Good Parallel Scalability Research Articles

Study on Atomistic Behavior of Macromolecules by Molecular Dynamics Simulation and its Enlargement in Scale

Properties of materials are constituted by molecular structures. The molecular dynamics simulation technique has an advantage in clarifying the relationship between molecular structure and properties through analysis of atomistic structure and dynamics, which are difficult to measure by experiment. On the other hand, major problems with applying this technique to larger and more complicated molecules consist of controlling the calculation time, and the larger scale of molecules. In this study, first, a relationship between melting properties and molecular structure is investigated by using the atomistic molecular dynamics simulation to verify effectiveness of this technique. The relationship between the reciprocal of self diffusion coefficients obtained from the simulation and 1/2 FT, which is an experimental indicator for melting properties, shows good agreement. This result indicates that atomistic molecular behavior affects melting properties, and implies that polymers with Bisphenol A structure have higher molecular dynamics. Next, a reduction of calculation cost and an enlargement of molecular simulation are examined by following 2 approaches—the coarse-grained united-atom model and the supercomputer K computer. The coarse-grained united-atom model shows good parallel scalability. For polycarbonates, it has the equivalent accuracy of the full atomistic model, and is more than 15 times faster than the full atomistic model with the same number of molecules and cores. These techniques expanded the spatial scale of molecular dynamics simulation by one digit larger than ever before.

A semi‐local spectral/hp element solver for linear elasticity problems

SUMMARYWe develop an efficient semi‐local method for speeding up the solution of linear systems arising in spectral/hp element discretization of the linear elasticity equations. The main idea is to approximate the element‐wise residual distribution with a localization operator we introduce in this paper, and subsequently solve the local linear system. Additionally, we decouple the three directions of displacement in the localization operator, hence enabling the use of an efficient low energy preconditioner for the conjugate gradient solver. This approach is effective for both nodal and modal bases in the spectral/hp element method, but here, we focus on the modal hierarchical basis. In numerical tests, we verify that there is no loss of accuracy in the semi‐local method, and we obtain good parallel scalability and substantial speed‐up compared to the original formulation. In particular, our tests include both structure‐only and fluid‐structure interaction problems, with the latter modeling a 3D patient‐specific brain aneurysm. Copyright © 2014 John Wiley & Sons, Ltd.

An SMP soft classification algorithm for remote sensing

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 108pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over 4min using 32 processors.

A Scalable Numerical Method for Simulating Flows Around High-Speed Train Under Crosswind Conditions

AbstractThis paper presents a parallel Newton-Krylov-Schwarz method for the numerical simulation of unsteady flows at high Reynolds number around a high-speed train under crosswind. With a realistic train geometry, a realistic Reynolds number, and a realistic wind speed, this is a very challenging computational problem. Because of the limited parallel scalability, commercial CFD software is not suitable for supercomputers with a large number of processors. We develop a Newton-Krylov-Schwarz based fully implicit method, and the corresponding parallel software, for the 3D unsteady incompressible Navier-Stokes equations discretized with a stabilized finite element method on very fine unstructured meshes. We test the algorithm and software for flows passing a train modeled after China’s high-speed train CRH380B, and we also compare our results with results obtained from commercial CFD software. Our algorithm shows very good parallel scalability on a supercomputer with over one thousand processors.

Efficient Parameter Estimation and Implementation of a Contour Integral-Based Eigensolver

We consider an eigensolver for computing eigenvalues in a given domain and the corresponding eigenvectors of large-scale matrix pencils. The Sakurai-Sugiura (SS) method is an eigensolver based on complex moments given by contour integrals of matrix inverses with several shift points. This method has good parallel scalability, and is suitable for massively parallel computing environments. The SS method has several parameters, and the choice of these parameters is crucial for achieving high accuracy and good parallel performance. We discuss some numerical properties of the method, and present efficient parameter estimation techniques. We demonstrate the efficiency of our method with numerical experiments.

A discontinuous Galerkin conservative level set scheme for interface capturing in multiphase flows

The accurate conservative level set (ACLS) method of Desjardins et al. [O. Desjardins, V. Moureau, H. Pitsch, An accurate conservative level set/ghost fluid method for simulating turbulent atomization, J. Comput. Phys. 227 (18) (2008) 8395–8416] is extended by using a discontinuous Galerkin (DG) discretization. DG allows for the scheme to have an arbitrarily high order of accuracy with the smallest possible computational stencil resulting in an accurate method with good parallel scaling. This work includes a DG implementation of the level set transport equation, which moves the level set with the flow field velocity, and a DG implementation of the reinitialization equation, which is used to maintain the shape of the level set profile to promote good mass conservation. A near second order converging interface curvature is obtained by following a height function methodology (common amongst volume of fluid schemes) in the context of the conservative level set. Various numerical experiments are conducted to test the properties of the method and show excellent results, even on coarse meshes. The tests include Zalesak’s disk, two-dimensional deformation of a circle, time evolution of a standing wave, and a study of the Kelvin–Helmholtz instability. Finally, this novel methodology is employed to simulate the break-up of a turbulent liquid jet.

