arXiv subjects

Fande Kong

Publications and source records attributed to Fande Kong.

9 recordsLinked to original sources

arXiv2022

The PETSc Community Is the Infrastructure

The communities who develop and support open source scientific software packages are crucial to the utility and success of such packages. Moreover, these communities form an important part of the human infrastructure that enables scientific progress. This paper discusses aspects of the PETSc (Portable Extensible Toolkit for Scientific Computation) community, its organization, and technical approaches that enable community members to help each other efficiently.

cs.SE↗

arXiv2020

Neutron transport criticality calculations using a parallel monolithic multilevel Schwarz preconditioner together with a nonlinear diffusion acceleration method

The multigroup neutron transport criticality calculations using modern supercomputers have been widely employed in a nuclear reactor analysis for studying whether or not a system is self-sustaining. However, the design and development of efficient parallel algorithms for the transport criticality calculations is challenging especially when the number of processor cores is large and an unstructured mesh is adopted. In particular, both the compute time and memory usage have to be carefully taken into consideration due to the dimensionality of the neutron transport equations. In this paper, we study a monolithic multilevel Schwarz preconditioner for the transport criticality calculations based on a nonlinear diffusion acceleration (NDA) method. We propose a monolithic multilevel Schwarz method that is capable of efficiently handling the systems of linear equations for both the transport system and the diffusion system. However, in the multilevel method, algebraically constructing coarse spaces is expensive and often unscalable. We study a subspace-based coarsening algorithm to address such a challenge by exploring the matrix structures of the transport equations and the nonlinear diffusion equations. We numerically demonstrate that the monolithic multilevel preconditioner with the subspace-based coarsening algorithm is twice as fast as that equipped with an unmodified coarsening approach on thousands of processor cores for an unstructured mesh neutron transport problem with billions of unknowns.

math.NA↗

arXiv2019

MOOSE: Enabling Massively Parallel Multiphysics Simulation

Harnessing modern parallel computing resources to achieve complex multi-physics simulations is a daunting task. The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable such development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite-element solve that is handled internally. Through the use of interfaces and inheritance, each portion of a simulation becomes reusable and composable in a manner that allows disparate research groups to share code and create an ecosystem of growing capability that lowers the barrier for the creation of multiphysics simulation codes. Included within the framework is a unique capability for building multiscale, multiphysics simulations through simultaneous execution of multiple sub-applications with data transfers between the scales. Other capabilities include automatic differentiation, scaling to a large number of processors, hybrid parallelism, and mesh adaptivity. To date, MOOSE-based applications have been created in areas of science and engineering such as nuclear physics, geothermal science, magneto-hydrodynamics, seismic events, compressible and incompressible fluid flow, microstructure evolution, and advanced manufacturing processes.

cs.MS↗

arXiv2019

A scalable multilevel domain decomposition preconditioner with a subspace-based coarsening algorithm for the neutron transport calculations

The multigroup neutron transport equations has been widely used to study the interactions of neutrons with their background materials in nuclear reactors. High-resolution simulations of the multigroup neutron transport equations using modern supercomputers require the development of scalable parallel solving techniques. In this paper, we study a scalable transport method for solving the algebraic system arising from the discretization of the multigroup neutron transport equations. The proposed transport method consists of a fully coupled Newton solver for the generalized eigenvalue problems and GMRES together with a novel multilevel domain decomposition preconditioner for the Jacobian system. The multilevel preconditioner has been successfully used for many problems, but the construction of coarse spaces for certain problems, especially for unstructured mesh problems, is expensive and often unscalable. We introduce a new subspace-based coarsening algorithm to address this issue by exploring the structure of the matrix in the discretized version of the neutron transport problems. We numerically demonstrate that the proposed transport method is highly scalable with more than 10,000 processor cores for the 3D C5G7 benchmark problem on unstructured meshes with billions of unknowns. Compared with the traditional multilevel domain decomposition method, the new approach equipped with the subspace-based coarsening algorithm is much faster on the construction of coarse spaces.

math.NA↗

arXiv2019

Parallel memory-efficient all-at-once algorithms for the sparse matrix triple products in multigrid methods

Multilevel/multigrid methods is one of the most popular approaches for solving a large sparse linear system of equations, typically, arising from the discretization of partial differential equations. One critical step in the multilevel/multigrid methods is to form coarse matrices through a sequence of sparse matrix triple products. A commonly used approach for the triple products explicitly involves two steps, and during each step a sparse matrix-matrix multiplication is employed. This approach works well for many applications with a good computational efficiency, but it has a high memory overhead since some auxiliary matrices need to be temporarily stored for accomplishing the calculations. In this work, we propose two new algorithms that construct a coarse matrix with taking one pass through the input matrices without involving any auxiliary matrices for saving memory. The new approaches are referred to as "all-at-once" and "merged all-at-once", and the traditional method is denoted as "two-step". The all-at-once and the merged all-at-once algorithms are implemented based on hash tables in PETSc as part of this work with a careful consideration on the performance in terms of the compute time and the memory usage. We numerically show that the proposed algorithms and their implementations are perfectly scalable in both the compute time and the memory usage with up to 32,768 processor cores for a model problem with 27 billions of unknowns. The scalability is also demonstrated for a realistic neutron transport problem with over 2 billion unknowns on a supercomputer with 10,000 processor cores. Compared with the traditional two-step method, the all-at-once and the merged all-at-once algorithms consume much less memory for both the model problem and the realistic neutron transport problem meanwhile they are able to maintain the computational efficiency.

