Iteration constructs in data-flow visual programming languages

Mosconi, Mauro; Porta, Marco

doi:10.1016/s0096-0551(01)00009-1

Cited by 22 publications

(19 citation statements)

References 8 publications

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“…We propose a fourth kind of iteration for workflows, dynamic loops, a particular type of conditional loops where the condition of the loop and the data being processed in the loop may be adapted during the execution. We follow the iterative constructs of [22] to build a data-centric iterative approach, inspired by functional database programming languages [6]. In order to support dynamic loops, we combine the data-centric iterative approach with a dynamic execution model, which we will discuss in Section 4.…”

Section: Iterative Constructs For the Workflow Algebramentioning

confidence: 99%

“…Definition 3. A data-centric scientific workflow is a data-driven dataflow [22], which is made up only of activities associated with algebraic operators, additional attributes of the operator and their respective input and output relations. An activity receives an input relation, which is fragmented into atoms according to the algebraic operator associated to that activity.…”

Section: Definitionmentioning

confidence: 99%

“…Since the input of activity A depends on the output of activity B, activity A fires again and the process never ends. According to Mosconi et al [22], an iteration can have an end if: (a) there is a mechanism that prevents at least one activity in the iterated subgraph from firing if some conditions are satisfied, or (b) at least one activity in the iterated subgraph is prevented to produce all its outputs; if some conditions are satisfied when firing. We believe the second option best fits scientific workflows.…”

Section: A Loop Can Iteratementioning

confidence: 99%

“…Case (a) prevents an activity Y of the workflow from firing. Thus, we would need an enabling signal (token) dataflow [22] to fire the remaining activities of which input data depends on the output of Y. In our relational approach, we only use data atoms as signals to fire an activity.…”

Section: A Loop Can Iteratementioning

confidence: 99%

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Data-centric iteration in dynamic workflows

Dias

Guerra

Rochinha

et al. 2015

Future Generation Computer Systems

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SummaryDynamic workflows are scientific workflows to support computational science simulations, typically using dynamic processes based on runtime scientific data analyses. They require the ability of adapting the workflow, at runtime, based on user input and dynamic steering. Supporting data-centric iteration is an important step towards dynamic workflows because user interaction with workflows is iterative. However, current support for iteration in scientific workflows is static and does not allow for changing data at runtime. In this paper, we propose a solution based on algebraic operators and a dynamic execution model to enable workflow adaptation based on user input and dynamic steering. We introduce the concept of iteration lineage that makes provenance data management consistent with dynamic iterative workflow changes. Lineage enables scientists to interact with workflow data and configuration at runtime through an API that triggers steering. We evaluate our approach using a novel and real large-scale workflow for uncertainty quantification on a 640-core cluster. The results show impressive execution time savings from 2.5 to 24 days, compared to noniterative workflow execution. We verify that the maximum overhead introduced by our iterative model is less than 5% of execution time. Also, our proposed steering algorithms are very efficient and run in less than 1 millisecond, in the worst-case scenario.

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Section: Iterative Constructs For the Workflow Algebramentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: A Loop Can Iteratementioning

confidence: 99%

Section: A Loop Can Iteratementioning

confidence: 99%

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Data-centric iteration in dynamic workflows

Dias

Guerra

Rochinha

et al. 2015

Future Generation Computer Systems

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“…Supporting iteration in a language based on the data flow paradigm requires to introduce some auxiliary construct [32]. In our approach we rely on two constructs with a different degree of generality.…”

Section: Iterationmentioning

confidence: 99%

JOpera: A Toolkit for Efficient Visual Composition of Web Services

Pautasso

Alonso

2005

International Journal of Electronic Commerce

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The Dinamica Virtual Machine for Geosciences

Ferreira¹,

Soares‐Filho²,

Pereira³

2015

Programming Languages

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This work describes DinamicaVM, the virtual machine that runs applications developed in Dinamica EGO. Dinamica EGO is a framework used in the development of geomodeling applications. Behind its multitude of visual modes and graphic elements, Dinamica EGO runs on top of a virtual machine. This machine-DinamicaVM-offers developers a rich instruction set architecture, featuring elements such as map and reduce, which are typical in the functional/parallel world. Ensuring that these very expressive components work together efficiently is a challenging endeavour. Dinamica's runtime addresses this challenge through a suite of optimizations, which borrows ideas from functional programming languages, and leverages specific behavior expected in geo-scientific programs. As we show in this work some of these optimizations deliver speedups of almost 100x, and are key to the industrial-quality performance of one of the world's most widely used geomodeling tools.

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Iteration constructs in data-flow visual programming languages

Cited by 22 publications

References 8 publications

Data-centric iteration in dynamic workflows

Data-centric iteration in dynamic workflows

JOpera: A Toolkit for Efficient Visual Composition of Web Services

The Dinamica Virtual Machine for Geosciences

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