Scalable Parallel Research Articles

A Nonlinear Approach in the Quantification of Numerical Uncertainty by High-Order Methods for Compressible Turbulence with Shocks

This is a comprehensive overview on our research work to link interdisciplinary modeling and simulation techniques to improve the predictability and reliability simulations (PARs) of compressible turbulence with shock waves for general audiences who are not familiar with our nonlinear approach. This focused nonlinear approach is to integrate our “nonlinear dynamical approach” with our “newly developed high order entropy-conserving, momentum-conserving and kinetic energy-preserving methods” in the quantification of numerical uncertainty in highly nonlinear flow simulations. The central issue is that the solution space of discrete genuinely nonlinear systems is much larger than that of the corresponding genuinely nonlinear continuous systems, thus obtaining numerical solutions that might not be solutions of the continuous systems. Traditional uncertainty quantification (UQ) approaches in numerical simulations commonly employ linearized analysis that might not provide the true behavior of genuinely nonlinear physical fluid flows. Due to the rapid development of high-performance computing, the last two decades have been an era when computation is ahead of analysis and when very large-scale practical computations are increasingly used in poorly understood multiscale data-limited complex nonlinear physical problems and non-traditional fields. This is compounded by the fact that the numerical schemes used in production computational fluid dynamics (CFD) computer codes often do not take into consideration the genuinely nonlinear behavior of numerical methods for more realistic modeling and simulations. Often, the numerical methods used might have been developed for weakly nonlinear flow or different flow types other than the flow being investigated. In addition, some of these methods are not discretely physics-preserving (structure-preserving); this includes but is not limited to entropy-conserving, momentum-conserving and kinetic energy-preserving methods. Employing theories of nonlinear dynamics to guide the construction of more appropriate, stable and accurate numerical methods could help, e.g., (a) delineate solutions of the discretized counterparts but not solutions of the governing equations; (b) prevent numerical chaos or numerical “turbulence” leading to FALSE predication of transition to turbulence; (c) provide more reliable numerical simulations of nonlinear fluid dynamical systems, especially by direct numerical simulations (DNS), large eddy simulations (LES) and implicit large eddy simulations (ILES) simulations; and (d) prevent incorrect computed shock speeds for problems containing stiff nonlinear source terms, if present. For computation intensive turbulent flows, the desirable methods should also be efficient and exhibit scalable parallelism for current high-performance computing. Selected numerical examples to illustrate the genuinely nonlinear behavior of numerical methods and our integrated approach to improve PARs are included.

Evaluation of Granite Fertility Utilizing Porphyry Indicator Minerals (Zircon, Apatite, and Titanite) and Geochemical Data: A Case Study from an Emerging Metallogenic Province in the Taimyr Peninsula, Siberian High Arctic