cs.DC↗

arXiv2019

A highly parallel multilevel Newton-Krylov-Schwarz method with subspace-based coarsening and partition-based balancing for the multigroup neutron transport equations on 3D unstructured meshes

The multigroup neutron transport equations have been widely used to study the motion of neutrons and their interactions with the background materials. Numerical simulation of the multigroup neutron transport equations is computationally challenging because the equations is defined on a high dimensional phase space (1D in energy, 2D in angle, and 3D in spatial space), and furthermore, for realistic applications, the computational spatial domain is complex and the materials are heterogeneous. The multilevel domain decomposition methods is one of the most popular algorithms for solving the multigroup neutron transport equations, but the construction of coarse spaces is expensive and often not strongly scalable when the number of processor cores is large. In this paper, we study a highly parallel multilevel Newton-Krylov-Schwarz method equipped with several novel components, such as subspace-based coarsening, partition-based balancing and hierarchical mesh partitioning, that enable the overall simulation strongly scalable in terms of the compute time. Compared with the traditional coarsening method, the subspace-based coarsening algorithm significantly reduces the cost of the preconditioner setup that is often unscalable. In addition, the partition-based balancing strategy enhances the parallel efficiency of the overall solver by assigning a nearly-equal amount of work to each processor core. The hierarchical mesh partitioning is able to generate a large number of subdomains and meanwhile minimizes the off-node communication. We numerically show that the proposed algorithm is scalable with more than 10,000 processor cores for a realistic application with a few billions unknowns on 3D unstructured meshes.

math.NA↗

arXiv2018

A general-purpose hierarchical mesh partitioning method with node balancing strategies for large-scale numerical simulations

Large-scale parallel numerical simulations are essential for a wide range of engineering problems that involve complex, coupled physical processes interacting across a broad range of spatial and temporal scales. The data structures involved in such simulations (meshes, sparse matrices, etc.) are frequently represented as graphs, and these graphs must be optimally partitioned across the available computational resources in order for the underlying calculations to scale efficiently. Partitions which minimize the number of graph edges that are cut (edge-cuts) while simultaneously maintaining a balance in the amount of work (i.e. graph nodes) assigned to each processor core are desirable, and the performance of most existing partitioning software begins to degrade in this metric for partitions with more than than $O(10^3)$ processor cores. In this work, we consider a general-purpose hierarchical partitioner which takes into account the existence of multiple processor cores and shared memory in a compute node while partitioning a graph into an arbitrary number of subgraphs. We demonstrate that our algorithms significantly improve the preconditioning efficiency and overall performance of realistic numerical simulations running on up to 32,768 processor cores with nearly $10^9$ unknowns.

cs.MS↗

arXiv2018

Simulation of unsteady blood flows in a patient-specific compliant pulmonary artery with a highly parallel monolithically coupled fluid-structure interaction algorithm

Computational fluid dynamics (CFD) is increasingly used to study blood flows in patient-specific arteries for understanding certain cardiovascular diseases. The techniques work quite well for relatively simple problems, but need improvements when the problems become harder in the case when (1) the geometry becomes complex (from a few branches to a full pulmonary artery), (2) the model becomes more complex (from fluid-only calculation to coupled fluid-structure interaction calculation), (3) both the fluid and wall models become highly nonlinear, and (4) the computer on which we run the simulation is a supercomputer with tens of thousands of processor cores. To push the limit of CFD in all four fronts, in this paper, we develop and study a highly parallel algorithm for solving a monolithically coupled fluid-structure system for the modeling of the interaction of the blood flow and the arterial wall. As a case study, we consider a patient-specific, full size pulmonary artery obtained from CT (Computed Tomography) images, with an artificially added layer of wall with a fixed thickness. The fluid is modeled with a system of incompressible Navier-Stokes equations and the wall is modeled by a geometrically nonlinear elasticity equation. As far as we know this is the first time the unsteady blood flow in a full pulmonary artery is simulated without assuming a rigid wall. The proposed numerical algorithm and software scale well beyond 10,000 processor cores on a supercomputer for solving the fluid-structure interaction problem discretized with a stabilized finite element method in space and an implicit scheme in time involving hundreds of millions of unknowns.

physics.comp-ph↗

arXiv2017

Overview of the Incompressible Navier-Stokes simulation capabilities in the MOOSE Framework

The Multiphysics Object Oriented Simulation Environment (MOOSE) framework is a high-performance, open source, C++ finite element toolkit developed at Idaho National Laboratory. MOOSE was created with the aim of assisting domain scientists and engineers in creating customizable, high-quality tools for multiphysics simulations. While the core MOOSE framework itself does not contain code for simulating any particular physical application, it is distributed with a number of physics "modules" which are tailored to solving e.g. heat conduction, phase field, and solid/fluid mechanics problems. In this report, we describe the basic equations, finite element formulations, software implementation, and regression/verification tests currently available in MOOSE's navier_stokes module for solving the Incompressible Navier-Stokes (INS) equations.

math.NA↗