A performance portability framework for Python

Awar, Nader Al; Zhu, Steven; Biros, George; Gligoric, Milos

doi:10.1145/3447818.3460376

Cited by 19 publications

(5 citation statements)

References 19 publications

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“…The Python 3.8.12 programming language [31] together with four libraries-Keras 2.10.0 [32], Tensorflow 2.10.0, MathPlotLib [33], and SKlearn 1.0.1-were chosen to implement and evaluate these six selected machine learning methods.…”

Section: Selected Machine Learning Methodsmentioning

confidence: 99%

An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

Iordan

2024

Mathematics

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Software effort estimation has constituted a significant research theme in recent years. The more important provocation for project managers concerns reaching their targets within the fixed time boundary. Machine learning strategies can lead software management to an entire novel stage. The purpose of this research work is to compare an optimized long short-term memory neural network, based on particle swarm optimization, with six machine learning methods used to predict software development effort: K-nearest neighbours, decision tree, random forest, gradient boosted tree, multilayer perceptron, and long short-term memory. The process of effort estimation uses five datasets: China and Desharnais, for which outputs are expressed in person-hours; and Albrecht, Kemerer, and Cocomo81, for which outputs are measured in person-months. To compare the accuracy of these intelligent methods four metrics were used: mean absolute error, median absolute error, root mean square error, and coefficient of determination. For all five datasets, based on metric values, it was concluded that the proposed optimized long short-term memory intelligent method predicts more accurately the effort required to develop a software product. Python 3.8.12 programming language was used in conjunction with the TensorFlow 2.10.0, Keras 2.10.0, and SKlearn 1.0.1 to implement these machine learning methods.

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Section: Selected Machine Learning Methodsmentioning

confidence: 99%

An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

Iordan

2024

Mathematics

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“…These compute kernels are then bound and executed by a larger Python script. The MC/DC team studied other acceleration and abstraction techniques (e.g., PyKokkos (Al Awar et al, 2021), PyOpenCL, and PyCUDA (Klöckner et al, 2012)) but determined that Numba was the least intrusive and easiest to use, while still providing significant speed-up for Monte Carlo applications (Morgan, Palmer, and Niemeyer, 2022).…”

Section: Mc/dc+harmonize: Jit Compilation and Asynchronous Gpu Schedu...mentioning

confidence: 99%

A Quasi–Monte Carlo Method With Krylov Linear Solvers for Multigroup Neutron Transport Simulations

Pasmann

Ilham

Kelley

et al. 2023

Nuclear Science and Engineering

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The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25-26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used by various code teams, and consider the adoption of industry-standard benchmark problems to better facilitate code-to-code performance comparisons. A large portion of the meeting included candid discussions of direct experiences with approaches that have and have not worked. Participants reported that identifying and implementing suitable Monte Carlo algorithms for GPUs continues to be a sticking point. They also report significant difficulty porting existing algorithms between GPU APIs (specifically Nvidia CUDA to AMD ROCm). To better compare code-to-code performance, participants decided to design a C5G7like benchmark problem with a defined figure of merit, with the expectation of adding more benchmarks in the future. Problem specifications and results will eventually be hosted in a public repository and will be open to submissions by all Monte Carlo transport codes capable of running the benchmark problem. The participants also identified the need to explore the intermediate and long-term future of the Monte Carlo neutron transport community and how best to modernize and contextualize Monte Carlo as a useful tool in modern industry. Overall the summit was considered to be a success by the organizers and participants, and the group shared a strong desire for future, potentially larger, Monte Carlo summits.

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“…The script implemented in Python [27] processes the data from the sensors using an algorithm specific to artificial intelligence [28]. The C# programming language [29] is used both for the implementation of the graphical user interface and for the development of the virtual application using the Unity development environment.…”

Section: Software Application Developmentmentioning

confidence: 99%

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

Covaciu

Iordan

2022

Micromachines

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In recent years, drones have been widely used in various applications, from entertainment, agriculture, their use in photo and video services, military applications and so on. The risk of accidents while using a drone is quite high. To meet this risk, the most important solution is to use a device that helps and simplifies the control of a drone; in addition, the training of drone pilots is very important. To train the drone pilots, both physical and virtual environments can be used, but the probability of an accident is higher for beginners, so the safest method is to train in a virtual environment. The aim of this study is to develop a new device for controlling a drone in a virtual environment. This device is attached to the upper limb of the person involved in the control of that drone. For precise control, the newly created device uses MEMS sensor technology and artificial intelligence-specific methods.

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A performance portability framework for Python

Cited by 19 publications

References 19 publications

An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

An Optimized LSTM Neural Network for Accurate Estimation of Software Development Effort

A Quasi–Monte Carlo Method With Krylov Linear Solvers for Multigroup Neutron Transport Simulations

Control of a Drone in Virtual Reality Using MEMS Sensor Technology and Machine Learning

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