High Performance Computing Architectures Research Articles

Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6

Abstract. Diabatic transport schemes with hybrid zeta coordinates, which follow isentropes in the stratosphere, are known to greatly improve Lagrangian transport calculations compared to the kinematic approach. However, some Lagrangian transport calculations with a diabatic approach, such as the Chemical Lagrangian Transport Model of the Stratosphere (CLaMS), are not well prepared to run on modern high-performance computing (HPC) architectures. Here, we implemented and evaluated a new diabatic transport scheme in the Massive-Parallel Trajectory Calculations (MPTRAC) model. While MPTRAC can be used either with shared-memory multiprocessing on CPUs or with GPUs to offload computationally intensive calculations, making it flexible for many HPC applications, it has been limited to kinematic trajectories in pressure coordinates. The extended modelling approach now enables the use of either kinematic or diabatic vertical velocities and the coupling of different MPTRAC modules based on pressure or hybrid zeta coordinates. This study focus on the accuracy of the implementation in comparison to the CLaMS model. The evaluation of the new transport scheme in MPTRAC shows that, after 90 d of forward calculations, distributions of air parcels in the upper troposphere and lower stratosphere (UTLS) are almost identical for MPTRAC and CLaMS. No significant bias between the two Lagrangian models was found. Furthermore, after 1 d, internal uncertainties (e.g. due to interpolation or the numerical integration method) in the Lagrangian transport calculations are at least 1 order of magnitude smaller than external uncertainties (e.g. from reanalysis selection or downsampling of ERA5). Differences between trajectories using either CLaMS or MPTRAC are on the order of the combined internal uncertainties within MPTRAC. Since the largest systematic differences are caused by the reanalysis and the vertical velocity (diabatic vs. kinematic), the results support the development efforts for trajectory codes that can access the full resolution of ERA5 in combination with diabatic vertical velocities. This work is part of a larger effort to adapt Lagrangian transport in state-of-the-art models such as CLaMS and MPTRAC to current and future HPC architectures and exascale applications.

Edge HPC Architectures for AI-Based Video Surveillance Applications

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.

TTCF4LAMMPS: A toolkit for simulation of the non-equilibrium behaviour of molecular fluids at experimentally accessible shear rates

We present TTCF4LAMMPS, a toolkit for performing non-equilibrium molecular dynamics (NEMD) simulations to study the fluid behaviour at low shear rates using the LAMMPS software. By combining direct NEMD simulations and the transient-time correlation function (TTCF) technique, we study the fluid response to shear rates spanning 15 orders of magnitude. We present two examples for simple monatomic systems: one consisting of a bulk liquid and another with a liquid layer confined between two solid walls. The small bulk system is suitable for testing on personal computers, while the larger confined system requires high-performance computing (HPC) resources. We demonstrate that the TTCF formalism can successfully detect the system response for arbitrarily weak external fields. We provide a brief mathematical explanation for this feature. Although we showcase the method for simple monatomic systems, TTCF can be readily extended to study more complex molecular fluids. Moreover, in addition to shear flows, the method can be extended to investigate elongational or mixed flows as well as thermal or electric fields. The high computational cost needed for the method is offset by the two following benefits: i) the cost is independent of the magnitude of the external field, and ii) the simulations can be made highly efficient on HPC architectures by exploiting the parallel design of the algorithm. We expect the toolkit to be useful for computational researchers striving to study the nonequilibrium behaviour of fluids under experimentally-accessible conditions. Program summaryProgram title: TTCF4LAMMPSCPC Library link to program files:https://doi.org/10.17632/hh2rkcxbrf.1Developer's repository link:https://github.com/edwardsmith999/TTCF4LAMMPSLicensing provisions: GNU General Public License 3Programming language: Python 3Nature of problem: Measuring the nonequilibrium behaviour of bulk and confined fluids under experimentally accessible strain rates in non-equilibrium molecular dynamics (NEMD) simulations.Solution method: Creating a Python-based code that utilises the transient-time correlation function method and the LAMMPS software to enable the bulk fluid properties (e.g. viscosity) and confined fluid interfacial properties (e.g. shear stress and slip velocity) to be computed at low shear rates with NEMD.

