Teaching Concurrent and Distributed Programming With Concepts Over Mathematical Proofs

Marchant, David; Johnsen, Carl-Johannes; Vinter, Brian; Skovhede, Kenneth

doi:10.1109/eduhpc49559.2019.00012

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…tool and associated simulations on student learning, a tool evaluation survey was conducted at the end of the Fall 2019 semester, where students provided feedback on their experience with the tool. The survey questions were constructed following similar educational evaluation studies [9], [10], [38]- [43], and comprised of both rating-based as well as text-response-based questions.…”

Section: Q9mentioning

confidence: 99%

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning

Jadhao¹,

Kadupitiya²

2020

Preprint

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We explore the idea of integrating machine learning (ML) with high performance computing (HPC)driven simulations to address challenges in using simulations to teach computational science and engineering courses. We demonstrate that a ML surrogate, designed using artificial neural networks, yields predictions in excellent agreement with explicit simulation, but at far less time and computing costs. We develop a web application on nanoHUB that supports both HPC-driven simulation and the ML surrogate methods to produce simulation outputs. This tool is used for both in-classroom instruction and for solving homework problems associated with two courses covering topics in the broad areas of computational materials science, modeling and simulation, and engineering applications of HPC-enabled simulations. The evaluation of the tool via in-classroom student feedback and surveys shows that the ML-enhanced tool provides a dynamic and responsive simulation environment that enhances student learning. The improvement in the interactivity with the simulation framework in terms of real-time engagement and anytime access enables students to develop intuition for the physical system behavior through rapid visualization of variations in output quantities with changes in inputs.

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Section: Q9mentioning

confidence: 99%

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning

Jadhao¹,

Kadupitiya²

2020

Preprint

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“…What CSP does well, is expressing concurrent programs without introducing the hazards normally found in a parallel programming model. Teaching CSP as a programming model to students shows the model itself is simpler than other methods [4] and initial results also show that the SME model allows students familiar with sequential programming to pick up the parallelism [5].…”

Section: Introductionmentioning

confidence: 99%

SME: A High Productivity FPGA Tool for Software Programmers

Johnsen,

Thegler,

Skovhede

et al. 2021

Preprint

Self Cite

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For several decades, the CPU has been the standard model to use in the majority of computing. While the CPU does excel in some areas, heterogeneous computing, such as reconfigurable hardware, is showing increasing potential in areas like parallelization, performance, and power usage. This is especially prominent in problems favoring deep pipelining or tight latency requirements. However, due to the nature of these problems, they can be hard to program, at least for software developers. Synchronous Message Exchange (SME) is a runtime environment that allows development, testing and verification of hardware designs for FPGA devices in C#, with access to modern debugging and code features. The goal is to create a framework for software developers to easily implement systems for FPGA devices without having to obtain heavy hardware programming knowledge. This article presents a short introduction to the SME model as well as new updates to SME. Lastly, a selection of student projects and examples will be presented in order to show how it is possible to create quite complex structures in SME, even by students with no hardware experience.

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Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning

Jadhao

Kadupitiya

2020

2020 IEEE/ACM Workshop on Education for High-Performance Computing (EduHPC)

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Teaching Concurrent and Distributed Programming With Concepts Over Mathematical Proofs

Cited by 3 publications

References 7 publications

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning

SME: A High Productivity FPGA Tool for Software Programmers

Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning

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