Highly parallel particle-laden flow solver for turbulence research

In this paper, we present a Highly Parallel Particle-laden flow Solver for Turbulence Research (HiPPSTR). HiPPSTR is designed to perform three-dimensional direct numerical simulations of homogeneous turbulent flows using a pseudospectral algorithm with Lagrangian tracking of inertial point and/or fluid particles with one-way coupling on massively parallel architectures, and is the most general and efficient multiphase flow solver of its kind. We discuss the governing equations, parallelization strategies, time integration techniques, and interpolation methods used by HiPPSTR. By quantifying the errors in the numerical solution, we obtain optimal parameters for a given domain size and Reynolds number, and thereby achieve good parallel scaling on O(104) processors.

Preconditioning for modal discontinuous Galerkin methods for unsteady 3D Navier–Stokes equations

We compare different block preconditioners in the context of parallel time adaptive higher order implicit time integration using Jacobian-free Newton–Krylov (JFNK) solvers for discontinuous Galerkin (DG) discretizations of the three dimensional time dependent Navier–Stokes equations. A special emphasis of this work is the performance for a relative high number of processors, i.e. with a low number of elements on the processor. For high order DG discretizations, a particular problem that needs to be addressed is the size of the blocks in the Jacobian. Thus, we propose a new class of preconditioners that exploits the hierarchy of modal basis functions and introduces a flexible order of the off-diagonal Jacobian blocks. While the standard preconditioners ‘block Jacobi’ (no off-blocks) and full symmetric Gauss–Seidel (full off-blocks) are included as special cases, the reduction of the off-block order results in the new scheme ROBO-SGS. This allows us to investigate the impact of the preconditioner’s sparsity pattern with respect to the computational performance. Since the number of iterations is not well suited to judge the efficiency of a preconditioner, we additionally consider CPU time for the comparisons. We found that both block Jacobi and ROBO-SGS have good overall performance and good strong parallel scaling behavior.

Long read alignment based on maximal exact match seeds

Motivation: The explosive growth of next-generation sequencing datasets poses a challenge to the mapping of reads to reference genomes in terms of alignment quality and execution speed. With the continuing progress of high-throughput sequencing technologies, read length is constantly increasing and many existing aligners are becoming inefficient as generated reads grow larger.Results: We present CUSHAW2, a parallelized, accurate, and memory-efficient long read aligner. Our aligner is based on the seed-and-extend approach and uses maximal exact matches as seeds to find gapped alignments. We have evaluated and compared CUSHAW2 to the three other long read aligners BWA-SW, Bowtie2 and GASSST, by aligning simulated and real datasets to the human genome. The performance evaluation shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads.Availability: CUSHAW2, written in C++, and all simulated datasets are available at http://cushaw2.sourceforge.netContact: liuy@uni-mainz.de; bertil.schmidt@uni-mainz.deSupplementary information: Supplementary data are available at Bioinformatics online.

A NEW MONTE CARLO METHOD FOR TIME-DEPENDENT NEUTRINO RADIATION TRANSPORT

Monte Carlo approaches to radiation transport have several attractive properties such as simplicity of implementation, high accuracy, and good parallel scaling. Moreover, Monte Carlo methods can handle complicated geometries and are relatively easy to extend to multiple spatial dimensions, which makes them potentially interesting in modeling complex multi-dimensional astrophysical phenomena such as core-collapse supernovae. The aim of this paper is to explore Monte Carlo methods for modeling neutrino transport in core-collapse supernovae. We generalize the Implicit Monte Carlo photon transport scheme of Fleck & Cummings and gray discrete-diffusion scheme of Densmore et al. to energy-, time-, and velocity-dependent neutrino transport. Using our 1D spherically-symmetric implementation, we show that, similar to the photon transport case, the implicit scheme enables significantly larger timesteps compared with explicit time discretization, without sacrificing accuracy, while the discrete-diffusion method leads to significant speed-ups at high optical depth. Our results suggest that a combination of spectral, velocity-dependent, Implicit Monte Carlo and discrete-diffusion Monte Carlo methods represents a robust approach for use in neutrino transport calculations in core-collapse supernovae. Our velocity-dependent scheme can easily be adapted to photon transport.