A parallel and performance portable implementation of a full-field crystal plasticity model

We have developed a parallel implementation of an Elasto-Viscoplastic Fast Fourier Transform-based (EVPFFT) micromechanical solver to enable computationally efficient crystal plasticity modeling for polycrystalline materials. Our primary focus lies in achieving performance portability, allowing a single EVPFFT implementation to run optimally on various homogeneous architectures, including multi-core Central Processing Units (CPUs), as well as on heterogeneous computer architectures comprising multi-core CPUs and Graphics Processing Units (GPUs) from different vendors. To accomplish this goal, we have leveraged MATAR, a C++ software library that simplifies the creation and utilization of multidimensional dense or sparse matrix and array data structures. These data structures are designed to be portable across diverse architectures through the use of Kokkos, a performance-portable library. Additionally, we have employed the Message Passing Interface (MPI) to efficiently distribute the computational workload among processors. The heFFTe (Highly Efficient FFT for Exascale) library is used to facilitate the performance portability of the fast Fourier transforms (FFTs) computation. The computational performance of EVPFFT is evaluated and presented in terms of parallel scalability and simulation runtime on different high-performance computing (HPC) architectures. The utility of the developed framework to efficiently simulate the micro-mechanical fields in polycrystalline microstructures in engineering applications is discussed. Program summaryProgram Title: EVPFFTCPC Library link to program files:https://doi.org/10.17632/2k8579fyyv.1Developer's repository link:https://github.com/lanl/FierroLicensing provisions: BSD 3-Clause LicenseProgramming language: C++External routines/libraries: MPI, Kokkos, MATAR, HeFFTe, HDF5Nature of problem: EVPFFT is a crystal plasticity code designed to compute micro-mechanical fields within a polycrystalline representative volume element (RVE) and predict the macroscale response of the RVE.Solution method: EVPFFT uses the periodic Green's function method in Fourier space to solve the field equations of static stress equilibrium in a periodic spatial domain.

Vast Parameter Space Exploration of the Virtual Brain: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics

Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states.

On the advantages of radial variation in assembly design for a nuclear reactor core

The geometric flexibility of additively manufactured metals and ceramics generates a vast, open design space requiring advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to inform the design space, to enhance evaluation of potential designs, and to generate optimized results more efficiently.This report documents efforts of the Transformational Challenge Reactor (TCR) program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, thus driving the evolution of the cooling channel shapes to minimize temperature peaking and material stress. The flexibility of additive manufacturing allows for the design of unique assemblies for each radial ring of the reactor core, which drives an increase in the selected performance metrics.One of the unique contributions made by this work is the capability to computationally demonstrate a 26.5% improvement in the selected reactor core performance metric by exploiting the freedom of axially variable assembly design for a nuclear reactor core. At the same pressure, the drop in the core decreased by a factor of 2.6, and the maximum temperature dropped by 10.7%. Additionally, the radially optimized design uses less materials, with a 15% decrease in total fuel and a 17.5% decrease in total moderator volume. These are the first quantitative results to allow for variation in assembly design in the core’s different radial rings.

An efficient heterogeneous parallel algorithm of the 3D MOC for multizone heterogeneous systems

