36,702 research outputs found

    PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

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    The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

    SPARQL Playground: A block programming tool to experiment with SPARQL

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    SPARQL is a powerful query language for SemanticWeb data sources but one which is quite complex to master. As the block programming paradigm has been succesfully used to teach programming skills, we propose a tool that allows users to build and run SPARQL queries on an endpoint without previous knowledge of the syntax of SPARQL and the model of the data in the endpoint (vocabularies and semantics). This user interface attempts to close the gap between tools for the lay user that do not allow to express complex queries and overtly complex technical tools

    Visual Execution and Data Visualisation in Natural Language Processing

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    We describe GGI, a visual system that allows the user to execute an automatically generated data flow graph containing code modules that perform natural language processing tasks. These code modules operate on text documents. GGI has a suite of text visualisation tools that allows the user useful views of the annotation data that is produced by the modules in the executable graph. GGI forms part of the GATE natural language engineering system

    Structure Learning for Neural Module Networks

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    Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that involve human-specified neural modules, each designed for a specific form of reasoning. In current formulations of such networks only the parameters of the neural modules and/or the order of their execution is learned. In this work, we further expand this approach and also learn the underlying internal structure of modules in terms of the ordering and combination of simple and elementary arithmetic operators. Our results show that one is indeed able to simultaneously learn both internal module structure and module sequencing without extra supervisory signals for module execution sequencing. With this approach, we report performance comparable to models using hand-designed modules
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