The Taimyr Peninsula in the Russian High Arctic comprises a late Paleozoic-early Mesozoic collisional belt where several porphyry-type mineralization occurrences were identified during the last decade, making this area a potential exploration target for Cu-Mo deposits. In order to further evaluate the metallogenic potential of the poorly outcropped northeastern part of Taimyr, samples from seven granitoid intrusions were investigated in this study aimed to evaluate the granite fertility based on petrography, geochemistry, and composition of porphyry indicator minerals (zircon, apatite, and titanite). The studied intrusions represent small to moderate-sized bodies (40–800 km2) composed of biotite (±amphibole) quartz monzonites, granodiorites, granites, and biotite leucogranites that formed in the course of late Paleozoic-early Mesozoic tectono-magmatic events at the Siberian margins. The late Carboniferous Tessemsky massif represents suprasubduction granitoid series, while the Pekinskiy, Shirokinskiy, Dorozhinskiy, Kristifensenskiy, and Yuzhno-Lodochnikovskiy massifs are correlated with the early Triassic Siberian Traps LIP. The rocks of intrusions comprise a relatively uniform geochemically, predominantly magnesian, slightly peraluminous, calc-alkaline high-K amphibole-bearing I-type granitoid series with adakitic affinity, where Triassic plume-related granitoids inherit geochemical signatures of Carboniferous supra-subduction granitoids, and all rock types are marked by enrichment in LILE and negative Ta, Nb, and Ti anomalies. It is suggested that the adakitic geochemical characteristics of the Taimyr granites are a result of derivation from a relatively homogeneous mafic lower crustal source that formed at the stage of Carboniferous continental subduction and continued to produce granitic melts in the course of the early Mesozoic magmatic evolution. Whole rock geochemistry and composition of porphyry mineral indicators (zircon, apatite, and titanite) indicate that the Taimyr granites crystallized from oxidized water-saturated magmas at moderate temperatures, with the majority of samples showing characteristics typical for porphyry-fertile granites worldwide (fO2 = ΔFMQ +1 to +3 with zircon Eu/Eu* > 0.4 and apatite SO3 > 0.2 wt.%). Data from Dorozhinskiy, Kristifensenskiy, Pekinskiy, and Tessemskiy intrusions fully match geochemical criteria for porphyry-fertile granitoids, and these massifs are considered the most prospective for Cu-Mo mineralization. Granites from Shirokinskiy and Yuzhno-Lodochnikovskiy intrusions only partially match compositional constraints for fertile melts and can be considered as second-tier exploration targets. Finally, available data for the Simsovsky massif preclude its classification as a porphyry-fertile body. These conclusions are in line with previously developed exploration criteria for the northeastern Taimyr, showing that geochemical indicators of granite-fertility can be used on a regional scale in parallel with other exploration methods.

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Performance Analysis and Parallel Scalability of Numerical Methods for Fractional-in-Space Diffusion Problems with Adaptive Time Stepping

We study numerical methods and algorithms for time-dependent fractional-in-space diffusion problems. The considered anomalous diffusion is modelled by the fractional Laplacian (−Δ)α, 0<α<1, following the integral definition. Fractional diffusion is non-local, and the finite element method (FEM) discretization in space leads to a dense stiffness matrix. It is well known that numerically solving such non-local boundary value problems is expensive. Difficulties increase significantly when the problem is time-dependent. The aim of the article is to develop computationally efficient methods and algorithms. There are two main features of our approach. Hierarchical semi-separable (HSS) compression is applied for an approximate solution of the arising linear systems. For time discretization, we use an adaptive forward–backward Euler scheme. The properties of the composite algorithm thus obtained are investigated. In particular, the block representation of HSS compression allowed us to upgrade the HSS solver to efficiently handle varying diagonally perturbed transition matrices corresponding to changing time steps. The contribution of the paper is threefold. The methods are completely constructive, which allows for a clearly structured description of the algorithms. A theoretical estimate of the computational complexity is presented. It shows the advantages of the adaptive time stepping in combination with the HSS solver. Theoretical results are supported by representative numerical experiments. Both sequential and parallel scalability and efficiency are analyzed. The presented results provide convincing proof of the concept of the proposed methods and algorithms.

Scalable quantum detector tomography by high-performance computing

Abstract At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyze these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterizing their operation. While classical computational approaches cannot perform like-for-like computations of quantum systems beyond a certain scale, classical high-performance computing (HPC) may nevertheless be useful for precisely these characterization and certification tasks. By developing open-source customized algorithms using high-performance computing, we perform quantum tomography on a megascale quantum photonic detector covering a Hilbert space of 106. This requires finding 108 elements of the matrix corresponding to the positive operator valued measure (POVM), the quantum description of the detector, and is achieved in minutes of computation time. Moreover, by exploiting the structure of the problem, we achieve highly efficient parallel scaling, paving the way for quantum objects up to a system size of 1012 elements to be reconstructed using this method. In general, this shows that a consistent quantum mechanical description of quantum phenomena is applicable at everyday scales. More concretely, this enables the reconstruction of large-scale quantum sources, processes and detectors used in computation and sampling tasks, which may be necessary to prove their nonclassical character or quantum computational advantage.

Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2

Abstract. A novel split-explicit (SE) external mode solver for the Finite volumE Sea ice–Ocean Model (FESOM2) and its sub-versions is presented. It is compared with the semi-implicit (SI) solver currently used in FESOM2. The split-explicit solver for the external mode utilizes a dissipative asynchronous (forward–backward) time-stepping scheme that does not require additional filtering during the barotropic sub-cycling. Its implementation with arbitrary Lagrangian–Eulerian vertical coordinates like Z star (z*) and Z tilde (z̃) is explored. The comparisons are performed through multiple test cases involving idealized and realistic global simulations. The SE solver demonstrates lower phase errors and dissipation, while maintaining a simulated mean ocean state very similar to the SI solver. The SE solver is also shown to possess better run-time performance and parallel scalability across all workloads tested.

AHKASH: a new Hybrid particle-in-cell code for simulations of astrophysical collisionless plasma.

We introduce Astrophysical Hybrid-Kinetic simulations with the flash code ([Formula: see text]) - a new Hybrid particle-in-cell (PIC) code developed within the framework of the multiphysics code flash. The new code uses a second-order accurate Boris integrator and a predictor-predictor-corrector algorithm for advancing the Hybrid-kinetic equations, using the constraint transport method to ensure that magnetic fields are divergence-free. The code supports various interpolation schemes between the particles and grid cells, with post-interpolation smoothing to reduce finite particle noise. We further implement a [Formula: see text] method to study instabilities in weakly collisional plasmas. The new code is tested on standard physical problems such as the motion of charged particles in uniform and spatially varying magnetic fields, the propagation of Alfvén and whistler waves, and Landau damping of ion acoustic waves. We test different interpolation kernels and demonstrate the necessity of performing post-interpolation smoothing. We couple the turbgen turbulence driving module to the new Hybrid PIC code, allowing us to test the code on the highly complex physical problem of the turbulent dynamo. To investigate steady-state turbulence with a fixed sonic Mach number, it is important to maintain isothermal plasma conditions. Therefore, we introduce a novel cooling method for Hybrid PIC codes and provide tests and calibrations of this method to keep the plasma isothermal. We describe and test the 'hybrid precision' method, which significantly reduces (by a factor [Formula: see text]) the computational cost, without compromising the accuracy of the numerical solutions. Finally, we test the parallel scalability of the new code, showing excellent scaling up to 10,000 cores.

Artificial neural network aided vapor–liquid equilibrium model for multi-component high-pressure transcritical flows with phase change

In this work, an artificial neural network (ANN) aided vapor–liquid equilibrium (VLE) model is developed and coupled with a fully compressible computational fluid dynamics (CFD) solver to simulate the transcritical processes occurring in high-pressure liquid-fueled propulsion systems. The ANN is trained in Python using TensorFlow, optimized for inference using Open Neural Network Exchange Runtime, and coupled with a C++ based CFD solver. This plug-and-play model/methodology can be used to convert any multi-component CFD solver to simulate transcritical processes using only open-source packages, without the need of in-house VLE model development. The solver is then used to study high-pressure transcritical shock-droplet interaction in both two- and four-component systems and a turbulent temporal mixing layer (TML), where both qualitative and quantitative agreement (maximum relative error less than 5%) is shown with respect to results based on both direct evaluation and the state-of-the-art in situ adaptive tabulation (ISAT) method. The ANN method showed a 6 times speed-up over the direct evaluation and a 2.2-time speed-up over the ISAT method for the two-component shock-droplet interaction case. The ANN method is faster than the ISAT method by 12 times for the four-component shock-droplet interaction. A 7 times speed-up is observed for the TML case for the ANN method compared to the ISAT method while achieving a data compression factor of 2881. The ANN method also shows intrinsic load balancing, unlike traditional VLE solvers. A strong parallel scalability of this ANN method with the number of processors was observed for all the three test cases. Code repository for 0D VLE solvers, and C++ ANN interface—https://github.com/UMN-CRFEL/ANN_VLE.git.

