A fast GPU Monte Carlo implementation for radiative heat transfer in graded-index media

Shao, Jiang; Zhu, Keyong; Huang, Yong

doi:10.1016/j.jqsrt.2021.107680

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…It is possible to accelerate MCRT calculations using GPUs [51][52][53][54][55][56]. Since the MCRT utilizes a large number of MC particles that are evolved independently from each other (at least, with a timestep), the many-core architecture can evolve particles in parallel, leading to a significant speed-up compared to serial calculations [57][58][59]. There have been many studies and applications of GPU-accelerated MCRT [60][61][62][63].…”

Section: Introductionmentioning

confidence: 99%

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Askar,

Yergaliyev,

Shukirgaliyev

et al. 2024

Computation

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This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport in plane-parallel geometry on three GPU cards: NVIDIA Tesla A100, Tesla V100, and GeForce RTX3080. We compared Numba and CuPy to each other and our CUDA C implementation. The results show that CUDA C, as expected, has the fastest performance and highest energy efficiency, while Numba offers comparable performance when data movement is minimal. While CuPy offers ease of implementation, it performs slower for compute-heavy tasks.

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Section: Introductionmentioning

confidence: 99%

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Askar,

Yergaliyev,

Shukirgaliyev

et al. 2024

Computation

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show abstract

“…Shao et al (2021) [15] developed two-and three-dimensional models optimized for graded-index (GRIN) media using parallel computing on GPUs to enhance processing. Computational times were compared between GPU implementations using an NVIDIA GeForce ® GTX ™ 1080 Ti and CPU implementations using the Intel ® Core ™ i7 8750H and the Xeon ™ Gold 5120 processors.…”

Section: Introductionmentioning

confidence: 99%

An Implementation of LASER Beam Welding Simulation on Graphics Processing Unit Using CUDA

Nascimento,

Magalhães,

Azevedo

et al. 2024

Computation

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The maximum number of parallel threads in traditional CFD solutions is limited by the Central Processing Unit (CPU) capacity, which is lower than the capabilities of a modern Graphics Processing Unit (GPU). In this context, the GPU allows for simultaneous processing of several parallel threads with double-precision floating-point formatting. The present study was focused on evaluating the advantages and drawbacks of implementing LASER Beam Welding (LBW) simulations using the CUDA platform. The performance of the developed code was compared to that of three top-rated commercial codes executed on the CPU. The unsteady three-dimensional heat conduction Partial Differential Equation (PDE) was discretized in space and time using the Finite Volume Method (FVM). The Volumetric Thermal Capacitor (VTC) approach was employed to model the melting-solidification. The GPU solutions were computed using a CUDA-C language in-house code, running on a Gigabyte Nvidia GeForce RTX™ 3090 video card and an MSI 4090 video card (both made in Hsinchu, Taiwan), each with 24 GB of memory. The commercial solutions were executed on an Intel® Core™ i9-12900KF CPU (made in Hillsboro, Oregon, United States of America) with a 3.6 GHz base clock and 16 cores. The results demonstrated that GPU and CPU processing achieve similar precision, but the GPU solution exhibited significantly faster speeds and greater power efficiency, resulting in speed-ups ranging from 75.6 to 1351.2 times compared to the CPU solutions. The in-house code also demonstrated optimized memory usage, with an average of 3.86 times less RAM utilization. Therefore, adopting parallelized algorithms run on GPU can lead to reduced CFD computational costs compared to traditional codes while maintaining high accuracy.

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Numerical analysis of the radiant heating effectiveness of a continuous glass annealing furnace

Altun

Basol

2022

Applied Thermal Engineering

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A fast GPU Monte Carlo implementation for radiative heat transfer in graded-index media

Cited by 4 publications

References 30 publications

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

Exploring Numba and CuPy for GPU-Accelerated Monte Carlo Radiation Transport

An Implementation of LASER Beam Welding Simulation on Graphics Processing Unit Using CUDA

Numerical analysis of the radiant heating effectiveness of a continuous glass annealing furnace

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