Asynchronous Execution of Python Code on Task-Based Runtime Systems

Tohid, R.; Wagle, Bibek; Shirzad, Shahrzad; Diehl, Patrick; Serio, Adrian; Kheirkhahan, Alireza; Amini, Parsa; Williams, Kirk R.; Isaacs, Katherine E.; Huck, Kevin; Brandt, Steven R.; Kaiser, Hartmut

doi:10.1109/espm2.2018.00009

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…It has various libraries (Dubosson et al, 2016) and is open source (Samal et al, 2017). Some of the libraries adopted included the following: TensorFlow (Tohid et al, 2019), Keras (Stancin and Jovic, 2019), Seaborn (Fahad and Yahya, 2018), Matplotlib (Hung et al, 2020), pandas (Li et al, 2020), and NumPy (Momm et al, 2020). Additionally, the Laravel PHP framework was adopted in writing application programming interfaces (APIs).…”

Section: Resourcesmentioning

confidence: 99%

“…Deep learning is currently shaping how machine learning is ap-plied to various areas. Some of the prominent areas where deep learning has shown a lot of promise include machine vision, speech recognition, audio processing, health diagnosis, and fraud detection, among others (Too et al, 2019). With the complication of challenges in the 21st century, majority voting is being applied to machine learning to improve performance as it provides an extra layer of decision-making to the model.…”

Section: Introductionmentioning

confidence: 99%

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An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

Kimutai

Ngenzi

Ngoga

et al. 2021

J. Sens. Sens. Syst.

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Abstract. Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.

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Section: Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

Kimutai

Ngenzi

Ngoga

et al. 2021

J. Sens. Sens. Syst.

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“…-Kokkos (Carter , the C++ Performance Portability Programming EcoSystem. -Phylanx (Tohid et al, 2018;Wagle et al, 2019) An Asynchronous Distributed C++ Array Processing Toolkit.…”

Section: Applicationsmentioning

confidence: 99%

HPX - The C++ Standard Library for Parallelism and Concurrency

Kaiser¹,

Diehl²,

Lemoine³

et al. 2020

JOSS

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The new challenges presented by exascale system architectures have resulted in difficulty achieving the desired scalability using traditional distributed-memory runtimes. Asynchronous many-task systems (AMT) are based on a new paradigm showing promise in addressing these challenges, providing application developers with a productive and performant approach to programming on next generation systems. HPX is a C++ Library for concurrency and parallelism that is developed by The STE||AR Group, an international group of collaborators working in the field of distributed and parallel programming (Heller, Diehl, Byerly, Biddiscombe, & Kaiser, 2017; Kaiser et al., n.d.; Tabbal, Anderson, Brodowicz, Kaiser, & Sterling, 2011). It is a runtime system written using modern C++ techniques that are linked as part of an application. HPX exposes extended services and functionalities supporting the implementation of parallel, concurrent, and distributed capabilities for applications in any domain; it has been used in scientific computing, gaming, finances, data mining, and other fields.

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“…We conducted this design study as part of a visualization initiative in the Phylanx Project. First, we provide technical background of the Phylanx [60] system necessary to understand our resulting visualization. We then discuss the organization of the project itself, how it led us to accept winnowing pitfalls, and the roles we cast in the design study.…”

Section: The Phylanx Projectmentioning

confidence: 99%

Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns

Williams

Bigelow

Isaacs

2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Design Study timeline (log scale). The top contains a mark for each collected artifact. Connections to identified goals, sub-goals, and tasks are marked when direct evidence for them has been identified. Artifacts from meetings presenting major design changes and notes from the evaluation sessions of Section 7.2 are indicated with color. The bottom shows the timing of various deployments with users. This rich collection of over 150 artifacts mitigated issues in designing around shifting data and concerns.Abstract-Common pitfalls in visualization projects include lack of data availability and the domain users' needs and focus changing too rapidly for the design process to complete. While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a "chicken and egg" problem of having the data and tools to analyze it. We found this to be the case in the domain of task parallel computing where such data and tooling is an open area of research. Despite these hurdles, we conducted a design study. Through a tightly-coupled iterative design process, we built Atria, a multi-view execution graph visualization to support performance analysis. Atria simplifies the initial representation of the execution graph by aggregating nodes as related to their line of code. We deployed Atria on multiple platforms, some requiring design alteration. We describe how we adapted the design study methodology to the "moving target" of both the data and the domain experts' concerns and how this movement kept both the visualization and programming project healthy. We reflect on our process and discuss what factors allow the project to be successful in the presence of changing data and user needs.

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Asynchronous Execution of Python Code on Task-Based Runtime Systems

Cited by 15 publications

References 15 publications

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

HPX - The C++ Standard Library for Parallelism and Concurrency

Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns

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