VisualTPL: A visual dataflow language for report data transformation

Data transformation, an important part of report generation, converts the layout of source data into a new layout suitable for presentation. Many report tools have been developed for end-users to specify data transformation. However, current report tools only support a limited set of report layouts. This paper proposes a visual dataflow programming language, called VisualTPL, to resolve this problem. Data transformation is accomplished by writing graphical dataflow programs, which manipulate tables as first-class objects with a set of extendable table operations. A report tool, called VisualTPS, has been developed to offer an easy and intuitive end-user programming environment. Reports with sophisticated layouts can be created through top-down decomposition and incremental development. An evaluation has been conducted to assess end-users' performance with VisualTPL. The results indicated that end-users could learn VisualTPL in a short time and create complicated report layouts all by themselves. And, in comparison with a commercial report tool, VisualTPL offered end-users similar performances and was preferred over the commercial tool.     

软件平台技术在广州地铁综合监控系统中的应用

介绍了软件平台技术在广州地铁综合监控系统中的应用，并提出软件平台的选型和工程实施过程中应注意的问题。     

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.     

A Domain-Specific Language for Generating Dataflow Analyzers

Dataflow analysis is a well-understood and very powerful technique for analyzing programs as part of the compilation process. Virtually all compilers use some sort of dataflow analysis as part of their optimization phase. However, despite being well-understood theoretically, such analyses are often difficult to code, making it difficult to quickly experiment with variants.To address this, we developed a domain-specific language, Analyzer Generator (AG), that synthesizes dataflow analysis phases for Microsoft's Phoenix compiler framework. AG hides the fussy details needed to make analyses modular, yet generates code that is as efficient as the hand-coded equivalent. One key construct we introduce allows IR object classes to be extended without recompiling.Experimental results on three analyses show that AG code can be one-tenth the size of the equivalent handwritten C++ code with no loss of performance. It is our hope that AG will make developing new dataflow analyses much easier.     

APGAN and RPMC: Complementary Heuristics for Translating DSP Block Diagrams into Efficient Software Implementations

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 on programmable processors. The synchronous dataflow (SDF) model is particularly well-suited to dataflow-based graphical 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 consider two approaches. The first is a customization to acyclic graphs of a bottom-up technique, called pairwise grouping of adjacent nodes (PGAN), that was proposed earlier for general SDF graphs. We show that our customization to acyclic graphs significantly reduces the complexity 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 operation, 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. We have implemented these algorithms in the Ptolemy software environment [5] at UC Berkeley.     

High level synthesis of integrated heterogeneous pipelined processing elements for DSP applications

A technique for scheduling and processor allocation leading to the synthesis of integrated heterogeneous pipelined processing elements, implementing digital signal processing applications, is proposed. The proposed technique achieves efficient hardware implementations at the logic-level by minimizing the number of processing units used, without compromising the rate and delay optimality criteria.

The proposed algorithm is found to outperform algorithms resulting in homogeneous implementations, as it gives schedules with lower iteration periods, requires less hardware resources, and has lower time complexity at design time. In comparison with the already existing heterogeneous algorithms, the proposed algorithm produces schedules of lower time complexity and lower iteration period for some applications. The optimal performance of the proposed algorithm has been verified on several benchmarks.     

一种基于Petri网原理的数据流模型研究

数据是系统信息资源的主要表现形式,在信息系统的系统设计和系统分析过程中,数据流建模是一个不可缺少的核心环节。从分析数据资源的特点出发,重点介绍了一种基于Petri网原理的新型的数据流程建模方法——数据流Petri网（DSPN）,给出了数据流Petri网模型的形式化定义和图形表示,并以求解裴波那契数列第n项数值为例,详细说明了数据流Petri网模型在数据流程建模上的表达能力和建模特点。     

Mapping streaming applications on multiprocessors with time-division-multiplexed network-on-chip

This paper addresses mapping of streaming applications (such as MPEG) on multiprocessor platforms with time-division-multiplexed network-on-chip. In particular, we solve processor selection, path selection and router configuration problems. Given the complexity of these problems, state of the art approaches in this area largely rely on greedy heuristics, which do not guarantee optimality. Our approach is based on a constraint programming formulation that merges a number of steps, usually tackled in sequence in classic approaches. Thus, our method has the potential of finding optimal solutions with respect to resource usage under throughput constraints. The experimental evaluation presented in here shows that our approach is capable of exploring a range of solutions while giving the designer the opportunity to emphasize the importance of various design metrics.     

Fairness in dataflow scheduling in the cloud

Expensive dataflow queries which may involve large-scale computations operating on significant volumes of data are typically executed on distributed platforms to improve application performance. Among these, cloud computing has emerged as an attractive option for users to execute dataflows allowing them to select proper configurations (e.g., number of machines) to achieve desired trade-offs between execution time and monetary cost. Discovering dataflow schedules that exhibit the best trade-offs within a plethora of potential solutions can be challenging, especially in a heterogeneous environment where resource characteristics like performance and price can be varied. To increase resource utilization, users may also submit multiple dataflows for execution concurrently. Traditionally, building fair schedules (schedules where the slowdown of all dataflows due to resource sharing is similar) while achieving good performance is a major concern. However, considering fairness in the cloud computing setting where monetary cost is part of the optimization objectives significantly increases the difficulty of the scheduling problem. This paper proposes an algorithm for the scheduling of multiple dataflows on heterogeneous clouds that identifies Pareto-optimal solutions (schedules) in the three-dimensional space formed from the different trade-offs between overall execution time, monetary cost and fairness. The results show that in most cases the proposed approach can provide solutions with fairer schedules without significantly impacting the quality of the execution time to monetary cost skyline compared to the state of the art where the fairness of a solution is not taken into account.     

语法制导生成的虚拟仪器开发平台

如何以一种与特殊的图形用户接口无关的形式一致地描述数据流可视化编程语言及其开发平台,成为虚拟仪器开发平台自动生成的主要问题。该文以自主研制的开发平台LabScene为例,形式化地描述其中最主要的3个部分：编辑,运行,调试,把这些形式化的描述翻译成C#语言,卖现自动生成,为以非数据流为核心的虚拟仪器开发平台的自动生成提供参考依据。