A complexity measure for data flow models

Network teaching is to use computer equipment and Internet technology in the virtual space, through a certain method to implement the teaching method on the network, the perfection of the network teaching system can directly replace the traditional teaching method in the whole process of teaching. In order to solve the problem of system development, this paper carries out the demand analysis. First, the use of data flow graph technology, depicting information flow and data from the input to the output of the process of transformation; and then, the use of data dictionary technology, defining the relevant elements of the data flow diagram; finally, the use of tree structure diagram is represented by the administrator subsystem, teacher subsystem and student subsystem structure of the system. The requirement analysis system design the bridge of analysis and software, has laid a foundation for the system development.

The ULowell Data Flow Project combines dataflow hardware, dataflow software, and a data flow development environment. The hardware component, a data flow coprocessor card, utilizes seven NEC 7281 Image Pipelined Processors. Each chip models a static dataflow computation and is programmable. The coprocessor was designed and implemented by ULowell personnel and uses an AMIGA 2000 as a host. The software component currently consists of a library of data flow image processing algorithms and an evolving software development environment. Currently the image processing library includes typical imaging operations such as: edge enhancement, filtering, histogramming, and affine transformation. The software development environment (SDE) is depicted in Figure 1. The SDE contains a Data Flow Graph Editor which programmers use to design their data flow code. The data flow algorithm can then be converted directly into NEC data flow assembly code which is then assembled using the ULowell Data Flow Assembler. The assembler differs from NEC's assembler in that it can produce relocatable code. The object code output of the assembler is given to the Data Flow Resource Manager. The resource manager monitors the available resources within the chips. Thus, if a single process does not totally utilize a chip's table space or memory, then a second process may be concurrently loaded onto the same chip. The ability to have multiple data flow processes coexisting within a single chip is unique to the ULowell environment. The data flow resource manager can download and monitor code to either the hardware or to a software simulator/debugger. The simulator/debugger can monitor and trace data flow tokens as they proceed through the chip. Output of the simulator/debugger can be fed back into the data flow graph editior to visually trace token flow through the original program design. The last component of our SDE is a High Level Language translator which can compile programs written in SISAL, a high level language for parallel computation, down to NEC 7281 assembly language. We have already implemented the relocatable assembler, and the data flow resource manager. Prototype versions of the high level translator, simulator/debugger and graph/editor are currently being implemented.

Despite the attractive features of data flow computers, skepticism exists concerning their efficiency in handling arrays (vectors) in high performance scientific computation. This paper argues that massive parallelism in vector computation can be exploited effectively utilizing data flow principles. The key is to organize the data flow machine program graph such that array operations can be fully pipelined. Unlike in conventional pipelined vector processors, there is no requirement that the activities belonging to the same vector operation be continuously processed by one or a group of physically tightly-coupled function units in the processor. The applicative nature of the data flow graph model allows flexible scheduling of the execution of enabled instructions in the pipeline data flow programs. Accordingly, program transformation can be performed on the basis of both the global and local data flow analysis to generate efficient pipelined data flow machine code. As a result of such fine-grain pa...

The theory of computational complexity is based on the tradeoff between two computational resources, memory space and computer time. This paper investigates the physical counterparts of these resources. Memory space is the number of bits of information required by a computer over the course of a computation. Physically, information is intimately connected with entropy: entropy can be thought of as random bits of information. The physical analogue of the amount of memory space required for a computation, Ф- SPACE, is the difference between the maximum entropy Smax of the computing system and its actual entropy, S.Ф-SPACE = S - Smax is the thermodynamic depth of the computation. Ф-SPACE is the maximum number of bits of memory space available for a physical system to perform computation. In conventional computational complexity theory, computer time is identified as the number of elementary logical operations (AND, OR, NOT, COPY) required to perform the computation. The physical analogue of time computational complexity is simply the time required for the physical computing system to perform the computation. In order to obtain a number that measures the number of physical operations that can be performed by the system over time t, we define Ф-TIME to be proportional to the computation time multiplied by the energy E of the computing system over the course of the computation: Ф-TIME = 2Et/πħ. Here, the proportionality factor 2/πħ is chosen to match the time it takes a computing system with energy E to flip a bit in time t. Ф-TIME is thus the number of bit flips that can take place in an information processing system. In addition, we propose a novel measure of physical complexity that combines these measures. The free energy F = E - TS of a system is the amount of energy that is available to do useful work - as opposed to the energy bound up in the thermal fluctuations of heat. We define the free complexity to be the free energy × time product, 2Ft/πħ required by a physical system to perform a computation. Physical spatial and temporal complexity provide upper bounds on the amount of computation that can be performed by any physical system. We discuss the implications of physical complexity for the computational complexity hierarchy, in particular, we show that in the presence of errors in the computation, Ф-SPACE is proportional Ф-TIME. We apply the resulting theory of physical computational complexity to analyze the computational power of black holes: exactly half of the energy of a black hole is available for physical computation.