A parallel block preconditioner for large-scale poroelasticity with highly heterogeneous material parameters

Large-scale simulations of coupled flow in deformable porous media require iterative methods for solving the systems of linear algebraic equations. Construction of efficient iterative methods is particularly challenging in problems with large jumps in material properties, which is often the case in realistic geological applications, such as basin evolution at regional scales. The success of iterative methods for such problems depends strongly on finding effective preconditioners with good parallel scaling properties, which is the topic of the present paper. We present a parallel preconditioner for Biot’s equations of coupled elasticity and fluid flow in porous media. The preconditioner is based on an approximation of the exact inverse of the two-by-two block system arising from a finite element discretisation. The approximation relies on a highly scalable approximation of the global Schur complement of the coefficient matrix, combined with generally available state-of-the-art multilevel preconditioners for the individual blocks. This preconditioner is shown to be robust on problems with highly heterogeneous material parameters. We investigate the weak and strong parallel scaling of this preconditioner on up to 512 processors and demonstrate its ability on a realistic basin-scale problem in poroelasticity with over eight million tetrahedral elements.

GPAW - massively parallel electronic structure calculations with Python-based software

Electronic structure calculations are a widely used tool in materials science and large consumer of supercomputing resources. Traditionally, the software packages for these kind of simulations have been implemented in compiled languages, where Fortran in its different versions has been the most popular choice. While dynamic, interpreted languages, such as Python, can increase the effciency of programmer, they cannot compete directly with the raw performance of compiled languages. However, by using an interpreted language together with a compiled language, it is possible to have most of the productivity enhancing features together with a good numerical performance. We have used this approach in implementing an electronic structure simulation software GPAW using the combination of Python and C programming languages. While the chosen approach works well in standard workstations and Unix environments, massively parallel supercomputing systems can present some challenges in porting, debugging and profiling the software. In this paper we describe some details of the implementation and discuss the advantages and challenges of the combined Python/C approach. We show that despite the challenges it is possible to obtain good numerical performance and good parallel scalability with Python based software.

Fully-coupled engineering and mesoscale simulations of thermal conductivity in UO2 fuel using an implicit multiscale approach

Though the thermal conductivity of solid UO2 is well characterized, its value is sensitive to microstructure changes. In this study, we propose a two-way coupling of a mesoscale phase field irradiation model to an engineering scale, finite element calculation to capture the microstructure dependence of the conductivity. To achieve this, the engineering scale thermomechanics system is solved in a parallel, fully-coupled, fully-implicit manner using the preconditioned Jacobian-free Newton Krylov (JFNK) method. Within the JFNK function evaluation phase of the calculation, the microstructure-influenced thermal conductivity is calculated by the mesoscale model and passed back to the engineering scale calculation. Initial results illustrate quadratic nonlinear convergence and good parallel scalability.

Parallel scalable adjoint-based adaptive solution of variable-viscosity Stokes flow problems

We present a framework for parallel adaptive solution of variable-viscosity Stokes flow problems. We focus on data structures, algorithms, and solvers that can scale to thousands of processor cores. The problem is discretized by octree-based finite elements with explicit enforcement of continuity constraints at hanging nodes. The parallel octree structure allows for fast neighbor-finding and facilitates local coarsening and refinement of the mesh. Mesh adaptivity is driven by a posteriori error indicators, including adjoint-based goal-oriented techniques. Dynamic load-balancing is achieved by dynamically partitioning a Morton-ordered space-filling curve. The Stokes system is solved iteratively using the minimum residual method (MINRES), preconditioned by a Schur-complement-based approximate inverse that employs algebraic multigrid V-cycle approximations of the inverses of the Poisson-like operators. We demonstrate the effectiveness of this framework on several testbed problems with up to 6 orders of magnitude variation in viscosity and up to 1.7 billion unknowns, on up to 4096 cores. The results indicate that the overhead due to all AMR components is less than 3% of the overall solve time, the solver exhibits very good algorithmic and parallel implementation scalability, the solver is insensitive to the magnitude of viscosity variation, and adjoint-based adaptivity results in over two orders of magnitude reduction in number of unknowns and up to an order of magnitude improvement in runtime relative to a uniform mesh, for the same level of error.

High Performance Hybrid Direct-Iterative Solution Method for Large Scale Structural Analysis Problems

High performance direct-iterative hybrid linear solver for large scale finite element problem is developed. Direct solution method is robust but difficult to parallelize, whereas iterative solution method is opposite for direct method. Therefore, combining two solution methods is desired to get both high performance parallel efficiency and numerical robustness for large scale structural analysis problems. Hybrid method mentioned in this paper is based on FETI-DP (Finite Element Tearing and Interconnecting-Dual Primal method) which has good parallel scalability and efficiency. It is suitable for fourth and second order finite element elliptic problems including structural analysis problems. We are using the hybrid concept of theses two solution method categories, combining the multifrontal solver into FETI-DP based iterative solver. Hybrid solver is implemented for our general structural analysis code, IPSAP.