Multi-Feature Cross Attention-Induced Transformer Network for Hyperspectral and LiDAR Data Classification

Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network’s ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness.

Nodal discontinuous Galerkin methods for Maxwell's equations in Lorentz-Kerr-Raman medium without nonlinear algebraic solver

The propagation of electromagnetic waves is modeled by time-dependent Maxwell's equations coupled with constitutive laws that describe the responses of the media. In this work, we consider a nonlinear model that describes the electromagnetic wave in an optical medium with the linear Lorentz effect and the cubic nonlinear instantaneous Kerr and delayed Raman effects. Mathematically this model obeys an energy conservative/dissipative law. Though there have been active efforts in designing numerical methods (e.g. of finite difference / finite element /discontinuous Galerkin type) to simulate this model, the methods proposed here are distinctive in that they are free of any nonlinear algebraic solvers. Moreover, in the absence of the Raman effect, our methods also enjoy a provable discrete energy law, and optimal a priori error estimates are further established when the exact solutions are sufficiently smooth. The key ingredients of the new methods include some novel treatment in time discretizations and nodal discontinuous Galerkin spatial discretization for the specific nonlinearities, and they also render a local nature of the methods and hence their suitability for parallel implementation with great efficiency. Numerical experiments are performed to illustrate the accuracy, stability, computational efficiency and parallel scalability of the proposed methods. We further apply the methods to simulate some physically relevant problems in one, two, and three dimensions.

MiMiC: A high-performance framework for multiscale molecular dynamics simulations.

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient and flexible, we adopt an interoperable approach based on a multiple-program multiple-data (MPMD) paradigm, serving as an intermediary responsible for fast data exchange and interactions between the subsystems. The main goal of MiMiC is to avoid interfering with the underlying parallelization of the external programs, including the operability on hybrid architectures (e.g., CPU/GPU), and keep their setup and execution as close as possible to the original. At the moment, MiMiC offers an efficient implementation of electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling in simulations of large biomolecules using CPMD and GROMACS as QM and MM engines, respectively. However, as it is designed for high flexibility with general multiscale models in mind, it can be straightforwardly extended beyond QM/MM. In this article, we illustrate the software design and the features of the framework, which make it a compelling choice for multiscale simulations in the upcoming era of exascale high-performance computing.

Mathematical Analysis for GPU Framework for High Performance Computing with Improved Scalability, Reliability and Seamless Configurability

In the field of High Performance Computing (HPC), which is changing very quickly, using Graphics Processing Units (GPUs) has become essential for getting a lot of computing power and speed. This study gives a thorough mathematical analysis of a GPU system that is meant to improve scalability, stability, and configurability, all of which are very important for next-generation HPC apps. The suggested framework uses complex mathematical models and methods to make the best use of GPU resources, cut down on delay, and guarantee strong performance across a wide range of tasks. Scalable parallel processing methods are built into the framework so it can adapt to changing computing needs, making the most of speed and resource use. Fault-tolerant methods that lessen the effects of hardware breakdowns and computing mistakes also improve reliability, making sure that results are always correct and consistent. The framework can be set up in different ways because it is based on flexible design, which makes it easy to change and adapt to the needs of each application without affecting speed. In diverse computer settings, where different apps may have different working needs, this ability to change is very important. The mathematical analysis includes performance measures like speed, delay, and mistake rates, which give a solid basis for judging how well the system works. Additionally, tests comparing the suggested system to current GPU tools show that it is more reliable and scalable. This study helps make better, more reliable computer systems by looking at some of the biggest problems in GPU-based high-performance computing. The results show that using this method can greatly improve the skills of HPC systems, which can lead to progress in scientific study, data analysis, and complex models. In the end, this work shows how advanced GPU systems can help drive innovation in HPC, leading to more powerful, reliable, and flexible computing options.

