Parallelization of 3D MPDATA Algorithm Using Many Graphics Processors

Rojek, Krzysztof; Wyrzykowski, Roman

doi:10.1007/978-3-319-21909-7_43

Cited by 7 publications

(12 citation statements)

References 19 publications

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“…The parallelization of the MPDATA code is designed for a single node containing multicore processors [27,28], employing OpenMP [14] within them [25]. Therefore, in this paper, we focus on investigating the power evaluation on a single node.…”

Section: Parallelization Of Mpdatamentioning

confidence: 99%

Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures

2017

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We propose an approach to estimate the power consumption of algorithms, as a function of the frequency and number of cores, using only a very reduced set of real power measures. In addition, we also provide the formulation of a method to select the voltage-frequency scaling-concurrency throttling configurations that should be tested in order to obtain accurate estimations of the power dissipation. The power models and selection methodology are verified using two real scientific application: the stencil-based 3D MPDATA algorithm and the conjugate gradient (CG) method for sparse linear systems. MPDATA is a crucial component of the EULAG model, which is widely used in weather forecast simulations. The CG algorithm is the keystone for iterative solution of sparse symmetric positive definite linear systems via Krylov subspace methods. The reliability of the method is confirmed for a variety of ARM The researchers from Czestochowa University of Technology were supported by the National Science Centre, Poland, under Grant No. UMO-2015/17/D/ST6/04059. The researcher from Universidad Jaime I (UJI) was supported by the CICYT Project TIN2014-53495-R of MINECO and FEDER. This work was partially performed during a short-term scientific mission (STSM)

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Section: Parallelization Of Mpdatamentioning

confidence: 99%

Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures

2017

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“…The MPDATA algorithm is the main module of the multiscale fluid solver EULAG [12][13][14][15][16]. MPDATA solves the continuity equation describing the advection of a nondiffusive quantity Ψ in a flow field, namely for(k=1; k <= n3m; ++k) for(j=1; j <= mp; ++j) for(i=1; i <= ilft; ++i) f1(i,j,k)=donor(c1*x(i-1,j,k)+c2,c1*x(i,j,k)+c2,v1(i,j,k)); for(k=1; k <= n3m; ++k) for(j=jbot; j <= mp; ++j)…”

Section: Overview Of 3d Mpdatamentioning

confidence: 99%

“…In consequence, the best configuration of the MPDATA algorithm is compression of 17 stencils into four GPU kernels. Figure 2 presents a single kernel which is obtained [15] after compression of the four MPDATA stencils shown in Fig. 1.…”

Section: Overview Of 3d Mpdatamentioning

confidence: 99%

“…Currently, the model is being implemented as the new dynamical core of the COSMO weather prediction framework. The multidimensional positive definite advection transport algorithm (MPDATA) is among the most time-consuming calculations of the EULAG solver [5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…The aim of paper [15] was to adapt 3D MPDATA computations to clusters with GPUs. Our approach was based on a hierarchical decomposition including the level of cluster, as well as an optimized distribution of computations between GPU resources within each node.…”

Section: Introductionmentioning

confidence: 99%

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Performance modeling of 3D MPDATA simulations on GPU cluster

Rojek

Wyrzykowski

2016

J Supercomput

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The goal of this study is to parallelize the multidimensional positive definite advection transport algorithm (MPDATA) across a computational cluster equipped with GPUs. Our approach permits us to provide an extensive overlapping GPU computations and data transfers, both between computational nodes, as well as between the GPU accelerator and CPU host within a node. For this aim, we decompose a computational domain into two unequal parts which correspond to either data dependent or data independent parts. Then, data transfers can be performed simultaneously with computations corresponding to the second part. Our approach allows for achieving 16.372 Tflop/s using 136 GPUs. To estimate the scalability of the proposed approach, a performance model dedicated to MPDATA simulations is developed. We focus on the analysis of computation and communication execution times, as well as the influence of overlapping data transfers and GPU computations, with regard to the number of nodes.

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Systematic adaptation of stencil‐based 3D MPDATA to GPU architectures

Rojek

Wyrzykowski

Kuczynski

2016

Concurrency and Computation

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In this work, we focus on a systematic adaptation of the stencil-based multidimensional positive definite advection transport algorithm (MPDATA) to different graphics processing unit (GPU)-based computing platforms. Another objective of this work is to compare the performance of MPDATA on several platforms, including a multi-GPU system with two NVIDIA Tesla K80 cards, and single-card platforms with Tesla K20X, GeForce GTX TITAN, and GeForce GTX 980. The usage of the following optimization methods is proposed to improve the overall performance: (i) reducing the number of operations by the subexpression elimination when implementing 2.5D blocking; (ii) reorganization of boundary conditions for reducing branch instructions; (iii) advanced memory management to increase the coalesced memory access; and (iv) warps rearrangement for optimizing the data access to GPU global memory. The presented methods of the MPDATA adaptation to GPU architectures allow us to efficiently use many graphics processors within a single node by applying peer-to-peer data transfers between GPU global memories. We propose an auto-tuning procedure to compensate architectural differences between the considered platforms. This procedure takes into account algorithm/GPU-specific parameters. The proposed approach to adaptation of MPDATA to GPU architectures allows us to achieve up to 482.5 Gflop/s for the platform equipped with two NVIDIA K80 GPUs. for simulating thermo-fluid flows across a wide range of scales and physical scenarios, such as numerical weather and climate prediction, simulation of urban flows, areas of turbulence, ocean currents, and others. Recently, the dynamical core of EULAG has been implemented into consortium for small-scale modeling weather prediction framework and is expected to be in operational use [5]. The dynamical core of EULAG is based on the non-hydrostatic Euler equations, either fully compressible or anelastic. The model employs the generalized curvilinear coordinate description, finite-volume non-oscillatory transport MPDATA, and advanced elliptic solver generalized conjugate residual (GCR) [6].To be able to run the existing codes efficiently on new hybrid platforms with accelerators, it is necessary to redesign structures of these codes [7]. In our previous work [8], we proposed two decompositions of 2D MPDATA computations, which provide adaptation to CPU and GPU architectures. We developed a hybrid CPU-GPU version of 2D MPDATA in order to fully utilize all the available computing resources. The next step in our research was to parallelize the 3D version of MPDATA. It required to develop a different approach than for the 2D version. In papers [7,9], we presented an analysis of resources usage in GPU, and its influence on the resulting performance. We detected the bottlenecks and developed a method for the efficient distribution of computation across GPU kernels.Following our previous papers, in this work, we propose a set of methods for adaptating the 3D MPDATA to different GPU accelerators. We investigate differ...

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Parallelization of 3D MPDATA Algorithm Using Many Graphics Processors

Cited by 7 publications

References 19 publications

Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures

Modeling power consumption of 3D MPDATA and the CG method on ARM and Intel multicore architectures

Performance modeling of 3D MPDATA simulations on GPU cluster

Systematic adaptation of stencil‐based 3D MPDATA to GPU architectures

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