A GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions

Liang, Yihao; Xing, Xiangjun; Li, Yaohang

doi:10.1016/j.jcp.2017.02.069

Cited by 15 publications

(11 citation statements)

References 42 publications

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“…The rapid development of GPU cards in recent decades led to widespread adoption in many areas of scientific computing [1][2][3][4][5][6]. If efficiently utilized, the many-core architecture of the GPUs enables high-performance parallel computations.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Pseudo-Random Number Generation on GPU Cards

et al. 2021

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Monte Carlo methods rely on sequences of random numbers to obtain solutions to many problems in science and engineering. In this work, we evaluate the performance of different pseudo-random number generators (PRNGs) of the Curand library on a number of modern Nvidia GPU cards. As a numerical test, we generate pseudo-random number (PRN) sequences and obtain non-uniform distributions using the acceptance-rejection method. We consider GPU, CPU, and hybrid CPU/GPU implementations. For the GPU, we additionally consider two different implementations using the host and device application programming interfaces (API). We study how the performance depends on implementation parameters, including the number of threads per block and the number of blocks per streaming multiprocessor. To achieve the fastest performance, one has to minimize the time consumed by PRNG seed setup and state update. The duration of seed setup time increases with the number of threads, while PRNG state update decreases. Hence, the fastest performance is achieved by the optimal balance of these opposing effects.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Pseudo-Random Number Generation on GPU Cards

et al. 2021

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“…Recently, in various fields, the computation time for the Monte Carlo optimization method has been significantly reduced by hardware implementations on graphical processing units (GPU) and field-programmable gate arrays (FPGA). Liang et al [5] proposed a GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions. Their work reached remarkable speedup, compared with the serial implementation on a central processing unit (CPU).…”

Section: Introductionmentioning

confidence: 99%

“…Find the global best configuration as the optimal value 17: End process Next, the time and design margin constraints for the sampled configuration were respectively calculated by modifying Equations (4) and (5) with consideration of the uncertainties of T ISR and T PD as follows:…”

mentioning

confidence: 99%

Real-Time Monte Carlo Optimization on FPGA for the Efficient and Reliable Message Chain Structure

Lee

Kim

2019

Electronics

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This paper addresses the real-time optimization problem to find the most efficient and reliable message chain structure in data communications based on half-duplex command-response protocols such as MIL-STD-1553B communication systems. This paper proposes a real-time Monte Carlo optimization method implemented on field programmable gate arrays (FPGA) which can not only be conducted very quickly but also avoid the conflicts with other tasks on a central processing unit (CPU). Evaluation results showed that the proposed method can consistently find the optimal message chain structure within a quite small and deterministic time, which was much faster than the conventional Monte Carlo optimization method on a CPU.Abstract: This paper addresses the real-time optimization problem to find the most efficient and reliable message chain structure in data communications based on half-duplex command-response protocols such as MIL-STD-1553B communication systems. This paper proposes a real-time Monte Carlo optimization method implemented on field programmable gate arrays (FPGA) which can not only be conducted very quickly but also avoid the conflicts with other tasks on a central processing unit (CPU). Evaluation results showed that the proposed method can consistently find the optimal message chain structure within a quite small and deterministic time, which was much faster than the conventional Monte Carlo optimization method on a CPU.

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“…Likewise, in the fluid mechanics field, Khajeh et al [16] and Salvadore et al [17] have been porting a Navier-Stokes solver on GPUs obtaining speedups up to 22 ×. Additionally, many Monte Carlo codes have been developed on graphical processing units for many diverse fields and applications such as finance [18] and molecular dynamics [19]. On the other hand, to the authors knowledge, the only MC method applied to the solution of thermal radiation implemented on GPU was developed by Humphrey et al [20] for grey gas applications.…”

Section: Introductionmentioning

confidence: 99%

“…Comparison of MC implementation with the line-by-line solution for H20 (left) and CO2 (right) at 1 [atm] with a parabolic temperature profile, equation(19).…”

mentioning

confidence: 99%

A fast GPU Monte Carlo radiative heat transfer implementation for coupling with direct numerical simulation

Silvestri

Pečnik

2019

Journal of Computational Physics: X

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We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve radiative heat transfer in turbulent flows of non-grey participating media that can be coupled to fully resolved turbulent flows, namely to Direct Numerical Simulation (DNS). The spectrally varying absorption coefficient is treated in a narrow-band fashion with a correlated-k distribution. The implementation is verified with analytical solutions and validated with results from literature and line-by-line Monte Carlo computations. The method is implemented on GPU with a thorough attention to memory transfer and computational efficiency. The bottlenecks that dominate the computational expenses are addressed and several techniques are proposed to optimize the GPU execution. By implementing the proposed algorithmic accelerations, a speed-up of up to 3 orders of magnitude can be achieved, while maintaining the same accuracy.

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A GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions

Cited by 15 publications

References 42 publications

Evaluation of Pseudo-Random Number Generation on GPU Cards

Evaluation of Pseudo-Random Number Generation on GPU Cards

Real-Time Monte Carlo Optimization on FPGA for the Efficient and Reliable Message Chain Structure

A fast GPU Monte Carlo radiative heat transfer implementation for coupling with direct numerical simulation

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