A matrix-free parallel two-level deflation preconditioner for two-dimensional heterogeneous Helmholtz problems

We propose a matrix-free parallel two-level deflation method combined with the Complex Shifted Laplacian Preconditioner (CSLP) for two-dimensional heterogeneous Helmholtz problems encountered in seismic exploration, antennas, and medical imaging. These problems pose challenges in terms of accuracy and convergence due to scalability issues with numerical solvers. Motivated by the limitations imposed by excessive computational time and memory constraints when employing a sequential solver with constructed matrices, we parallelize the two-level deflation method without constructing any matrices. Our approach utilizes preconditioned Krylov subspace methods and approximates the CSLP preconditioner with a parallel geometric multigrid V-cycle. For the two-level deflation, standard inter-grid deflation vectors and further high-order deflation vectors are considered. As another main contribution, the matrix-free Galerkin coarsening approach and a novel re-discretization scheme as well as high-order finite-difference schemes on the coarse grid are studied to obtain wavenumber-independent convergence. The optimal settings for an efficient coarse-grid problem solver are investigated. Numerical experiments of model problems show that the wavenumber independence has been obtained for medium wavenumbers. The matrix-free parallel framework shows satisfactory weak and strong parallel scalability.

Containerization on a self-supervised active foveated approach to computer vision

Scaling complexity and appropriate data sets availability for training current Computer Vision (CV) applications poses major challenges. We tackle these challenges finding inspiration in biology and introducing a Self-supervised (SS) active foveated approach for CV. In this paper we present our solution to achieve portability and reproducibility by means of containerization utilizing Singularity. We also show the parallelization scheme used to run our models on ThetaGPU–an Argonne Leadership Computing Facility (ALCF) machine of 24 NVIDIA DGX A100 nodes. We describe how to use mpi4py to provide DistributedDataParallel (DDP) with all the needed information about world size as well as global and local ranks. We also show our dual pipe implementation of a foveator using NVIDIA Data Loading Library (DALI). Finally we conduct a series of strong scaling tests on up to 16 ThetaGPU nodes (128 GPUs), and show some variability trends in parallel scaling efficiency.

Multilevel Schur-complement algorithms for scalable parallel reservoir simulation with temperature variation

In reservoir simulation, the non-isothermal multiphase flow problem introduces the temperature variable to account for thermal effects, simultaneously posing challenges in efficiently solving the nonlinear systems for large-scale simulations. In this paper, we introduce and investigate a family of Schur-complement-based field-split algorithms designed for addressing non-isothermal multiphase flow problems, particularly those characterized by high heterogeneity. This algorithm involves decomposing a large system into smaller, more manageable sub-systems for solving non-isothermal multiphase flow problems with multiple physical fields, which enables parallel computation and makes it suitable for high-performance computing environments. Furthermore, a multilevel Schur-complement preconditioner, which involves applying the Schur-complement technique at each level of the hierarchy by capturing the coupling between different fields and physics, is proposed to enhance the efficiency and robustness of the parallel simulator. Large-scale simulations for both benchmark and realistic problems are conducted on a supercomputer, showcasing the method's efficacy in managing heat diffusion, significantly reducing linear iterations, and demonstrating a good parallel scalability.