Data flow is the name applied when data are passed from one thing to another. The “thing” that passes the data can include a piece of software or an application, a computer or a region in a computer, a person, group, or organizational unit, or combinations of these. The data passed can be simple or complex and it can be passed as a stream or a unit. A diagram that depicts objects or processes and the possible flows of data among them is called a data flow diagram (DFD). DFDs are useful for depicting information about how an organization operates; the interfaces between an application and the people or other applications that use it; and the high‐level design of an application. DFDs have been used in application development for a long time. There are two alternatives to (pure) data flow and each alternative has substantial disadvantages when used to link software components. One of these alternatives to data flow is for the data not to flow; that is, have various software modules access it directly in shared common data areas. A second alternative to pure data flow is the “call” statement, used in most languages to link subroutines. The call statement has two substantial limitations versus pure data flow: it also passes control and it explicitly names the target subroutine. In today's application development methodologies, DFDs are important for: analyzing organizational units; depicting the context of an application relative to users and other applications; designing the high level (process) architecture of applications; showing the interfaces between application processes and data stores; and documenting batch job streams. DFDs are likely to be increasingly important for designing distributed systems, object‐oriented design, depicting asynchronous components, and for reusable code. An examination of each is given.

Most computer architecture courses are geared toward the classical von Neumann style of computer architectures, mentioning only in passing other models such as data flow computation. This is unfortunate, due to the high degree of parallelism possible using data flow. We present an alternative course, designed as an elective in computer architecture for upper level undergraduate or graduate students, that presents a side-by-side comparison of von Neumann and data flow architectures.Our teaching environment is based on Simple Arithmetic SISAL (SAS), a subset of the applicative programming language SISAL, which we designed for both teaching about and research into data flow architectures. SAS runs in a highly integrated environment, allowing students to implement their program on a von Neumann architecture, then observe its execution through a data flow simulator. The environment runs on a standard IBM-style personal computer, providing a cost-effective platform for presenting the course.

Visual domain-specific languages usually have low entry barrier. Sometimes even children can program on such languages by working with visual representations. This is widely used in educational robotics domain, where most commonly used programming environments are visual. The paper describes a novel dataflow visual programming environment for embedded robotic platforms. Obviously, complex dataflow languages are not simple for understanding. The purpose of our tool is to "bridge" between lightweight educational robotic programming tools (commonly these tools provide languages which are based on control flow model) and complex industrial tools (which provide languages based on more complex dataflow execution model). We compare programming environments mostly used by robotics community with our tool. After brief review of behavioural robotic architectures, some thoughts on expressing them in terms of our dataflow language are given. Visual language, which is described here, provides opportunity to mix dataflow and control flow models for robotics programming. We believe that it is important for educational purposes. Program on our language consists of different blocks (visual representation of data transformation processes) and "links" which presents data flow between them. Domain-specific modelling approach was used to develop our language. Also, this paper provides the examples of solving two typical robot control tasks in our language.

