HumanISE - Indexed Articles in Journals

Permanent URI for this collection

http://localhost:4000/handle/123456789/141

Browse

Recent Submissions

Now showing 1 - 5 of 180
  • Item
    Task-to-Accelerator Mapping for Heterogeneous Systems Using Heuristics
    ( 2025) Mohammad Samadi ; 10019
    Heterogeneous hardware platforms can be used to improve the performance of computing systems in terms of application response time. They are in high demand, especially with the recent emergence of complex AI applications. Parallel applications can be used on these platforms to utilize most of the hardware system capacity and decrease the execution time. However, predictability is still a notable challenge on these platforms when used in time-critical systems due to the variability in the execution of parallel runtime systems, like OpenMP. Therefore, this paper proposes a new heterogeneous task-to-accelerator mapping approach using heuristics to improve the performance and predictability of OpenMP applications running on heterogeneous systems.
  • Item
  • Item
    Task-to-accelerator mapping for predictable OpenMP applications
    ( 2026) Mohammad Samadi ; 10019
    OpenMP offers a high-level, portable programming model that simplifies the expression of parallelism and offloading in heterogeneous platforms, making it an ideal framework for managing task execution across CPUs and GPUs. However, as applications increasingly require predictability and multi-GPU execution, the lack of predictability in current OpenMP scheduling mechanisms poses significant challenges. Irregular workloads, variable data transfers, and dynamic resource contention often lead to inconsistent response times and reduced timing determinism. This unpredictability limits system efficiency and reliability, particularly in performance-critical and real-time domains. To overcome the challenge mentioned above, this paper proposes an efficient task-to-accelerator mapping (i.e., assigning tasks to GPU devices for execution) for multi-GPU OpenMP applications. The mechanism includes an offloading manager that operates a global queue and multiple local queues, and different heuristic algorithms that first allocate tasks to the most appropriate queues and then select the most suitable tasks from each queue. The proposed heuristics aim to balance queue loads and enhance the work-conserving property of the mapping process to minimize response time and response-time variability. The paper also presents a thorough evaluation using both synthetic graphs and graphs representative of real-world applications, showing that the proposed mapping method outperforms the other relevant mapping methods in terms of application response time and response-time variability.
  • Item
    Task-to-Thread Mapping in OpenMP Using Fuzzy Decision Making
    ( 2024) Mohammad Samadi ; 10019
    The performance of shared-resource multi-core hard-ware platforms in complex cyber-physical systems (CPSs), e.g., automotive industry, can be improved using task-based parallelism through OpenMP. However, most CPS require certain level of predictability, which challenges the efficient implementation of the task-to-thread mapping process. This exploratory work build on the fact that existing mapping methods mostly use elementary or heuristic algorithms, and the idea that artificial intelligence (AI) algorithms can be used to enhance the efficiency of such processes. Accordingly, this paper (1) evaluates the suitability of AI-based tech-niques in improving the performance of task-to-thread mapping in the OpenMP framework, and (2) proposes a hypothesis to perform an intelligent mapping using fuzzy logic for multi-queue schedulers to improve the predictability of the system. © 2025 Elsevier B.V., All rights reserved.
  • Item
    Combining low-code development with ChatGPT to novel no-code approaches: A focus-group study
    ( 2023) Henrique São Mamede ; José Luís Martins ; 6355 ; 6516
    Low-code tools are a trend in software development for business solutions due to their agility and ease of use. There are a certain number of vendors with such solutions. Still, in most Western countries, there is a clear need for the existence of greater quantities of certified and experienced professionals to work with those tools. This means that companies with more resources can attract and maintain those professionals, whilst other smaller organizations must rely on an endless search for this scarce resource. We will present and validate a model designed to transform ChatGPT into a low-code developer, addressing the demand for a more skilled human resource solution. This innovative tool underwent rigorous validation via a focus group study, engaging a panel of highly experienced experts. Their invaluable insights and feedback on the proposed model were systematically gathered and meticulously analysed.