Albert Farrés

Finite-difference staggered grids in GPUs for anisotropic elastic wave propagation simulation

The 3D elastic wave equations can be used to simulate the physics of waves traveling through the Earth more precisely than acoustic approximations. However, this improvement in quality has a counterpart in the cost of the numerical scheme. A possible strategy to mitigate that expense is using specialized, high-performing architectures such as GPUs. Nevertheless, porting and optimizing a code for such a platform require a deep understanding of both the underlying hardware architecture and the algorithm at hand. Furthermore, for very large problems, multiple GPUs must work concurrently, which adds yet another layer of complexity to the codes. In this work, we have tackled the problem of porting and optimizing a 3D elastic wave propagation engine which supports both standard- and fully-staggered grids to multi-GPU clusters. At the single GPU level, we have proposed and evaluated many optimization strategies and adopted the best performing ones for our final code. At the distributed memory level, a domain decomposition approach has been used which allows for good scalability thanks to using asynchronous communications and I/O. HighlightsWe use staggered grids to simulate an elastic wave propagation across the earth.As the performance is critical, we make use of GPUs to accelerate the simulation.We show how the work is distributed among the nodes in a domain decomposition case.Regarding the complexity of the simulation, we obtain speed-ups from 10i? to 14i?.

Modulated phase matching and high-order harmonic enhancement mediated by the carrier-envelope phase

The process of high-order harmonic generation in gases is numerically investigated in the presence of a few-cycle pulsed-Bessel-beam pump, featuring a periodic modulation in the peak intensity due to large carrier-envelope-phase mismatch. A two-decade enhancement in the conversion efficiency is observed and interpreted as the consequence of a mechanism known as a nonlinearly induced modulation in the phase mismatch.

Optimizing Isotropic and Fully-anisotropic Elastic Modelling on Multi-GPU Platforms

The acoustic isotropic assumption for wave propagation in the subsoil has limitations, which threat to hamper the results of modern day imaging tools, such as RTM or FWI. Elasticity and anisotropy bring us closer to the real physics of the propagating waves, although at a severe computational cost. Hence, we aim at solving 3D anisotropic elastic wave propagation problems in modern HPC platforms. We require our solutions to be sufficiently accurate, efficient and highly scalable for large-scale scenarios. This condition set leads us to use multi-GPU platforms and a variety of time-domain staggered-grid finite-difference (FD) schemes.

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

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.

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

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.

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

Summary 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.

3D Viscoelastic Anisotropic Seismic Modeling with High-Order Mimetic Finite Differences

We present a scheme to solve three-dimensional viscoelastic anisotropic wave propagation on structured staggered grids. The scheme uses a fully-staggered grid (FSG) or Lebedev grid (Lebedev, J Sov Comput Math Math Phys 4:449–465, 1964; Rubio et al. Comput Geosci 70:181–189, 2014), which allows for arbitrary anisotropy as well as grid deformation. This is useful when attempting to incorporate a bathymetry or topography in the model. The correct representation of surface waves is achieved by means of using high-order mimetic operators (Castillo and Grone, SIAM J Matrix Anal Appl 25:128–142, 2003; Castillo and Miranda, Mimetic discretization methods. CRC Press, Boca Raton, 2013), which allow for an accurate, compact and spatially high-order solution at the physical boundary condition. Furthermore, viscoelastic attenuation is represented with a generalized Maxwell body approximation, which requires of auxiliary variables to model the convolutional behavior of the stresses in lossy media. We present the scheme’s accuracy with a series of tests against analytical and numerical solutions. Similarly we show the scheme’s performance in high-performance computing platforms. Due to its accuracy and simple pre- and post-processing, the scheme is attractive for carrying out thousands of simulations in quick succession, as is necessary in many geophysical forward and inverse problems both for the industry and academia.