Multi-core-CPU and GPU-accelerated radiative transfer models based on the discrete ordinate method

Efremenko, Dmitry; Loyola, Diego; Doicu, Adrian; Spurr, Robert

doi:10.1016/j.cpc.2014.07.018

Cited by 27 publications

(20 citation statements)

References 33 publications

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“…This facilitates the inclusion of different physical models, as all that is needed to incorporate a new model is the relevant definition of the emulator (e.g., the training input/output pairs and the hyperparameters). The gain in speed also suggests that a generic function like a GP might be a better target for optimisation and the use of massive parallel architectures: rather than adapt the physical RT model to meet the requirements of the parallel architecture (as done in e.g., [75]), it is easier to provide a parallel implementation of the emulator code, that is then generic for many models. It should also be noted that emulators allow model developers to be more ambitious with the numerical complexity of their models: it is possible to start considering more realistic or accurate models, as provided a fast emulator is available, running the original model will not result in a processing bottleneck.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

2016

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Abstract:There is an increasing need to consistently combine observations from different sensors to monitor the state of the land surface. In order to achieve this, robust methods based on the inversion of radiative transfer (RT) models can be used to interpret the satellite observations. This typically results in an inverse problem, but a major drawback of these methods is the computational complexity. We introduce the concept of Gaussian Process (GP) emulators: surrogate functions that accurately approximate RT models using a small set of input (e.g., leaf area index, leaf chlorophyll, etc.) and output (e.g., top-of-canopy reflectances or at sensor radiances) pairs. The emulators quantify the uncertainty of their approximation, and provide a fast and easy route to estimating the Jacobian of the original model, enabling the use of e.g., efficient gradient descent methods. We demonstrate the emulation of widely used RT models (PROSAIL and SEMIDISCRETE) and the coupling of vegetation and atmospheric (6S) RT models targetting particular sensor bands. A comparison with the full original model outputs shows that the emulators are a viable option to replace the original model, with negligible bias and discrepancies which are much smaller than the typical uncertainty in the observations. We also extend the theory of GP to cope with models with multivariate outputs (e.g., over the full solar reflective domain), and apply this to the emulation of PROSAIL, coupled 6S and PROSAIL and to the emulation of individual spectral components of 6S. In all cases, emulators successfully predict the full model output as well as accurately predict the gradient of the model calculated by finite differences, and produce speed ups between 10,000 and 50,000 times that of the original model. Finally, we use emulators to invert leaf area index (LAI), leaf chlorophyll content (C ab ) and equivalent leaf water thickness (C w ) from a time series of observations from Sentinel-2/MSI, Sentinel-3/SLSTR and Proba-V observations. We use sophisticated Hamiltonian Markov Chain Monte Carlo (MCMC) methods that exploit the speed of the emulators as well as the gradient estimation, a variational data assimilation (DA) method that extends the problem with temporal regularisation, and a particle filter using a regularisation model. The variational and particle filter approach appear more successful (meaning parameters closer to the truth, and smaller uncertainties) than the MCMC approach as a result of using the temporal regularisation mode. These work therefore suggests that GP emulators are a practical way to implement sophisticated parameter retrieval schemes in an era of increasing data volumes.

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Section: Discussion and Outlookmentioning

confidence: 99%

Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

2016

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“…In addition to research on the acceleration of hyperspectral remote sensing algorithms, GPUs and multicore processors have also been utilized for a few quantitative remote sensing applications. Efremenko et al developed and compared multicore and GPU implementations of a radiative transfer model based on the discrete ordinate solution method [12]. Su et al proposed a GPU implementation for the Monte-Carlo-based electromagnetic scattering of a double-layer vegetation model [13].…”

Section: Introductionmentioning

confidence: 99%

Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency

Liu

Feld

Xue

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Quantitative retrieval is a growing area in remote sensing due to the rapid development of remote instruments and retrieval algorithms. The aerosol optical depth (AOD) is a significant optical property of aerosol which is involved in further applications such as the atmospheric correction of remotely sensed surface features, monitoring of volcanic eruptions or forest fires, air quality, and even climate changes from satellite data. The AOD retrieval can be computationally expensive as a result of huge amounts of remote sensing data and compute-intensive algorithms. In this paper, we present two efficient implementations of an AOD retrieval algorithm from the moderate resolution imaging spectroradiometer (MODIS) satellite data. Here, we have employed two different high performance computing architectures: multicore processors and a graphics processing unit (GPU). The compute unified device architecture C (CUDA-C) has been used for the GPU implementation for NVIDIA's graphic cards and open multiprocessing (OpenMP) for threadparallelism in the multicore implementation. We observe for the Manuscript GPU accelerator, a maximal overall speedup of 68.x for the studied data, whereas the multicore processor achieves a reasonable 7.x speedup. Additionally, for the largest benchmark input dataset, the GPU implementation also shows a great advantage in terms of energy efficiency with an overall consumption of 3.15 kJ compared to 58.09 kJ on a CPU with 1 thread and 38.39 kJ with 16 threads. Furthermore, the retrieval accuracy of all implementations has been checked and analyzed. Altogether, using the GPU accelerator shows great advantages for an application in AOD retrieval in both performance and energy efficiency metrics. Nevertheless, the multicore processor provides the easier programmability for the majority of today's programmers. Our work exploits the parallel implementations, the performance, and the energy efficiency features of GPU accelerators and multicore processors. With this paper, we attempt to give suggestions to geoscientists demanding for efficient desktop solutions.Index Terms-Aerosol optical depth (AOD), graphics processing unit (GPU), High performance computing (HPC), OpenMP, quantitative remote sensing retrieval.

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“…Simulation methods that commonly used now are mainly Adding-Doubling Method [15], Matrix Operator Method [16], Discrete Ordinate Method [17][18][19], Successive Scattering Method [20] and Monte Carlo method [21,22] etc. These methods have good adaptability and high accuracy in researching the energy attenuation of pulse laser and the spread of time and space characteristics.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional imaging simulation of active laser detection based on DLOS method

Zhang

Zhou

Chen

et al. 2016

Infrared Physics & Technology

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Multi-core-CPU and GPU-accelerated radiative transfer models based on the discrete ordinate method

Cited by 27 publications

References 33 publications

Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences

Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency

Three-dimensional imaging simulation of active laser detection based on DLOS method

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