Spherical harmonic–based DEM in LAMMPS: Implementation, verification and performance assessment

Particle shape plays a major role in the behaviour of most granular systems. This has led to increasing interest in the representation of arbitrarily shaped particles in discrete element method (DEM) simulations. In this paper, we present a simulation approach based on the representation of particle shapes using spherical harmonics where their radii can be calculated in spherical coordinates. An energy-conserving contact model is adopted which is based on the volume of overlap between interacting particles. Contact detection makes use of the bounding spheres of the interacting particles, simplifying its incorporation within a conventional sphere-based DEM code. The volume of overlap and other required quantities are calculated using Gaussian quadrature integration of the spherical cap formed by the bounding spheres. Both the accuracy and the computational cost increase with the number of quadrature points. The algorithm has been implemented as a LAMMPS user package, and verified by means of energy conservation. The performance and parallel scaling of the approach are illustrated, and an observed scaling limitation owing to load imbalance arising from the evaluation of the overlap volume is discussed. Program summaryProgram Title: SH-DEM LAMMPS packageCPC Library link to program files:https://doi.org/10.17632/vk6fj6yjtf.1Developer's repository link:https://github.com/EPCCed/lammps/tree/feature-sh-demLicensing provisions: GPLv2Programming language: C++Nature of problem: Particles are often highly non-spherical. Spherical harmonics provide a natural way to represent complex particle shapes within a discrete element method (DEM) simulation. However, there is no publicly available DEM code which allows particle shapes to be represented using spherical harmonics.Solution method: The SH-DEM package extends the capabilities of LAMMPS so that irregularly shaped particles can be represented using spherical harmonics. The package includes the definition of a new ‘shdem’ atom style for spherical harmonic particles, a time integration scheme for these particles based on the Velocity Verlet algorithm, algorithms for detecting and evaluating contacts between spherical harmonic particles, evaluation of the contact forces between these particles and rigid walls, and two energy computes for groups of spherical harmonic particles.Additional comments including restrictions and unusual features: The SH-DEM package is applicable only to 3D simulations. In order for a particle to be defined by spherical harmonics, it is required that any line segment drawn from an origin inside the particle crosses the contour of the particle's three-dimensional surface only once. If the ‘shdem’ atom style is used, the current implementation requires all particles to be defined using this atom style, e.g., mixtures of sphere and shdem atom styles are not permitted.

An Integral-like Numerical Approach for Solving Burgers’ Equation

The Burgers’ equation, commonly appeared in the study of turbulence, fluid dynamics, shock waves, and continuum mechanics, is a crucial part of the dynamical core of any numerical weather model, influencing simulated meteorological phenomena. While past studies have suggested several robust numerical approaches for solving the equation, many are too complicated for practical adaptation and too computationally expensive for operational deployment. This paper introduces an unconventional approach based on spline polynomial interpolations and the Hopf-Cole transformation. Using Taylor expansion to approximate the exponential term in the Hopf-Cole transformation, the analytical solution of the simplified equation is discretized to form our proposed scheme. The scheme is explicit and adaptable for parallel computing, although certain types of boundary conditions need to be employed implicitly. Three distinct test cases were utilized to evaluate its accuracy, parallel scalability, and numerical stability. In the aspect of accuracy, the schemes employed cubic and quintic spline interpolation perform equally well, managing to reduce the <i>ӏ</i><sub>1</sub>, <i>ӏ</i><sub>2</sub>, and <i>ӏ</i><sub>∞</sub> error norms down to the order of 10<sup>−4</sup>. Parallel scalability observed in the weak-scaling experiment depends on time step size but is generally as good as any explicit scheme. The stability condition is <i>ν</i>∆<i>t</i>/∆<i>x</i><sup>2</sup> > 0.02, including the viscosity coefficient <i>ν</i> due to the Hopf-Cole transformation step. From the stability condition, the schemes can run at a large time step size ∆<i>t</i> even when using a small grid spacing ∆<i>x</i>, emphasizing its suitability for practical applications such as numerical weather prediction.

