… With the parallel algorithm, the execution time was reduced by a factor of 290x compared to
… successfully by using a parallel GPU implementation of a evolutionary algorithm. In that study…

… To reduce computational cost, it is necessary to study efficient parallel algorithms and
implement performance optimizations for climate models. Wehner et al. used a two-dimensional …

… and optimizing the power grid’s performance… predicting system peak load. This article aims
to build an accurate long-term load forecasting model based on deep learning (DL) algorithms…

… algorithms to find optimal solutions. In this paper, we formulate the problem … optimization
of data-parallel applications on modern heterogeneous HPC platforms for performance…

… search algorithm named AB-MOEA/D by combining multi-objective optimization algorithm and
performance prediction model. AB… -based multi-objective optimization algorithm to find the …

… optimization model that can automatically and rapidly generate an optimally parallel
preconditioned conjugate gradient (PCG) algorithm … For our proposed model, there are the …

… In this paper, the PV model parameter estimation is an optimization process that minimize
OF(θ) by continuously adjusting the solution vector θ within the specified search area. …

… and robustness of passenger hotspot prediction (PHP), this paper proposes a parallel
Grid-Search-based Support Vector Machine (GS-SVM) optimization algorithm on Spark, which …

… implement the hidden parallelism is the main objective of this … optimizing and mapping
hard-to-parallel algorithms and programs to pipeline and network implementations. The algorithm …

… categorized into searching algorithms and trial schedulers. This section also evaluates the
efficiency and applicability of these algorithms for different machine learning models. Section …