315 research outputs found
Parallel 3-D marine controlled-source electromagnetic modelling using high-order tetrahedral Nédélec elements
We present a parallel and high-order Nédélec finite element solution for the marine controlled-source electromagnetic (CSEM) forward problem in 3-D media with isotropic conductivity. Our parallel Python code is implemented on unstructured tetrahedral meshes, which support multiple-scale structures and bathymetry for general marine 3-D CSEM modelling applications. Based on a primary/secondary field approach, we solve the diffusive form of Maxwell’s equations in the low-frequency domain. We investigate the accuracy and performance advantages of our new high-order algorithm against a low-order implementation proposed in our previous work. The numerical precision of our high-order method has been successfully verified by comparisons against previously published results that are relevant in terms of scale and geological properties. A convergence study confirms that high-order polynomials offer a better trade-off between accuracy and computation time. However, the optimum choice of the polynomial order depends on both the input model and the required accuracy as revealed by our tests. Also, we extend our adaptive-meshing strategy to high-order tetrahedral elements. Using adapted meshes to both physical parameters and high-order schemes, we are able to achieve a significant reduction in computational cost without sacrificing accuracy in the modelling. Furthermore, we demonstrate the excellent performance and quasi-linear scaling of our implementation in a state-of-the-art high-performance computing architecture.This project has received funding from the European Union's Horizon 2020 programme under the Marie Sklodowska-Curie grant agreement No. 777778. Furthermore, the research leading to these results has received funding from the European Union's Horizon 2020 programme under the ChEESE Project (https://cheese-coe.eu/ ), grant agreement No. 823844. In addition, the authors would also like to thank the support of the Ministerio de Educación y Ciencia (Spain) under Projects TEC2016-80386-P and TIN2016-80957-P.
The authors would like to thank the Editors-in-Chief and to both reviewers, Dr. Martin Cuma and Dr. Raphael Rochlitz, for their valuable comments and suggestions which helped
to improve the quality of the manuscript.
This work benefited from the valuable suggestions, comments, and proofreading of Dr. Otilio Rojas (BSC). Last but not least, Octavio Castillo-Reyes thanks Natalia Gutierrez (BSC) for her support in CSEM modeling with BSIT.Peer ReviewedPostprint (author's final draft
PETGEM: A parallel code for 3D CSEM forward modeling using edge finite elements
We present the capabilities and results of the Parallel Edge-based Tool for
Geophysical Electromagnetic modeling (PETGEM), as well as the physical and
numerical foundations upon which it has been developed. PETGEM is an
open-source and distributed parallel Python code for fast and highly accurate
modeling of 3D marine controlled-source electromagnetic (3D CSEM) problems. We
employ the N\'ed\'elec Edge Finite Element Method (EFEM) which offers a good
trade-off between accuracy and number of degrees of freedom, while naturally
supporting unstructured tetrahedral meshes. We have particularised this new
modeling tool to the 3D CSEM problem for infinitesimal point dipoles asumming
arbitrarily isotropic media for low-frequencies approximations. In order to
avoid source-singularities, PETGEM solves the frequency-domain Maxwell's
equations of the secondary electric field, and the primary electric field is
calculated analytically for homogeneous background media. We assess the PETGEM
accuracy using classical tests with known analytical solutions as well as
recent published data of real life geological scenarios. This assessment proves
that this new modeling tool reproduces expected accurate solutions in the
former tests, and its flexibility on realistic 3D electromagnetic problems.
Furthermore, an automatic mesh adaptation strategy for a given frequency and
specific source position is presented. We also include a scalability study
based on fundamental metrics for high-performance computing (HPC)
architectures.Comment: \c{opyright} 2018. This manuscript version is made available under
the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
This project has received funding from the EC-H2020 under the Marie
Sklodowska-Curie grant agreement No. 644202, and from the EC-H2020 under the
HPC4E Project, grant agreement No. 68977
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Design and Experimentation of a Large Scale Distributed Stochastic Control Algorithm Applied to Energy Management Problems
The Stochastic Dynamic Programming method often used to solve some stochastic optimization problems is only usable in low dimension, being plagued by the curse of dimensionality. In this article, we explain how to postpone this limit by using High Performance Computing: parallel and distributed algorithms design, optimized implementations and usage of large scale distributed architectures (PC clusters and Blue Gene/P)
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
Mesoscopic simulations of hydrocarbon flow in source shales are challenging,
in part due to the heterogeneous shale pores with sizes ranging from a few
nanometers to a few micrometers. Additionally, the sub-continuum fluid-fluid
and fluid-solid interactions in nano- to micro-scale shale pores, which are
physically and chemically sophisticated, must be captured. To address those
challenges, we present a GPU-accelerated package for simulation of flow in
nano- to micro-pore networks with a many-body dissipative particle dynamics
(mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the
code offloads all intensive workloads on GPUs. Other advancements, such as
smart particle packing and no-slip boundary condition in complex pore
geometries, are also implemented for the construction and the simulation of the
realistic shale pores from 3D nanometer-resolution stack images. Our code is
validated for accuracy and compared against the CPU counterpart for speedup. In
our benchmark tests, the code delivers nearly perfect strong scaling and weak
scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge
National Laboratory's (ORNL) Titan supercomputer. Moreover, a single-GPU
benchmark on ORNL's SummitDev and IBM's AC922 suggests that the host-to-device
NVLink can boost performance over PCIe by a remarkable 40\%. Lastly, we
demonstrate, through a flow simulation in realistic shale pores, that the CPU
counterpart requires 840 Power9 cores to rival the performance delivered by our
package with four V100 GPUs on ORNL's Summit architecture. This simulation
package enables quick-turnaround and high-throughput mesoscopic numerical
simulations for investigating complex flow phenomena in nano- to micro-porous
rocks with realistic pore geometries
- …