Iterative methods with mixed-precision preconditioning for ill-conditioned linear systems in multiphase CFD simulations

Ina, Takuya; Idomura, Yasuhiro; Imamura, Toshiyuki; Yamashita, Susumu; Onodera, Naoyuki

doi:10.1109/scala54577.2021.00006

Cited by 2 publications

(1 citation statement)

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“…As a result, mixed-precision floating-point computations based on single and double precision have been used in multiple research areas [34]- [37]. Inspired by this, in order to further improve the computational efficiency of preconditioning algorithms without losing their effectiveness, some researchers have attempted to construct preconditioners in mixed-precision mode [4], [38]- [43]. However, research on mixed-precision SPAI preconditioning algorithms is scarce.…”

Section: Introductionmentioning

confidence: 99%

Mixed-Precision Sparse Approximate Inverse Preconditioning Algorithm on GPU

Chu

2023

IEEE Access

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In this study, in order to further improve the construction efficiency of sparse approximate inverse (SPAI) preconditioners, we attempt to explore the construction method of SPAI preconditioners in mixed-precision mode from the perspective of single and double precision mixing, and thus propose two mixed-precision SPAI preconditioning algorithms on GPU, abbreviated as MP-SSPAI and MP-HeuriSPAI, respectively. In MP-SSPAI, with original static SPAI preconditioning algorithm as the research object, we mainly consider the following factors to construct its preconditioner in mixed-precision mode: 1) use single precision as much as possible to improve computational efficiency of the preconditioner while ensuring its validity; 2) store certain components in single precision after they have been determined to require single-precision computation to improve read efficiency; and 3) maintain the high-precision output of the preconditioner to ensure that it is computed with high precision when applied to the iterative algorithm. In MP-HeuriSPAI, a mixed-precision heuristic dynamic SPAI preconditioning algorithm on GPU is presented based on the above factors, using HeuriSPAI as the object of study. The experimental results demonstrate the effectiveness and high performance of the proposed MP-SSPAI and MP-HeuriSPAI by comparing them with their respective double-precision versions, single-precision versions, and extended versions.INDEX TERMS GPU, mixed precision, preconditioning algorithm, sparse approximate inverse.

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Section: Introductionmentioning

confidence: 99%

Mixed-Precision Sparse Approximate Inverse Preconditioning Algorithm on GPU

Chu

2023

IEEE Access

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Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purification and Desalination

Shahouni,

Abbasi,

Dibaj

et al. 2024

Water

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Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes.

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Iterative methods with mixed-precision preconditioning for ill-conditioned linear systems in multiphase CFD simulations

Cited by 2 publications

References 19 publications

Mixed-Precision Sparse Approximate Inverse Preconditioning Algorithm on GPU

Mixed-Precision Sparse Approximate Inverse Preconditioning Algorithm on GPU

Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purification and Desalination

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