Multicore Embedded Worst-Case Task Design Issues and Analysis Using Machine Learning Logic

Aradhya, Sumalatha; Thejaswini, S; Nagaveni, V.

doi:10.1007/978-981-16-3945-6_52

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…It is the heart of many signal processing and exploring the best algorithmic tool. Examples of their modern applications and usage include image reconstruction in life sciences (Dan et al, 2021;Prigent et al, 2023), visualization of large biological specimens in bioinformatics (Muhlich et al, 2022), weather simulations (Khouzami et al, 2022;Grady et al, 2023), option price prediction in financial mathematics (Alfeus and Schlögl, 2019;Phelan et al, 2019;Salavi et al, 2022), and machine learning (Dao et al, 2019;Aradhya et al, 2022). There are also other applications of FFTW in audio engineering (Faerman et al, 2021(Faerman et al, , 2020.…”

Section: Fftw Software Librarymentioning

confidence: 99%

Parallel computation to bidimensional heat equation using MPI/CUDA and FFTW package

Chakkour

2024

Front. Comput. Sci.

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In this study, we present a fast algorithm for the numerical solution of the heat equation. The heat equation models the heat diffusion over time and through a given region. We engage a finite difference method to solve this equation numerically. The performance of its parallel implementation is considered using Message Passing Interface (MPI), Compute Unified Device Architecture (CUDA), and time schemes, such as Forward Euler (FE) and Runge-Kutta (RK) methods. The originality of this study is research on parallel implementations of the fourth-order Runge-Kutta method (RK4) for sparse matrices on Graphics Processing Unit (GPU) architecture. The supreme proprietary framework for GPU computing is CUDA, provided by NVIDIA. We will show three metrics through this parallelization to compare the computing performance: time-to-solution, speed-up, and performance. The spectral method is investigated by utilizing the FFTW software library, based on the computation of the fast Fourier transforms (FFT) in parallel and distributed memory architectures. Our CUDA-based FFT, named CUFFT, is performed in platforms, which is a highly optimized FFTW implementation. We will give numerical tests to reveal that this method is up-and-coming for solving the heat equation. The final result demonstrates that CUDA has a significant advantage and performance since the computational cost is tiny compared with the MPI implementation. This vital performance gain is also achieved through careful attention of managing memory communication and access.

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Section: Fftw Software Librarymentioning

confidence: 99%

Parallel computation to bidimensional heat equation using MPI/CUDA and FFTW package

Chakkour

2024

Front. Comput. Sci.

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“…Research based on machine learning has been performed to predict, improve, and secure WCET estimations, when complete WCET calculations are not derivable during design time, which is relevant to our research as our current machine learning models rely on such measured WCET values [14,15]. The usage of machine learning for solving the challenges of schedulability analysis has also been analyzed by researchers in several disciplines.…”

Section: Related Workmentioning

confidence: 99%

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

Delgadillo

Blieninger

Kuhn

et al. 2022

JLPEA

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Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between different ECUs, which adds redundancy and fail-safety capabilities to the system. In this paper, we present a setup with a generalistic and modular architecture that allows for integrating and testing different ECU architectures and machine learning (ML) models. As part of a holistic testbed, we introduce a collection of reproducible tasks, as well as a toolchain that controls the dynamic migration of tasks depending on ECU status and load. The migration is aided by the machine learning predictions on the schedulability analysis of possible future task distributions. To demonstrate the capabilities of the setup, we show its integration with FreeRTOS-based ECUs and two ML models—a long short-term memory (LSTM) network and a spiking neural network—along with a collection of tasks to distribute among the ECUs. Our approach shows a promising potential for machine-learning-based schedulability analysis and enables a comparison between different ML models.

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Multicore Embedded Worst-Case Task Design Issues and Analysis Using Machine Learning Logic

Cited by 2 publications

References 23 publications

Parallel computation to bidimensional heat equation using MPI/CUDA and FFTW package

Parallel computation to bidimensional heat equation using MPI/CUDA and FFTW package

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

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