Performance of CUDA GPU in Monte Carlo simulation of light-skin diffuse reflectance spectra

Yusoff, Muhammad Nur Salihin; Jaafar, Mohammad Suhaimi

doi:10.1109/iecbes.2012.6498056

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…This method achieved high performance which make that applicable by partially alleviating the main limitation of long computational run-times [9] Another application of Monte-Carlo simulation by using CUDA programming on GPU is the evaluation of light-skin diffuse reflectance spectra for Multi-Layered Media. The speedup for this case is 71.19 and it varies across the wavelengths [21] Ice engineers who work with different types of environmental inputs, estimate probabilistic distributions of sea ice parameters in order to simulate the interaction models with offshore structures. Also, they applied Monte-Carlo simulations on design of many models, based on the impact forces between sea ice and offshore structures.…”

Section: Related Workmentioning

confidence: 99%

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

Ayubian

Alawneh

Thijssen

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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High Performance Computing (HPC) has recently been considerably improved, for instance General Purpose computation on Graphics Processing Units (GPGPU) has been developed to accelerate parallel computing by using hundreds of cores simultaneously. GPU computing with Compute Unified Device Architecture (CUDA) is a new approach to solve complex problems and transform the GPU into a massively parallel processor. The present study applies this new technology to a Monte-Carlo simulation for a sea ice load application. The goal of this study is to measure the performance of the GPU and Multi-GPU against the serial Central Processing Unit (CPU), parallel CPU (OpenMP), MATLAB and MATLAB (Parallel for) implementations. Results show a speedup of up to 89,000 times, and reduction in elapsed time from about 3 hours to approximately 0.1 second.

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Section: Related Workmentioning

confidence: 99%

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

Ayubian

Alawneh

Thijssen

2016

2016 Summer Computer Simulation Conference (SCSC 2016)

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“…Optimization of a functional defined on a finite space cubic lattice relates to a wide range of problems from mathematics and physics to economics and operational research, which require high efficiency implementations. For example, in mathematics and natural sciences, Markov chain Monte Carlo (MCMC) simulations on cubic lattice are used in methodological studies of novel Monte Carlo techniques [13,14,37], spin models [9,17,44], quantum Monte Carlo [7,26,29], material design [47], bio-chemistry [31], etc. Keeping that in mind, we will focus on both the current implementation and general approach to the optimization of this type of algorithms.…”

Section: Introductionmentioning

confidence: 99%

Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms

Afanasyev

Lichmanov

Rudyak

et al. 2021

JSFI

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In this paper we demonstrate the process of efficient porting a software package for Markov chain Monte Carlo (MCMC) simulations on a finite cubic lattice on multiple modern architectures: Pascal, Volta and Turing NVIDIA GPUs, NEC SX-Aurora TSUBASA vector engines and Intel Xeon Gold processors. In the studied software, MCMC methodology is used for simulations of liquid crystal structures, but it can be as well employed in a wide range of problems of mathematical physics and numerical methods. The main goals of this work are to determine the best software optimization strategy for this class of algorithms and to examine the speed and the efficiency of such simulations on modern HPC platforms. We evaluate the effects of various optimizations, such as using more suitable memory access patterns, multitasking for efficient utilization of massive parallelism on the target architectures, improved cache hit-rates, parallel workload balancing, etc. We perform a detailed performance analysis for each target platform using software tools such as nvprof, Ftrace and VTune. On this basis, we evaluate and compare the efficiency of the developed computational kernels on different platforms and subsequently rank these platforms by their performance. The results show that NVIDIA GPU and NEC SX-Aurora TSUBASA platforms, although at first glance seem very different, require similar optimization approaches in many cases due to similarities in data processing principles.

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“…Alerstam et al firstly reported that GPU-based simulation tool can achieve massive speedup over traditional CPU code for homogeneous structures [18]. Yusoff and Jaafar [19] reported a CUDA based GPU acceleration version of MCML. Fang and Boas [20] implemented the acceleration version of 'tMCimg', named Monte Carlo eXtrem (MCX), for 3-D voxelized media.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Mode Optimized GPU Accelerated Monte Carlo Model for Light Propagation in 3-D Voxelized Bio-Tissues

Fang

Liu

et al. 2019

IEEE Access

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Monte Carlo simulation is a precise method to model light propagation in bio-tissues and has been considered the golden standard to estimate the result of other computation methods. But the huge computation burden limited the application. In this paper, we propose a parallel computing model using graphic card to accelerate the Monte Carlo simulation in 3-D voxelized media with the consideration of internal refraction. Optimization of the parallel mode is made by using segmentations and offered an extra boost of simulation speed. The acceleration efficiency affecting factors are investigated and the acceleration rate of the five segmented model is 32.6 times higher than non-GPU model and 1.66 times higher than non-optimized model for a real human head 3-D structure simulation. INDEX TERMS Bio-tissue, graphic card acceleration, light propagation, Monte Carlo simulation, parallel mode.

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Performance of CUDA GPU in Monte Carlo simulation of light-skin diffuse reflectance spectra

Cited by 5 publications

References 22 publications

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

GPU-Based Monte-Carlo Simulation for a Sea Ice Load Application

Efficient Implementation of Liquid Crystal Simulation Software on Modern HPC Platforms

A Parallel Mode Optimized GPU Accelerated Monte Carlo Model for Light Propagation in 3-D Voxelized Bio-Tissues

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