Optimizing Fully Anisotropic Elastic Propagation on Intel Xeon Phi Coprocessors

Caballero, Diego; Farrés, Albert; Durán, Alejandro; Hanzich, Mauricio; Fernández, Santiago Delgado; Martorell, Xavier

doi:10.3997/2214-4609.201414035

Cited by 11 publications

(12 citation statements)

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“…The optimizations were enabled one after the other; thus the figures show the accumulative improvement of all previous optimizations. For additional reference, the performance of the same algorithm but optimized for the first generation Intel Xeon Phi product (code-named Knights Corner) is presented on [1]. Cooperative threading and streaming stores optimizations did not improve performance in the best settings combination found by the GA, thus we do not show them in Figure 2.…”

Section: Optimization Strategiesmentioning

confidence: 99%

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Optimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors

et al. 2017

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This work shows several optimization strategies evaluated and applied to an elastic wave propagation engine, based on a Fully Staggered Grid, running on the latest Intel Xeon Phi processors, the second generation of the product (code-named Knights Landing). Our fully optimized code shows a speed-up of about 4x when compared with the same algorithm optimized for the previous generation processor.Authors also thank Repsol for the permission to publish the present research, carried out at the Repsol-BSC Research Center. This work has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from the Brazilian Ministry of Science, Technology and Innovation through Rede\ud Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant\ud agreement n.◦ 689772.\ud \ud * Other brands and names are the property of their respective owners.Peer ReviewedPostprint (author's final draft

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Section: Optimization Strategiesmentioning

confidence: 99%

“…1 and 2 for supporting fully anisotropic scenarios, we have used a Finite Differences (FD) method over a Fully Staggered Grid [2] as shown in a previous work by this group [1]. Our base implementation is the direct result of developing an FD method over an FSG grid.…”

Section: Optimization Strategiesmentioning

confidence: 99%

Optimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors

et al. 2017

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“…Thus, Krukeja et al [3] automatically generate a highly optimized stencil code for multiple target architectures, while Niu et al [4] suggest using run-time reconfiguration, and a performance model, to reduce resource consumption. Caballero et al [5] studied the effect of different optimizations on elastic wave propagation equations, achieving more than an order of magnitude of improvement compared with the basic OpenMP parallel version.…”

Section: Related Workmentioning

confidence: 99%

Strategies to Improve the Performance of a Geophysics Model for Different Manycore Systems

Serpa

Cruz

Diener

et al. 2017

2017 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW)

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Abstract-Many software mechanisms for geophysics exploration in Oil & Gas industries are based on wave propagation simulation. To perform such simulations, state-of-art HPC architectures are employed, generating results faster and with more accuracy at each generation. The software must evolve to support the new features of each design to keep performance scaling. Furthermore, it is important to understand the impact of each change applied to the software, in order to improve the performance as most as possible. In this paper, we propose several optimization strategies for a wave propagation model for five architectures: Intel Haswell, Intel Knights Corner, Intel Knights Landing, NVIDIA Kepler and NVIDIA Maxwell. We focus on improving the cache memory usage, vectorization, and locality in the memory hierarchy. We analyze the hardware impact of the optimizations, providing insights of how each strategy can improve the performance. The results show that NVIDIA Maxwell improves over Intel Haswell, Intel Knights Corner, Intel Knights Landing and NVIDIA Kepler performance by up to 17.9x.

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“…The computational kernel is responsible for simulating seismic wavefields with the purpose of measuring data misfits, generating gradients and obtaining approximations to the Hessian. Some acceleration efforts focusing on the FWI computing kernels include porting these kernels to accelerator-based hardware [5,11,39], optimizing the kernels' performance in general purpose processors [23] for off-the-shelf hardware or parallelizing the kernels in many compute units [30,36,38]. Nevertheless, we wish to show here that there are workflow strategies that are orthogonal to kernel optimization and which result in notable computational savings, thus making elastic FWI a routinely applicable tool for 3D datasets.…”

Section: Introductionmentioning

confidence: 99%

“…In order to mitigate the computational burden, previous work has mostly focused on improving the efficiency of the computational kernel [11,13,21], which takes most of the FWI execution time. The computational kernel is responsible for simulating seismic wavefields with the purpose of measuring data misfits, generating gradients and obtaining approximations to the Hessian.…”

Section: Introductionmentioning

confidence: 99%

Acceleration strategies for elastic full waveform inversion workflows in 2D and 3D

et al. 2016

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Full waveform inversion (FWI) is one of the most challenging procedures to obtain quantitative information of the subsurface. For elastic inversions, when both compressional and shear velocities have to be inverted, the algorithmic issue becomes also a computational challenge due to the high cost related to modelling elastic rather than acoustic waves. This shortcoming has been moderately mitigated by using high-performance computing to accelerate 3D elastic FWI kernels. Nevertheless, there is room in the FWI workflows for obtaining large speedups at the cost of proper grid pre-processing and data decimation techniques. In the present work, we show how by making full use of frequency-adapted grids, composite shot lists and a novel dynamic offset control strategy, we can reduce by several orders of magnitude the compute time while improving the convergence of the method in the studied cases, regardless of the forward and adjoint compute kernels used.

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Optimizing Fully Anisotropic Elastic Propagation on Intel Xeon Phi Coprocessors

Cited by 11 publications

References 0 publications

Optimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors

Optimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors

Strategies to Improve the Performance of a Geophysics Model for Different Manycore Systems

Acceleration strategies for elastic full waveform inversion workflows in 2D and 3D

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