Adaptation of fluid model EULAG to graphics processing unit architecture

Rojek, Krzysztof; Ciznicki, Milosz; Rosa, Bogdan; Kopta, Piotr; Kulczewski, Michal; Kurowski, Krzysztof; Piotrowski, Zbigniew; Szustak, Łukasz; Wójcik, Damian K.; Wyrzykowski, Roman

doi:10.1002/cpe.3417

Cited by 25 publications

(31 citation statements)

References 31 publications

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“…In previous works [26,35], we proved that MPDATA belongs to the group of memory-bound algorithms [13,36]. This makes MPDATA a challenging candidate for a technology such as dynamic VFS [16], which can reduce CPU energy consumption without a significant loss of performance.…”

Section: Overviewmentioning

confidence: 95%

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: Overviewmentioning

confidence: 95%

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

2017

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“…The next step of our research was to parallelize the 3D version of MPDATA. It required to develop a different approach [14] than for the 2D version. In paper [13], we presented an analysis of resources usage in GPU, and its influence on the resulting performance.…”

Section: Introductionmentioning

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%

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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“…for computational fluid dynamics, geometric modelling, solving partial differential equations or image and video processing [1][2][3][4][5]. As computing time and memory usage grow linearly with the number of array elements in stencil computations our research targets highly parallel implementations of stencil codes together with task scheduling and optimization techniques taking into consideration energy cost and data locality [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The scheduling problem is introduced in Sect. 4. Performance and energy models are introduced in Sect.…”

Section: Introductionmentioning

confidence: 99%

Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures

2016

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Performance of high-end supercomputers will reach the exascale through the advent of core counts in billions. However, in the upcoming exascale computing era it is important not only to focus on the performance, but also on scalability of fine-grained parallel applications, data locality and energy aware scheduling within the parallel code. In fact, parallel applications need to change even now by redesigning algorithms and data structures respectively to take advantage of the recent improvements in energy efficiency of heterogeneous computing hardware, including multicore processors and GPU accelerators. Over the next few years one of the biggest challenges for exascale will be the ability of parallel applications to fully exploit locality which will, in turn, be required to achieve expected performance and energy efficiency. Future highly parallel applications will have to deal with deep memory hierarchies taking into account energy cost in moving data off-chip. Therefore, they will have to apply new coordinated scheduling approaches to balance energy aware resource utilization and minimize work starvation during runtime. As new constraints and limits on memory bandwidth and energy will play a key role in high performance computing (HPC) in the future, more sophisticated and dynamic scheduling techniques will be needed and applied within the parallel code. In this paper we focus on an energy-aware distribution of the stencil workload on heterogeneous processors. Our analysis of energy and performance models focused on relevant class of stencil computations to explore the relationship between task scheduling algorithms and energy constraints. More precisely, we search for a schedule which minimizes the energy usage within a specified computation's deadline of the stencil workload on heterogeneous architectures. Since the problem is computationally intractable, we present an integer linear programming formulation for finding optimal schedules. As finding optimal schedules is time consuming we have developed four heuristics and tested them experimentally with respect to optimal solutions. In our work we focus on a single node configurations with heterogeneous processors. These configurations represent the state of the art multi-and many-core architectures.

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Adaptation of fluid model EULAG to graphics processing unit architecture

Cited by 25 publications

References 31 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

Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures

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