Parallelization of 2D MPDATA EULAG algorithm on hybrid architectures with GPU accelerators

Wyrzykowski, Roman; Szustak, Łukasz; Rojek, Krzysztof

doi:10.1016/j.parco.2014.04.009

Cited by 23 publications

(38 citation statements)

References 29 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: 93%

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: 93%

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

2017

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“…The achieved performance results showed the possibility of achieving high performance both on CPU and GPU platforms. Recently, we have developed [14] a hybrid CPU-GPU version of 2D MPDATA, to fully utilize all the available computing resources by spreading computations across the entire machine. To reveal performance constraints for the MPDATA algorithm running on hybrid architectures, we follow the simple methodology presented in [8], where the attainable performance is estimated based on the flop-per-byte ratio.…”

Section: Related Workmentioning

confidence: 99%

“…Such kernels have been investigated by many authors over the years [8,9,14,[20][21][22][23][24]. The main direction of memory optimizations for stencil computations has principally focused on different decomposition strategies, like space and temporal blocking techniques [20], that attempt to exploit locality by performing operations on data blocks of a suitable size before moving on to the next block.…”

Section: Related Workmentioning

confidence: 99%

“…The main direction of memory optimizations for stencil computations has principally focused on different decomposition strategies, like space and temporal blocking techniques [20], that attempt to exploit locality by performing operations on data blocks of a suitable size before moving on to the next block. These strategies have been used to improve the efficiency of implementing stencil codes in the context of variety of multi-/manycore architectures (see, e.g., [14,22,25]). The main assumption for using the temporal blocking method is that no other computations need to be performed between consecutive stencils (or stages).…”

Section: Related Workmentioning

confidence: 99%

“…The main assumption for using the temporal blocking method is that no other computations need to be performed between consecutive stencils (or stages). This assumption has been aggressively used by us in [14] to improve the efficiency of implementing 2D stencil codes on hybrid CPU-GPU platforms by removing or delaying synchronization between stages.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Adaptation of MPDATA Heterogeneous Stencil Computation to Intel Xeon Phi Coprocessor

Szustak

Rojek

Olas

et al. 2015

Scientific Programming

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The multidimensional positive definite advection transport algorithm (MPDATA) belongs to the group of nonoscillatory forwardin-time algorithms and performs a sequence of stencil computations. MPDATA is one of the major parts of the dynamic core of the EULAG geophysical model. In this work, we outline an approach to adaptation of the 3D MPDATA algorithm to the Intel MIC architecture. In order to utilize available computing resources, we propose the (3 + 1)D decomposition of MPDATA heterogeneous stencil computations. This approach is based on combination of the loop tiling and fusion techniques. It allows us to ease memory/communication bounds and better exploit the theoretical floating point efficiency of target computing platforms. An important method of improving the efficiency of the (3 + 1)D decomposition is partitioning of available cores/threads into work teams. It permits for reducing inter-cache communication overheads. This method also increases opportunities for the efficient distribution of MPDATA computation onto available resources of the Intel MIC architecture, as well as Intel CPUs. We discuss preliminary performance results obtained on two hybrid platforms, containing two CPUs and Intel Xeon Phi. The top-of-the-line Intel Xeon Phi 7120P gives the best performance results, and executes MPDATA almost 2 times faster than two Intel Xeon E5-2697v2 CPUs.

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Adaptation of fluid model EULAG to graphics processing unit architecture

Rojek

Ciznicki

Rosa

et al. 2014

Concurrency and Computation

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SUMMARYThe goal of this study is to adapt the multiscale fluid solver EULerian or LAGrangian framewrok (EULAG) to future graphics processing units (GPU) platforms. The EULAG model has the proven record of successful applications, and excellent efficiency and scalability on conventional supercomputer architectures. Currently, the model is being implemented as the new dynamical core of the COSMO weather prediction framework. Within this study, two main modules of EULAG, namely the multidimensional positive definite advection transport algorithm (MPDATA) and the variational generalized conjugate residual, elliptic pressure solver Generalized Conjugate Residual (GCR) are analyzed and optimized. In this paper, a method is proposed, which ensures a comprehensive analysis of the resource consumption including registers, shared, and global memories. This method allows us to identify bottlenecks of the algorithm, including data transfers between host and global memory, global and shared memories, as well as GPU occupancy. We put the emphasis on providing a fixed memory access pattern, padding as well as organizing computation in the MPDATA algorithm. The testing and validation of the new GPU implementation have been carried out based on modeling decaying turbulence of a homogeneous incompressible fluid in a triply-periodic cube. Simulations performed using the standard version of EULAG and its new GPU implementation give similar solutions. Preliminary results show a promising increase in terms of computational efficiency.

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Parallelization of 2D MPDATA EULAG algorithm on hybrid architectures with GPU accelerators

Cited by 23 publications

References 29 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

Adaptation of MPDATA Heterogeneous Stencil Computation to Intel Xeon Phi Coprocessor

Adaptation of fluid model EULAG to graphics processing unit architecture

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