In wavelet-based video compression, some kernel functions such as block matching, discrete wavelet transform, are essential but with large complexity in computation. If we dissect two functions into the basic matrix-vector product forms, a unified design for them becomes feasible. In this paper, with carefully extraction the common computation component, a unified one-dimensional systolic array that can perform block matching and wavelet transform is presented. In this design, the input data is serial-in to save the amount of required pins, the data flow are carefully arranged to simplify the interconnection between computation components. Unlike the other full-search block matching hardware, it does not require either data broadcasting or the parallel adder. When executing a wavelet transform, the data flow is scheduled simply and effectively. It does not require any interconnecting network or off-chip memory to store intermediate results.

This paper deals with the data flow computing paradigm, with the characteristics of program execution control using a flow of data instead of a flow of instructions. Data flow computing represents an important alternative to the control flow computing paradigm, which is currently the mainstream computing paradigm represented by architectures mostly based on the Von Neumann principles, in which the flow of program execution is controlled by a flow of instructions. This paper also deals with the tile computing paradigm - a modern approach to designing multi-core microprocessors with components laid out in two-dimensional grids with various geometries of cores, memory elements and interconnection networks with architectures using both data flow and control flow program execution control. In this paper we introduce a data flow computer architecture designed at the Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Slovakia, employing the principles of tile computing. Efficient data flow architectures applying a different approach to the control of program execution flow also demand a different operating memory design. As the part of our effort to build data flow computer architectures, we have designed an innovative memory architecture that fulfils the specific needs of data flow computer architectures. In the final section of the paper we describe the functions and operations possible with this kind of memory.

Reconfigurable computing refers to the notions of configware and flowware. Configware means structural programming, or programming in space to execute computation in space. Flowware means dataflow programming that schedules the data flow for output from or input to the configware architecture. In this paper, data flows of a synthesized computation are formalized. This means that data flow is specified as a behavioral stream function in stream calculus, which is used to underpin the semantics for Register Transfer Level (RTL) synthesis. A stream representation allows the use of coinductive principles in stream calculus. In particular, using the coinductive proof principle, we show that behavioral stream functions in the three-stage synthesis process (scheduling, register allocation and binding, allocation and binding of functional units) are always bisimilar regardless of changes in a scheduling, allocation or binding procedure. Our formalization makes pipelining possible, in which all functional units as well as registers of hardware resources are reused during different control steps (C-steps). Moreover, a coinductive approach to verifying flowware synthesis, which is independent of the heuristic during register allocating and binding step, is proposed as a practical technique.

The ways that the methods of data flow analysis can be applied to improve software reliability are described. There is also a review of the basic terminology from graph theory and from data flow analysis in global program optimization. The notation of regular expressions is used to describe actions on data for sets of paths. These expressions provide the basis of a classification scheme for data flow which represents patterns of data flow along paths within subprograms and along paths which cross subprogram boundaries. Fast algorithms, originally introduced for global optimization, are described and it is shown how they can be used to implement the classification scheme. It is then shown how these same algorithms can also be used to detect the presence of data flow anomalies which are symptomatic of programming errors. Finally, some characteristics of and experience with DAVE, a data flow analysis system embodying some of these ideas, are described.

Implementation of a context-oriented approach for intelligent processing of navigation data flows in operation of water vehicles was considered. The initial sample of processing data flows in the conditions of the current pilot navigation method does not correspond to the regularities of functioning of a complex object with the modern instrumental method of navigation. Solution to the problem consists in overcoming difficulties by defining complex problems of the processes that differ from a simple sum of parameters of elements with the same type of multilevel connections.A method for processing flows of navigation data under continuous mode was proposed. In the process of study, combining of information from multiple sources with finding more accurate and reliable data was considered. The method of estimation of problem solutions is the criterion method where each single alternative is estimated by a specific number (criterion, objective function). Comparison of alternatives was reduced to a comparison of the corresponding numbers. Various variants of the choice of alternatives and criteria of optimality were taken into account: the criteria of Bayes, Wald, Jain, Laplace. The method used is characterized by multicriteria conditions. Methods of decision-making in games with environment, normalization, the use of neural networks based on the context were applied. The architecture of an artificial neural network was constructed.The study was conducted with the aim of obtaining a stable structure of the system with certain classes of input signals based on artificial neural networks. It is interesting from the theoretical point of view to obtain a parametric variation of the parameters within the specified limits of the optimality criteria. The conducted experiments confirmed efficiency of the use of the proposed methods. The most constructive direction of intelligent processing of the navigational data flow is the context-oriented approach. Implementation of this approach guarantees high level of observance of accuracy criteria in the conditions of river navigation.An applied aspect of using the results obtained in the process of study is the possibility to abandon the pilotage method of navigation and installation of coastal and floating means of navigation equipment. An important result is derivation of a differentiated mapping of the depth array on an electronic chart. The obtained results give grounds to assert the possibility of introducing the proposed context-oriented approach to real navigation.

