Adaptation of MPDATA Heterogeneous Stencil Computation to Intel Xeon Phi Coprocessor

Szustak, Łukasz; Rojek, Krzysztof; Olas, Tomasz; Kuczynski, Lukasz; Halbiniak, Kamil; Gepner, Paweł

doi:10.1155/2015/642705

Cited by 29 publications

(40 citation statements)

References 30 publications

(52 reference statements)

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“…It allows solving advection problems and offers various options to model a wide range of complex geophysical flows. MPDATA corresponds to the group of iterative algorithms, where each time step [15] operates on five input arrays and returns one output array that is necessary for the next step. Every time step encompasses a set of 17 stencil kernels.…”

Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

“…In our previous works [7,14,15], we proposed methods for the adaptation of MPDATA to multi-/manycore systems. These methods contribute to ease memory and communication bounds and to better exploit computation resources of sharedmemory systems, including CPUs and Intel Xeon Phi.…”

Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

“…The main challenge of these works was to minimize data transfers between the main memory and cache hierarchy and better exploit the computing resources. To reach this goal, we proposed the (3+1)D decomposition of MPDATA computation [15] based on a combination of the loop fusion and loop tiling optimization techniques. The proposed approach yields a The proposed decomposition requires partitioning the MPDATA grid into a set of sub-domains of size that enables to keep all the intermediate data in the cache memory.…”

Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

“…The first one is the Multidimensional Positive Definite Advection Transport Algorithm (MPDATA), the main module of the multiscale fluid model EULAG [10,15]. The EULAG model is an innovative solver in the field of numerical modeling of multiscale geophysical flows.…”

Section: Introductionmentioning

confidence: 99%

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Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems

Szustak

2018

J Supercomput

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In this paper, an innovative strategy for the data-flow synchronization in shared-memory systems is proposed. This strategy assumes to synchronize only interdependent threads instead of using the barrier approach that-in contrast to our approach-synchronize all threads. We demonstrate the adaptation of the data-flow synchronization strategy to two complex scientific applications based on stencil codes. An algorithm for the data-flow synchronization is developed and successfully used for both applications. The proposed approach is evaluated for various Intel microarchitectures released in the last 5 years, including the newest processors: Skylake and Knights Landing. The important part of this assessment is the performance comparison of the proposed data-flow synchronization with the OpenMP barrier. The experimental results show that the performance of the studied applications can be accelerated up to 1.3 times using the proposed data-flow synchronizations strategy.

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Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

Section: Adaptation Of Data-flow Synchronization Strategy To Mpdatamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems

Szustak

2018

J Supercomput

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“…Those readers that are more interested in the specification of MPDATA and its implementation can refer to [29,34,36].…”

Section: Overviewmentioning

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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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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Adaptation of MPDATA Heterogeneous Stencil Computation to Intel Xeon Phi Coprocessor

Cited by 29 publications

References 30 publications

Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems

Strategy for data-flow synchronizations in stencil parallel computations on multi-/manycore systems

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

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

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