Reducing memory requirements for large size LBM simulations on GPUs

Valero-Lara, Pedro

doi:10.1002/cpe.4221

Cited by 21 publications

(8 citation statements)

References 31 publications

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“…We apply a more efficient approach which uses only one data instance in memory and is known as the A-A memory layout pattern [6,47], cf. Figure 3.…”

Section: Memory Layout Patternmentioning

confidence: 99%

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

et al. 2017

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Heterogeneous clusters are a widely utilized class of supercomputers assembled from different types of computing devices, for instance CPUs and GPUs, providing a huge computational potential. Programming them in a scalable way exploiting the maximal performance introduces numerous challenges such as optimizations for different computing devices, dealing with multiple levels of parallelism, the application of different programming models, work distribution, and hiding of communication with computation. We utilize the lattice Boltzmann method for fluid flow as a representative of a scientific computing application and develop a holistic implementation for large-scale CPU/GPU heterogeneous clusters. We review and combine a set of best practices and techniques ranging from optimizations for the particular computing devices to the orchestration of tens of thousands of CPU cores and thousands of GPUs. Eventually, we come up with an implementation using all the available computational resources for the lattice Boltzmann method operators. Our approach shows excellent scalability behavior making it future-proof for heterogeneous clusters of the upcoming architectures on the exaFLOPS scale. Parallel efficiencies of more than 90% are achieved leading to 2604.72 GLUPS utilizing 24,576 CPU cores and 2048 GPUs of the CPU/GPU heterogeneous cluster Piz Daint and computing more than 6.8 × 10 9 lattice cells.

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“…We apply a more efficient approach which uses only one data instance in memory and is known as the A-A memory layout pattern [6,47], cf. Figure 3.…”

Section: Memory Layout Patternmentioning

confidence: 99%

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

et al. 2017

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“…The memory problem is addressed by Valero in the paper Reducing Memory Requirements for Large Size LBM Simulations on GPUs , where he proposes some initiatives to minimize the memory requirements of the Lattice‐ Boltzmann Method for its usage on GPGPUs to run large scale simulations. The proposed approach allows the author to execute bigger simulations on the same platform without additional memory transfers, those achieving a high performance.…”

Section: Topics Of This Special Issuementioning

confidence: 99%

Algorithms and applications towards the convergence of high‐end data‐intensive and computing systems

Carretero

Blas

Nakano³

et al. 2017

Concurrency and Computation

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“…We emphasize that the methodology in this article may find application wherever GPUs are useful for accelerating established computational procedures. For example, in Fluid Mechanics, there has been a broad application of GPUs where solutions of Poisson's equation are commonly required [7,8], tsunami modelling and simulation [9], numerical linear algebra [10,11], batch solving of 1D partial differential equations [4], ADI methods for 2D simulations [5,6] and modelling mesoscopic-scale flows using Lattice-Boltzmann methods [12]. Also, in the field of gravitational wave data analysis, much work is done simplifying complex waveforms and analyses to achieve results in a physically reasonable and desirable amount of time.…”

Section: Introductionmentioning

confidence: 99%

A Batched GPU Methodology for Numerical Solutions of Partial Differential Equations

Carroll¹,

Gloster²,

Bustamante³

et al. 2021

Preprint

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In this paper we present a methodology for data accesses when solving batches of Tridiagonal and Pentadiagonal matrices that all share the same left-hand-side (LHS) matrix. The intended application is to the numerical solution of Partial Differential Equations via the finite-difference method, although the methodology is applicable more broadly. By only storing one copy of this matrix, a significant reduction in storage overheads is obtained, together with a corresponding decrease in compute time. Taken together, these two performance enhancements lead to an overall more efficient implementation over the current state of the art algorithms cuThomasBatch and cuPentBatch, allowing for a greater number of systems to be solved on a single GPU. We demonstrate the methodology in the case of the Diffusion Equation, Hyperdiffusion Equation, and the Cahn-Hilliard Equation, all in one spatial dimension. In this last example, we demonstrate how the method can be used to perform 2 20 independent simulations of phase separation in one dimension. In this way, we build up a robust statistical description of the coarsening phenomenon which is the defining behavior of phase separation. We anticipate that the method will be of further use in other similar contexts requiring statistical simulation of physical systems.

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Reducing memory requirements for large size LBM simulations on GPUs

Cited by 21 publications

References 31 publications

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

A Holistic Scalable Implementation Approach of the Lattice Boltzmann Method for CPU/GPU Heterogeneous Clusters

Algorithms and applications towards the convergence of high‐end data‐intensive and computing systems

A Batched GPU Methodology for Numerical Solutions of Partial Differential Equations

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