Elliptic Solver Performance Evaluation on Modern Hardware Architectures

Ciznicki, Milosz; Kopta, Piotr; Kulczewski, Michal; Kurowski, Krzysztof; Gepner, Paweł

doi:10.1007/978-3-642-55224-3_16

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…The CPU-based implementations of solving partial differential equations in 3D space for Numerical Weather Prediction (NWP) problems were presented in [18]. As the GCR elliptic solver is one of the most significant part of the EULAG model, several techniques for porting this solver to a cluster with multicore CPUs have been studied in [5], based on combining the MPI and OpenMP standards.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Several different techniques for porting the GCR elliptic solver to hybrid architectures have been presented in previous study [18]. The proposed techniques rely on porting MPI-all code (which uses MPI exclusively) to the hybrid version MPI+OpenMP.…”

Section: A Main Objectivesmentioning

confidence: 99%

Porting Multiscale Fluid Model EULAG to Modern Heterogeneous Architectures

Rosa¹,

Ciznicki²,

Rojek³

et al. 2014

IJAPM

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“…Heterogeneous multi-core and many-core computer systems can provide significant performance opportunities to a wide range of algorithms and legacy applications [22,18,2,10,11,12]. However, in order to improve the efficiency of calculations a coordinated use of CPUs and GPUs must be implemented.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Selected Resource Allocation and Scheduling Methods in Heterogeneous Many-Core Processors and Graphics Processing Units

Ciznicki

Kurowski

Węglarz

2014

Foundations of Computing and Decision Sciences

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Heterogeneous many-core computing resources are increasingly popular among users due to their improved performance over homogeneous systems. Many developers have realized that heterogeneous systems, e.g. a combination of a shared memory multi-core CPU machine with massively parallel Graphics Processing Units (GPUs), can provide significant performance opportunities to a wide range of applications. However, the best overall performance can only be achieved if application tasks are efficiently assigned to different types of processor units in time taking into account their specific resource requirements. Additionally, one should note that available heterogeneous resources have been designed as general purpose units, however, with many built-in features accelerating specific application operations. In other words, the same algorithm or application functionality can be implemented as a different task for CPU or GPU. Nevertheless, from the perspective of various evaluation criteria, e.g. the total execution time or energy consumption, we may observe completely different results. Therefore, as tasks can be scheduled and managed in many alternative ways on both many-core CPUs or GPUs and consequently have a huge impact on the overall computing resources performance, there are needs for new and improved resource management techniques. In this paper we discuss results achieved during experimental performance studies of selected task scheduling methods in heterogeneous computing systems. Additionally, we present a new architecture for resource allocation and task scheduling library which provides a generic application programming interface at the operating system level for improving scheduling polices taking into account a diversity of tasks and heterogeneous computing resources characteristics.

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Elliptic Solver Performance Evaluation on Modern Hardware Architectures

Cited by 13 publications

References 13 publications

Performance modeling of 3D MPDATA simulations on GPU cluster

Performance modeling of 3D MPDATA simulations on GPU cluster

Porting Multiscale Fluid Model EULAG to Modern Heterogeneous Architectures

Evaluation of Selected Resource Allocation and Scheduling Methods in Heterogeneous Many-Core Processors and Graphics Processing Units

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