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Conventional weather forecasting relies on numerical weather prediction (NWP), which solves atmospheric equations using numerical methods. The Korea Meteorological Administration (KMA) adopted the Met Office Global Seasonal Forecasting System version 6 (GloSea6) NWP model from the UK and runs it on a supercomputer. However, due to high task demands, the limited resources of the supercomputer have caused job queue delays. To address this, the KMA developed a low-resolution version, Low GloSea6, for smaller-scale servers at universities and research institutions. Despite its ability to run on less powerful servers, Low GloSea6 still requires significant computational resources like those of high-performance computing (HPC) clusters. We integrated deep learning with Low GloSea6 to reduce execution time and improve meteorological research efficiency. Through profiling, we confirmed that deep learning models can be integrated without altering the original configuration of Low GloSea6 or complicating physical interpretation. The profiling identified "tri_sor.F90" as the main CPU time hotspot. By combining the biconjugate gradient stabilized (BiCGStab) method, used for solving the Helmholtz problem, with a deep learning model, we reduced unnecessary hotspot calls, shortening execution time. We also propose a convolutional block attention module-based Half-UNet (CH-UNet), a lightweight 3D-based U-Net architecture, for faster deep-learning computations. In experiments, CH-UNet showed 10.24% lower RMSE than Half-UNet, which has fewer FLOPs. Integrating CH-UNet into Low GloSea6 reduced execution time by up to 71 s per timestep, averaging a 2.6% reduction compared to the original Low GloSea6, and 6.8% compared to using Half-UNet. This demonstrates that CH-UNet, with balanced FLOPs and high predictive accuracy, offers more significant execution time reductions than models with fewer FLOPs. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) is a recent swarm intelligence‐based meta‐heuristic optimisation algorithm that has shown excellent results for a broad class of optimisation problems in diverse fields. However, CS is generally compute intensive and slow when implemented in software requiring large number of fitness function evaluations to obtain acceptable solutions. In this study, the authors present a problem specific parallel pipelined field programmable gate array‐based accelerator to reduce execution time when solving complex optimisation problems. Experiments conducted on a large number of well‐known benchmark functions revealed that the hardware approach offers a promising average speedup of 75× and 53× than software and GPU implementations, respectively. [ABSTRACT FROM AUTHOR]

This study presents the work carried out by the authors to apply Intel MKL PARDISO (a parallel sparse matrix solver) to the load flow solution algorithms of MATPOWER and OpenDSS. The goal is to explore the potential execution time reduction obtained when working with large power systems and multi-core installations. Test systems of different sizes were solved in order to observe the time reduction as function of the system size and the number of cores used in the parallel execution. Results show that except for the full Newton–Raphson algorithm, one should not expect a reduction in execution times when using a parallel routine. The use of a parallel sparse matrix solver is only justified when the sparse matrix must be recalculated at every step of the solution process or the systems under study are much larger than those analysed here. This study also presents how a parallel computing solution can be implemented in different applications by using available high-performance parallel libraries.

This study presents design and hardware implementation of cascade neural network (NN) based flux estimator using field programmable gate array (FPGA) for speed estimation in induction motor drives. The main focus of this study is the FPGA implementation of cascade NN based flux estimator. The major issues in FPGA implementation are optimisation of cost (resource) and execution time. A simple non‐linear activation function called as Elliott function is used to reduce the execution time. To reduce the cost, and effectively utilise resource, the concept of layer multiplexing is adopted. The lowest bit precision needed for good performance of the estimator is identified and implemented. The proposed NN based flux estimator using simple excitation function and minimum bit precision is implemented using layer multiplexing technique. The designed estimator is tested on Spartan FPGA kit (3sd1800afg676‐4) and the results obtained are presented. [ABSTRACT FROM AUTHOR]