Highly scalable parallel genetic algorithm on Sunway many-core processors

Xiao, Zhiyong; Xu, Liu; Xu, Jia; Sun, Qingxiao; Gan, Lin

doi:10.1016/j.future.2020.08.028

Cited by 15 publications

(2 citation statements)

References 23 publications

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“…To establish an optimal benchmark for energy forecasting in utilities and buildings, effective energy forecasting approaches must be complemented by suitable management and operational strategies (Ahmad et al, 2020). The utilization of a web-based parallel genetic model for resource computing has been proposed to reduce network computation time (Xiao et al, 2021). Artificial Neural Networks (ANNs) have been extensively applied, constituting approximately 40% of reviewed energy forecasting models.…”

Section: Examining Various Models Used For Forecastingmentioning

confidence: 99%

Forecasting Energy Consumption with AI: A Review for Sustainable Energy Management

Iriakuma,

Ukela,

Uche

2024

IJLTEMAS

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This paper provides a comprehensive review of the integration of artificial intelligence (AI) within the context of Industry 4.0, emphasizing its transformative impact on various industries and its specific applications in energy consumption forecasting for sustainable energy management. Beginning with a historical perspective on industrial evolution, from automation to the current cyber-physical systems era, the review highlights the pivotal role of AI in reshaping manufacturing processes. The article explores the diverse applications of AI in the energy sector, particularly its effectiveness in short-term load forecasting, demand response optimization, and accurate predictions for renewable energy sources like solar and wind. The growing complexity of power systems due to decentralization and the proliferation of grid-connected devices is discussed, underscoring the importance of effective information exchange facilitated by AI. Additionally, the review delves into various models used for energy forecasting, including supervised learning models, artificial neural networks, and deep learning models. The practical applications of AI in power system control, management, energy market pricing, and policy recommendations are outlined, showcasing its potential in optimizing energy efficiency and balancing electricity production and consumption. The practical examples of AI's role in improving predictions of supply and demand, such as Google's subsidiary DeepMind enhancing wind power output forecasts, highlight the real-world impact of these technologies. However, the abstract also acknowledges existing challenges, including insufficient theoretical background, practical expertise, and financial constraints hindering widespread AI adoption in the energy industry. In conclusion, the article offers valuable insights into the current state, challenges, and potential of AI in forecasting energy consumption, providing a roadmap for sustainable energy management across diverse industries.

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Section: Examining Various Models Used For Forecastingmentioning

confidence: 99%

Forecasting Energy Consumption with AI: A Review for Sustainable Energy Management

Iriakuma,

Ukela,

Uche

2024

IJLTEMAS

View full text Add to dashboard Cite

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“…The two works together provide an efficient computing platform for specific applications, and different parallelism strategies can be used to solve different problems in the field of molecular docking (Altuntaş et al, 2016). Heterogeneous multicore architectures have advantages in the field of molecular docking owing to their unique architecture (Fu et al, 2016;Chen et al, 2017;Xiao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Redesigning Vina@QNLM for Ultra-Large-Scale Molecular Docking and Screening on a Sunway Supercomputer

Wei

Wang

et al. 2021

Front. Chem.

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Ultra-large-scale molecular docking can improve the accuracy of lead compounds in drug discovery. In this study, we developed a molecular docking piece of software, Vina@QNLM, which can use more than 4,80,000 parallel processes to search for potential lead compounds from hundreds of millions of compounds. We proposed a task scheduling mechanism for large-scale parallelism based on Vinardo and Sunway supercomputer architecture. Then, we readopted the core docking algorithm to incorporate the full advantage of the heterogeneous multicore processor architecture in intensive computing. We successfully expanded it to 10, 465, 065 cores (1,61,001 management process elements and 0, 465, 065 computing process elements), with a strong scalability of 55.92%. To the best of our knowledge, this is the first time that 10 million cores are used for molecular docking on Sunway. The introduction of the heterogeneous multicore processor architecture achieved the best speedup, which is 11x more than that of the management process element of Sunway. The performance of Vina@QNLM was comprehensively evaluated using the CASF-2013 and CASF-2016 protein–ligand benchmarks, and the screening power was the highest out of the 27 pieces of software tested in the CASF-2013 benchmark. In some existing applications, we used Vina@QNLM to dock more than 10 million molecules to nine rigid proteins related to SARS-CoV-2 within 8.5 h on 10 million cores. We also developed a platform for the general public to use the software.

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Optimizing island sequencing in laser powder bed fusion using Genetic Algorithms

Ball,

Raut,

Basak

2024

Neural Comput & Applic

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Highly scalable parallel genetic algorithm on Sunway many-core processors

Cited by 15 publications

References 23 publications

Forecasting Energy Consumption with AI: A Review for Sustainable Energy Management

Forecasting Energy Consumption with AI: A Review for Sustainable Energy Management

Redesigning Vina@QNLM for Ultra-Large-Scale Molecular Docking and Screening on a Sunway Supercomputer

Optimizing island sequencing in laser powder bed fusion using Genetic Algorithms

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