The analysis and exploration of complex data sets are common problems in many areas, including scientific and business domains. This need has led to substantial development of the data visualization field. In this paper, we present VMetaFlow, a graphical meta-framework to design interactive and coordinated views applications for data visualization. Our meta-framework is based on data flow diagrams since they have proved their value in simplifying the design of data visualizations. VMetaFlow operates as an abstraction layer that encapsulates and interconnects visualization frameworks in a web-based environment, providing them with interoperability mechanisms. The only requirement is that the visualization framework must be accessible through a JavaScript API. We propose a novel data flow model that allows users to define both interactions between multiple data views and how the data flows between visualization and data processing modules. In contrast with previous data-flow-based frameworks for visualization, we separate the view interactions from data items, broadening the expressiveness of our model and supporting the most common types of multi-view interactions. Our meta-framework allows visualization and data analysis experts to focus their efforts on creating data representations and transformations for their applications, whereas nonexperts can reuse previously developed components to design their applications through a user-friendly interface. We validate our approach through a critical inspection with visualization experts and two case studies. We have carefully selected these case studies to illustrate its capabilities. Finally, we compare our approach with the subset flow model designed for multiple coordinated views.

Dataflow programming models are well-suited to program many-core streaming applications. However, many streaming applications have a dynamic behavior. To capture this behavior, parametric dataflow models have been introduced over the years. Still, such models do not allow the topology of the dataflow graph to change at runtime, a feature that is also required to program modern streaming applications. To overcome these restrictions, we propose a new model of computation, the Boolean Parametric Data Flow (BPDF) model which combines integer parameters (to express dynamic rates) and boolean parameters (to express the activation and deactivation of communication channels). High dynamism is provided by integer parameters which can change at each basic iteration and boolean parameters which can even change within the iteration. The major challenge with such dynamic models is to guarantee liveness and boundedness. We present static analyses which ensure statically the liveness and the boundedness of BDPF graphs. We also introduce a scheduling methodology to implement our model on highly parallel platforms and demonstrate our approach using a video decoder case study.

The subject matter of the article is information and communication networks (ICN) of critical infrastructure systems (CIS). The goal of the work is to create methods for managing the data flows and resources of the ICN of CIS to improve the efficiency of information processing. The following tasks were solved in the article: the data flow model of multi-level ICN structure was developed, the method of adaptive distribution of data flows was developed, the method of network resource assignment to multi-server nodes was developed. The following methods used are –methods of mathematical statistics for random processes, the theory of queuing systems, methods of optimization theory and operations research. The following results were obtained – the principles of managing the distribution of network traffic in the ICN of CIS were formulated and the practical requirements arising in the efficiency of data transmission were determined. The possible approaches to the formulation and solution of the listed problems were suggested according to the developed general approach to network management. The multi-level information structure was investigated. The mathematical model of data flows of a multilevel information structure of the network was developed; it has a three-level unstratified structure and consists of a number of subnets and groups of nodes. The method for adaptive management of data flows distribution was developed; this method includes the stratified two-level management which is based on the development of a multidimensional space of the network state and management parameters taking into account user activities. The management is carried out at the first level by setting the basic parameters of the network, at the second – by operational management with constant basic parameters. The method for distributing the resources of a multi-server information processing node was developed, as server systems are considered as a set of single-line queuing systems and information about the distribution of the bandwidth of communication channels is used. Conclusions: using the method of the adaptive management of traffic distribution enables reducing the time for processing system transactions and total costs for maintenance. The use of the method resource distribution of the server node in the course of re-engineering CIS processes minimizes the costs of servicing the data flows.