6 papers · ranked by Valyu relevance
Vladislav Skorpil, Vaclav Oujezsky, Arcangelo Castiglione, Gianni D’Angelo
'Gianni D’Angelo'] This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are…
Fabian Woller, Lis Arend, Christian Fuchsberger, Markus List + 1 more
To demonstrate the practical applicability of NApy on a real-world dataset, all statistical tests were performed on the CHRIS study data both without any parallelization (single thread) and with heavy parallelization (64 threads). For correlations, pandas was used as comparison as it was the fastest compared to other…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
Emad Alamoudi, Felipe Reck, Nils Bundgaard, Frederik Graw + 4 more
'Lutz Brusch' 'Jan Hasenauer' 'Yannik Schälte' 'Abel C.H. Chen'] Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often…
Jan Janssen, Janine George, Julian Geiger, Marnik Bercx + 7 more
Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow…
Reza Rafati Bonab, Ali Akbar Jamali, Kyle Klenk, Mohammad Mahdi Moayeri + 2 more
Parallelization of the SW algorithm can be implemented on different levels: single-instruction, multiple-data (SIMD) parallelism, thread-level parallelism, process-level parallelism, and heterogeneous parallelism. SIMD parallelism, also known as vector-level parallelism, employs a controller to manage multiple…