GPU-acceleration of tensor renormalization with PyTorch using CUDA

Jha, Raghav G.; Samlodia, Abhishek

doi:10.1016/j.cpc.2023.108941

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Although some of these improved TRG methods were originally proposed for spin systems, recent numerical calculations have shown that these methods are also efficient for fermionic systems. Research on such improved algorithms has continued to progress in recent years; a new algorithm of loop-TNR [181], combinations of the Monte Carlo method and TRG [182][183][184], and application of the machine-learning techniques to the TRG [185][186][187]. The extension of these novel improved methods to fermionic systems is considered important in examining whether these methods are also valid for more general physical systems including fermions.…”

Section: Discussionmentioning

confidence: 99%

Tensor renormalization group for fermions

Akiyama,

Meurice,

Sakai

2024

J. Phys.: Condens. Matter

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We review the basic ideas of the Tensor Renormalization Group method and show how they can be applied for lattice field theory models involving relativistic fermions and Grassmann variables in arbitrary dimensions. We discuss recent progress for entanglement filtering, loop optimization, bond-weighting techniques and matrix product decompositions for Grassmann tensor networks. The new methods are tested with two-dimensional Wilson--Majorana fermions and multi-flavor Gross--Neveu models. We show that the methods can also be applied to the fermionic Hubbard model in 1+1 and 2+1 dimensions.

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Section: Discussionmentioning

confidence: 99%

Tensor renormalization group for fermions

Akiyama,

Meurice,

Sakai

2024

J. Phys.: Condens. Matter

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“…Qianjiao Wu et al [29] CUDA algorithm improved the computational efficiency of multiscale DEM analysis, reducing response times.Engels et al [30]'s CUDA-SHAPE algorithm and Axel Davy et al [31] GPU-accelerated denoising solution both enhanced the performance of their respective software and algorithms. The works of Bhaskar Jyoti Borah et al [32] and Dariusz Puchala and Kamil Stokfiszewski [33] further demonstrated the effectiveness of GPU acceleration in image processing.Tianru Xue et al [34] real-time anomaly detection technology and Raghav G. Jha et al [35] TRG accelerated computation method, along with Qiyang Xiong et al [36] PIC simulation optimization scheme, all showcase the potential of GPUs in enhancing remote sensing data processing capabilities. These studies provide new directions for efficient processing of remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Xiong,

Wang,

Qiao

et al. 2024

Preprint

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The computational efficiency is low when the vast volume of unmanned aerial vehicle airborne gamma-ray spectrum (UAVAGS) data is handled by wavelet denoising in CPU. So, a CUDA-based GPU parallel solution is recommended to resolve this issue in this paper. This proposed solution aims to significantly enhance the efficiency of parallel acceleration for wavelet denoising of UAVAGS data. In the preliminary stage, experiments were conducted with varying block sizes to investigate the influence of different block sizes on processing time. The objective was to identify the most suitable block size for efficiently processing UAVAGS data. Subsequently, a performance evaluation was conducted by comparing the acceleration ratios of GPU and CPU for different data volumes, as well as varying wavelet basis functions under the same data volume conditions. Finally, by intentionally introducing noise, calculations were performed to determine the optimal wavelet basis function concerning signal-to-noise ratio after denoising. The research findings indicate that the optimal two-dimensional block size falls within the range of 64×64 to 128×128. The majority of wavelet basis functions achieved acceleration ratios exceeding 100-fold in total processing time, with the coif5 wavelet basis function reaching an acceleration ratio of 185-fold. Comparative analysis of various denoising functions revealed that, under low signal-to-noise ratios, these functions exhibited insufficient denoising effects, while at high signal-to-noise ratios, there was a risk of excessive denoising. However, significant denoising effects were observed when employing hard thresholding with coif5, soft thresholding, and an improved thresholding method with db3.

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Physics-informed neural networks coupled with flamelet/progress variable model for solving combustion physics considering detailed reaction mechanism

Song,

Tang,

Xing

et al. 2024

Physics of Fluids

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In recent years, physics-informed neural networks (PINNs) have shown potential as a method for solving combustion physics. However, current efforts using PINNs for the direct predictions of multi-dimensional flames only use global reaction mechanisms. Considering detailed chemistry is crucial for understanding detailed combustion physics, and how to accurately and efficiently consider detailed mechanisms under the framework of PINNs has not been explored yet and is still an open question. To this end, this paper proposes a PINN/flamelet/progress variable (FPV) approach to accurately and efficiently solve combustion physics, considering detailed chemistry. Specifically, the combustion thermophysical properties are tabulated using several control variables, with the FPV model considering detailed chemistry. Then, PINNs are used to solve the governing equations of continuity, momentum, and control variables with the thermophysical properties extracted from the FPV library. The performance of the proposed PINN/FPV approach is assessed for diffusion flames in a two-dimensional laminar mixing layer by comparing it with the computational fluid dynamics (CFD) results. It has been found that the PINN/FPV model can accurately reproduce the flow and combustion fields, regardless of the presence or absence of observation points. The quantitative statistics demonstrated that the mean relative error was less than 10%, and R2 values were all higher than 0.94. The applicability and stability of this model were further verified on other unseen cases with variable parameters. This study provides an efficient and accurate method to consider detailed reaction mechanisms in solving combustion physics using PINNs.

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GPU-acceleration of tensor renormalization with PyTorch using CUDA

Cited by 5 publications

References 23 publications

Tensor renormalization group for fermions

Tensor renormalization group for fermions

Parallel Acceleration Algorithm for Wavelet Denoising of UAVAGS Data Based on CUDA

Physics-informed neural networks coupled with flamelet/progress variable model for solving combustion physics considering detailed reaction mechanism

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