A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing

Humphrey, Alan; Harman, Todd; Berzins, Martin; Smith, Phillip J.

doi:10.1007/978-3-319-20119-1_16

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…A similar method, involving patches of interest, was previously implemented by Humphrey et al in two different occasions. Namely, in a parallel CPU Monte Carlo implementation [28] and in a grey gas GPU implementation [20]. They used this technique to reduce computational and communication time.…”

Section: Multigridmentioning

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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Section: Multigridmentioning

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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“…The 1000 MWe USC coal boiler being modeled by Uintah in this work has thermal radiation as a dominant heat transfer mode, and involves solving the conservation of energy equation and radiative heat transfer equation (RTE) simultaneously. This radiation calculation, in which the radiative-flux divergence at each cell of the discretized domain is calculated, can take up to 50% of the overall CPU time per timestep [11] using the discrete ordinates method (DOM) [6], one of the standard approaches to computing radiative heat transfer. Using a reverse Monte Carlo ray tracing approach combined with a novel use of Uintah's adaptive mesh refinement infrastructure, this calculation has been made to scale to 262K cores [11], and further adapted to run on up to 16K GPUs [12].…”

Section: Related Workmentioning

confidence: 99%

“…The heat transfer problem arising from the clean coal boilers being modeled by the Uintah framework has thermal radiation as a dominant heat transfer mode and involves solving the conservation of energy equation and radiative heat transfer equation (RTE) simultaneously [11]. Scalable modeling of radiation is currently one of the most challenging problems in large-scale simulations, due to the global, all-to-all nature of radiation [17], potentially affecting all regions of the domain simultaneously at a single instance in time.…”

Section: Radiation Modeling: Spatial Transport Sweepsmentioning

confidence: 99%

“…Reverse Monte Carlo Ray Tracing (RMCRT) [11] has been implemented on both CPUs and GPUs [11,12], and is a method for solving Eq. 2 by tracing radiation rays from one cell to the next, as described in detail in [11,12]. Reverse Monte Carlo (as opposed to forward) is desirable because rays are then independent of all other ray tracing processes, and are trivially parallel.…”

Section: Reverse Monte Carlo Ray Tracingmentioning

confidence: 99%

“…Numerical diffusion is non-existent for RMCRT. When computation resources are abundant and the solid-angle [11] can be well resolved, RMCRT is preferred to discrete-ordinates, where numerical diffusion hurts accuracy. This method has been made to scale up to 256K CPU cores and up to 16K GPUs within the Uintah framework [11,12,19], and is used for the large-scale, GPU-based boiler simulations, as detailed in [19].…”

Section: Reverse Monte Carlo Ray Tracingmentioning

confidence: 99%

See 2 more Smart Citations

Scalable Data Management of the Uintah Simulation Framework for Next-Generation Engineering Problems with Radiation

Kumar

Humphrey

Usher

et al. 2018

Supercomputing Frontiers

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The need to scale next-generation industrial engineering problems to the largest computational platforms presents unique challenges. This paper focuses on data management related problems faced by the Uintah simulation framework at a production scale of 260K processes. Uintah provides a highly scalable asynchronous many-task runtime system, which in this work is used for the modeling of a 1000 megawatt electric (MWe) ultra-supercritical (USC) coal boiler. At 260K processes, we faced both parallel I/O and visualization related challenges, e.g., the default file-per-process I/O approach of Uintah did not scale on Mira. In this paper we present a simple to implement, restructuring based parallel I/O technique. We impose a restructuring step that alters the distribution of data among processes. The goal is to distribute the dataset such that each process holds a larger chunk of data, which is then written to a file independently. This approach finds a middle ground between two of the most common parallel I/O schemes-file per process I/O and shared file I/O-in terms of both the total number of generated files, and the extent of communication involved during the data aggregation phase. To address scalability issues when visualizing the simulation data, we developed a lightweight renderer using OSPRay, which allows scientists to visualize the data interactively at high quality and make production movies. Finally, this work presents a highly efficient and scalable radiation model based on the sweeping method, which significantly outperforms previous approaches in Uintah, like discrete ordinates. The integrated approach allowed the USC boiler problem to run on 260K CPU cores on Mira.

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Improving Performance of the Hypre Iterative Solver for Uintah Combustion Codes on Manycore Architectures Using MPI Endpoints and Kernel Consolidation

Sahasrabudhe

Berzins

2020

Lecture Notes in Computer Science

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A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing

Cited by 12 publications

References 27 publications

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

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

Scalable Data Management of the Uintah Simulation Framework for Next-Generation Engineering Problems with Radiation

Improving Performance of the Hypre Iterative Solver for Uintah Combustion Codes on Manycore Architectures Using MPI Endpoints and Kernel Consolidation

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