FENNECS: A novel particle-in-cell code for simulating the formation of magnetized non-neutral plasmas trapped by electrodes of complex geometries

This paper presents the new 2D electrostatic particle-in-cell code FENNECS developed to study the formation of magnetized non-neutral plasmas in geometries with azimuthal symmetry. This code has been developed in the domain of gyrotron electron gun design, but solves general equations and can be applied in other domains of plasma physics. FENNECS is capable of simulating electron-neutral collisions using a Monte Carlo approach and considers both elastic and inelastic (ionization) processes. It is also capable of solving the Poisson equation on domains with arbitrary geometries with either Dirichlet or natural boundary conditions. The Poisson solver is based on a meshless Finite Element Method, called web-splines, based on b-splines of any order, and used for the first time in the domain of plasma physics. In addition, the effect of fast ions colliding with the electrodes and causing ion induced electron emission at the electrode surfaces has been implemented in the code. In this paper, the governing equations solved by FENNECS and the numerical methods used to solve them are presented. A number of verification cases are then reported. Finally, the parallelization scheme used in FENNECS and its parallel scalability are presented.

A Parallel Coloring Newton-Krylov Method for Multiphysics Coupling System in Nuclear Reactors

The Newton-Krylov method with the explicit Jacobian matrix is an efficient numerical method for solving the nuclear reactor nonlinear multiphysics coupling system. Compared with the Jacobian-free Newton-Krylov (JFNK) method, it has a better preconditioner matrix (the Jacobian matrix itself) and can achieve a more stable and faster convergence. How to compute the Jacobian matrix efficiently is a key issue for this method. The graph coloring algorithm is an essential technique and has been used to reduce the Jacobian computational burden by exploiting its sparsity. The fewer the coloring numbers in the Jacobian, the less the Jacobian computational cost will be. Besides, when computing the Jacobian in a distributed memory parallel environment, the parallel graph coloring algorithms are required because the Jacobian is distributed among processors. Currently, a popular parallel graph coloring algorithm has been used to color the Jacobian. However, this parallel graph coloring algorithm shows poor scalability in parallel. The coloring numbers will increase with the processors, resulting in poor Jacobian computational efficiency. In this paper, a more efficient parallel graph coloring method is proposed that aims to reduce the coloring numbers and improve Jacobian computation efficiency in parallel. The main feature of the new method is that the coloring numbers decrease with the increasing number of processors. A neutronics/thermal-hydraulic coupling problem arising from the simplified high-temperature gas coolant model is utilized to assess the performance of the newly proposed method. The results show that (1) the parallel coloring number is reduced significantly, (2) the Jacobian computed by the new method is completely correct and excellent parallel scalability is achieved, and (3) the parallel coloring Newton-Krylov method with explicit Jacobian is more efficient and more stable than the parallel JFNK due to a better preconditioner.

Hybrid Quadrupole Mass Filter - Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics.

Targeted mass spectrometry (MS) methods are powerful tools for selective and sensitive analysis of peptides identified by global discovery experiments. Selected reaction monitoring (SRM) is currently the most widely accepted MS method in the clinic, due to its reliability and analytical performance. However, due to limited throughput and the difficulty in setting up and analyzing large scale assays, SRM and parallel reaction monitoring (PRM) are typically used only for very refined assays of on the order of 100 targets or less. Here we introduce a new MS platform with a quadrupole mass filter, collision cell, linear ion trap architecture that has increased acquisition rates compared to the analogous hardware found in the Orbitrap™ Tribrid™ series instruments. The platform can target more analytes than existing SRM and PRM instruments - in the range of 5000 to 8000 peptides per hour. This capability for high multiplexing is enabled by acquisition rates of 70-100 Hz for peptide applications, and the incorporation of real-time chromatogram alignment that adjusts for retention time drift and enables narrow time scheduled acquisition windows. Finally, we describe a Skyline external software tool that implements the building of targeted methods based on data independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. We show that the platform delivers ~10x lower LOQs than traditional SRM analysis for a highly multiplex assay and also demonstrate how analytical figures of merit change while varying method duration with a constant number of analytes, or by keeping a constant time duration while varying the number of analytes.