A data-distributed parallel algorithm for wavelet-based fusion of remote sensing images

With the increasing importance of multiplatform remote sensing missions, the fast integration or fusion of digital images from disparate sources has become critical to the success of these endeavors. In this paper, to speed up the fusion process, a Data-distributed Parallel Algorithm for wavelet-based Fusion (DPAF for short) of remote sensing images which are not geo-registered remote sensing images is presented for the first time. To overcome the limitations on memory space as well as the computing capability of a single processor, data distribution, data-parallel processing and load balancing techniques are integrated into DPAF. To avoid the inherent communication overhead of a wavelet-based fusion method, a special design called redundant partitioning is used, which is inspired by the characteristics of wavelet transform. Finally, DPAF is evaluated in theory and tested on a 32-CPU cluster of workstations. The experimental results show that our algorithm has good parallel performance and scalability.

A versatile incompressible Navier–Stokes solver for blood flow application

AbstractWe present in this paper an integrated approach to compute quickly an incompressible Navier–Stokes (NS) flow in a section of a large blood vessel using medical imaging data. The goal is essentially to provide a first‐order approximation of some main quantities of interest in cardiovascular disease: the shear stress and the pressure on the wall. The NS solver relies on the L2 penalty approach pioneered by Caltagirone and co‐workers and combines nicely with a level set method based on the Mumford–Shah energy model. Simulations on stenosis cases based on angiogram are run in parallel with MatlabMPI on a shared‐memory machine. While MatlabMPI communications are based on the load and save functions of Matlab and have high latency indeed, we show that our Aitken–Schwarz domain decomposition algorithm provides a good parallel efficiency and scalability of the NS code. Copyright © 2006 John Wiley & Sons, Ltd.

Second derivatives in generalized Born theory

Generalized Born solvation models offer a popular method of including electrostatic aspects of solvation free energies within an analytical model that depends only upon atomic coordinates, charges, and dielectric radii. Here, we describe how second derivatives with respect to Cartesian coordinates can be computed in an efficient manner that can be distributed over multiple processors. This approach makes possible a variety of new methods of analysis for these implicit solvation models. We illustrate three of these methods here: the use of Newton-Raphson optimization to obtain precise minima in solution; normal mode analysis to compute solvation effects on the mechanical properties of DNA; and the calculation of configurational entropies in the MM/GBSA model. An implementation of these ideas, using the Amber generalized Born model, is available in the nucleic acid builder (NAB) code, and we present examples for proteins with up to 45,000 atoms. The code has been implemented for parallel computers using both the OpenMP and MPI environments, and good parallel scaling is seen with as many as 144 OpenMP processing threads or MPI processing tasks.

PPPM and TreePM Methods on GRAPE Systems for Cosmological N-Body Simulations

Abstract We present Particle–Particle–Particle–Mesh (PPPM) and Tree Particle–Mesh (TreePM) implementations on GRAPE-5 and GRAPE-6A systems, special-purpose hardware accelerators for gravitational many-body simulations. In our PPPM and TreePM implementations on GRAPE, the computational time is significantly reduced compared with the conventional implementations without GRAPE, especially under strong particle clustering, and is almost constant irrespective of the degree of particle clustering. We describe our survey of two simulation parameters, the PM grid spacing and the opening parameter for the most optimal combination of force accuracy and computational speed. We also describe the parallelization of these implementations on a PC–GRAPE cluster, in which each node has one GRAPE board, and present the optimal configuration of simulation parameters for good parallel scalability.

Racoon: A parallel mesh-adaptive framework for hyperbolic conservation laws

We report on the development of a computational framework for the parallel, mesh-adaptive solution of systems of hyperbolic conservation laws like the time-dependent Euler equations in compressible gas dynamics or Magneto-Hydrodynamics (MHD) and similar models in plasma physics. Local mesh refinement is realized by the recursive bisection of grid blocks along each spatial dimension, implemented numerical schemes include standard finite-differences as well as shock-capturing central schemes, both in connection with Runge–Kutta type integrators. Parallel execution is achieved through a configurable hybrid of POSIX-multi-threading and MPI distribution with dynamic load balancing. One-, two- and three-dimensional test computations for the Euler equations have been carried out and show good parallel scaling behavior. The Racoon framework is currently used to study the formation of singularities in plasmas and fluids.