processing subsystem increase.Several articles evaluated its performance and presented improved methods.The authors studied its processing subsystem and carried out the simulation.The simulation results show that the efficiency of the processing subsystem drops dramatically when average instruction execution microcycles become less and the maximum instruction execution rate is nearly attained.Two improved methods are presented to oversome the disadvantage.The improved processing subsystem with a cheap distributor made up of a bus and a two-level fixed priority circuit possesses almost full efficiency no matter whether the average nstruction execution microcycles number is large or small and even if the maximum instruction execution rate is approached.     

Dataflow computer extension towards real-time processing

This paper presents an extended architecture and a scheduling algorithm for a dataflow computer aimed at real-time processing. From the real-time processing point of view, current dataflow computers have several problems which stem from their hardware mechanisms for scheduling instructions based on data synchronization. This mechanism extracts as many eligible instructions as possible for execution of a program, then executes them in parallel. Hence, the computation in a dataflow computer is generally difficult to interrupt and schedule using software. To realize a controllable dataflow computation, two basic mechanisms are introduced for serializing concurrent processes and interrupting the execution of a process. A parallel and distributed algorithm for the scheduler is presented, with these two mechanisms, which controls and decides state transitions and execution order of the processes based on priority and execution depth, while still maintaining the number of the running state processes at a preferred value. To gear the scheduler algorithm to meet one of the requirements for real-time processing, such as time-constrained computing, a data-parallel algorithm for selection of the user-process with the current highest priority in O (x log _x n) time is proposed, where n is the number of priority levels.     

传统数据流模型的分析及改进

数据流计算机作为新一代并行机迅速发展起来，但由于数据流模型本身的局限性，使得商用数据流机器的制造还难于普及．本文通过分析运算的操作字符与非操作字符，改进传统模型的点火规则、引入变量并拓广变量的概念，定义了粗粒度数据流模型ＣＤＦＭ（ＣｏａｒｓｅｇｒａｎｕｌａｒｉｔｙＤａｔａＦｌｏｗＭｏｄｅｌ）．     

服务流程中的数据流处理

服务流程需要处理服务之间大量的异构数据的交互,不同的数据流处理方式直接影响了服务流程的执行效率。阐述了服务流程模型中的数据流表示模型、数据映射机制与数据流验证机制,论述了服务流程运行中的数据流调度、数据存储以及传输等数据管理问题,分析了数据流处理在服务流程中的应用情况。最后,结合现有的数据流研究进展,提出了数据流研究的展望。     

Study of a simulated stream machine for dataflow computation

In this paper, a stream-based dataflow architecture is proposed, and its simulation model, which has helped to evaluate the effectiveness of the proposed architectural concept, is discussed. The machine integrates the conventional Von Neumann type of control flow subsystem with a dataflow processing element of token storage type. The control flow unit tackles the dynamic nature of the stream structure including input/output whereas the dataflow unit does the computation part in an applicative style. A pipelined version of the stream machine is also discussed. The effectiveness of the machine is studied by running a few example programs in the simulated machine. The machine is expected to be useful in real time signal processing applications.     

Functional programming on a dataflow architecture: Applications in real-time image processing

This paper presents a dataflow functional computer (DFFC) developed at the Etablissement Technique Central de l'Armement (ETCA) and dedicated to real-time image processing. Two types of data-driven processing elements, dedicated respectively to low-level and mid-level processings are integrated in a regular 3D array. The design of the DFFC relies on a close integration of the dataflow-architecture principles and the functional programming concept. An image processing algorithm, expressed with a syntax similar to that of functional programming (FP) is first converted into a dataflow graph. The nodes of this graph are real-time operators that can be implemented on the physical processors of the dataflow machine. This dataflow graph is then mapped directly onto the processor array. The programming environment includes a complete compilation stream from the FP specification to hardware implementation, along with a global operator database. Apart from being a research tool for real-time image processing, the DFFC may also be used to perform the automatic synthesis of autonomous vision automata from a high-level functional specification. An experimental system, including 1024 lowlevel custom dataflow processors and 12 T800 transputers, was built and can perform up to 50 billion operations/s. Several image processing algorithms were implemented on this system and run in real-time at digital video speed.     

面向数据流结构的指令映射优化方法

在高性能计算领域,数据流是一类重要的计算结构,也在很多实际场景表现出很好的性能和适用性。在数据流计算模式中,程序是以数据流图来表示的,数据流计算中一个关键的问题是如何将数据流图映射到多个执行单元上。通过分析现有数据流结构的指令映射方法及其不足,提出了基于数据流结构的新型指令映射优化方法。主要是根据多地址共享数据包的特性对指令映射方法进行优化,延迟多地址共享数据路由包的拆分,减少网络拥堵。     

An evaluation of medium-grain dataflow code

In this paper, we study several issues related to the medium grain dataflow model of execution. We present bottom-up compilation of medium grainclusters from a fine grain dataflow graph. We compare thebasic block and thedependence sets algorithms that partition dataflow graphs into clusters. For an extensive set of benchmarks we assess the average number of instructions in a cluster and the reduction in matching operations compared with fine grain dataflow execution. We study the performance of medium grain dataflow when several architectural parameters, such as the number of processors, matching cost, and network latency, are varied. The results indicate that medium grain execution offers a good speedup over the fine grain model, that it is scalable, and tolerates network latency and high matching costs well. Medium grain execution can benefit from a higher output bandwidth of a processor and fainally, a simple superscalar processor with an issue rate of two is sufficient to exploit the internal parallelism of a cluster. This work is supported in part by NSF Grants CCR-9010240 and MIP-9113268.     

A data-driven VLSI array for arbitrary algorithms

The design of specialized processing array architectures, capable of executing any given arbitrary algorithm, is proposed. An approach is adopted in which the algorithm is first represented in the form of a dataflow graph and then mapped onto the specialized processor array. The processors in this array execute the operations included in the corresponding nodes (or subsets of nodes) of the dataflow graph, while regular interconnections of these elements serve as edges of the graph. To speed up the execution, the proposed array allows the generation of computation fronts and their cancellation at a later time, depending on the arriving data operands; thus it is called a data-driven array. The structure of the basic cell and its programming are examined. Some design details are presented for two selected blocks, the instruction memory and the flag array. A scheme for mapping a dataflow graph (program) onto a hexagonally connected array is described and analyzed. Two distinct performance measures-mapping efficiency and array utilization-and some performance results are discussed     

Redux: A Dynamic Dataflow Tracer

Redux is a tool that generates dynamic dataflow graphs. It generates these graphs by tracing a program's execution and recording every value-producing operation that takes place, building up a complete computational history of every value produced. For that execution, by considering the parts of the graph reachable from system call inputs, we can choose to see only the dataflow that affects the outside world. Redux works with program binaries, and thus is not restricted to programs written in any particular language.We explain how Redux works, and show how dynamic dataflow graphs give the essence of a program's computation. We show how Redux can be used for debugging and program slicing, and consider a range of other possible uses.