Recent innovations in powerful high-performance computing (HPC) architecture have enabled high-fidelity whole-core neutron transport simulations in a reasonable amount of time. Among high-fidelity numerical methods, the one-step 3D Method of Characteristic (MOC) is becoming increasingly popular in the reactor design community due to its numerical accuracy, geometrical flexibility and high degrees of parallelism. In this work, we propose a sophisticated heterogeneous parallel algorithm of the 3D MOC based on a supercomputer prototype consisting of brand-new multizone heterogeneous processors called the MT-3000, which has a peak double precision performance of 11.6 TFLOPS when operating at 1.2 GHz. In the 3D MOC calculation procedure, the total run time is dominated by the so-called MOC transport sweep where large amounts of tracks traverse the mesh grids along different directions. We significantly improve the performance of the MOC transport sweep through a three-level parallel algorithm with multiple hiding latency techniques. We have also implemented heterogeneous level-1 basic linear algebra subprograms (BLAS) that's the second computational hot spot. The 3D C5G7 benchmark problem is employed to verify the correctness and efficiency of the heterogeneous parallel 3D MOC algorithm. Numerical results show that it achieves very high performance on the MT-3000-based supercomputer prototype. Specifically, the three-layer parallel algorithm has proven to be highly scalable for each layer, and the Quarter-MT-3000 performance of the MOC transport sweep has achieved 23.6% of the peak performance, which is close to the theoretical upper limit.

Does Academic Research Drive Industrial Innovation in Computer Architecture?—Analyzing Citations to Academic Papers in Patents

Examines the number of citations to papers published in International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), International Symposium on High- Performance Computer Architecture (HPCA), International Symposium on Computer Architecture (ISCA), and International Symposium on Microarchitecture (MICRO). Given that these four conferences are the most prestigious in computer architecture, they are an excellent proxy for academic research as they likely contain the most innovative ideas. As such, papers from these conferences could provide important building blocks for a company’s patented inventions; if so, these papers should be cited in the company’s patents.

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and a Future System Architecture

Big data has revolutionized science and technology leading to the transformation of our societies. High-performance computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally, HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realization of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high-performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in situ and transit data analysis. This paper provides an extensive review of cutting-edge research on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

P3 problem and Magnolia language: Specializing array computations for emerging architectures

The problem of producing portable high-performance computing (HPC) software that is cheap to develop and maintain is called the P3 (performance, portability, productivity) problem. Good solutions to the P3 problem have been achieved when the performance profiles of the target machines have been similar. The variety of HPC architectures is, however, large and can be expected to grow larger. Software for HPC therefore needs to be highly adaptable, and there is a pressing need to provide developers with tools to produce software that can target machines with vastly different profiles. Multi-dimensional array manipulation constitutes a core component of numerous numerical methods, such as finite difference solvers of Partial Differential Equations (PDEs). The efficiency of these computations is tightly connected to traversing and distributing array data in a hardware-friendly way. The Mathematics of Arrays (MoA) allows for formally reasoning about array computations and enables systematic transformations of array-based programs, e.g., to use data layouts that fit to a specific architecture. This paper presents a programming methodology aimed for tackling the P3 problem in domains that are well-explored using Magnolia, a language designed to embody generic programming. The Magnolia programmer can restrict the semantic properties of abstract generic types and operations by defining so-called axioms. Axioms can be used to produce tests for concrete implementations of specifications, for formal verification, or to perform semantics-preserving program transformations. We leverage Magnolia's semantic specification facilities to extend the Magnolia compiler with a term rewriting system. We implement MoA's transformation rules in Magnolia, and demonstrate through a case study on a finite difference solver of PDEs how our rewriting system allows exploring the space of possible optimizations.

HPC-enabling technologies for high-fidelity combustion simulations

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end.

NonInvasive Multigrid For SemiStructured Grids

Multigrid solvers for hierarchical hybrid grids (HHG) have been proposed to promote the efficient utilization of high performance computer architectures. These HHG meshes are constructed by uniformly refining a relatively coarse fully unstructured mesh. While HHG meshes provide some flexibility for unstructured applications, most multigrid calculations can be accomplished using efficient structured grid ideas and kernels. This paper focuses on generalizing the HHG idea so that it is applicable to a broader community of computational scientists, and so that it is easier for existing applications to leverage structured multigrid components. Specifically, we adapt the structured multigrid methodology to significantly more complex semi-structured meshes. Further, we illustrate how mature applications might adopt a semi-structured solver in a relatively non-invasive fashion. To do this, we propose a formal mathematical framework for describing the semi-structured solver. This formalism allows us to precisely define the associated multigrid method and to show its relationship to a more traditional multigrid solver. Additionally, the mathematical framework clarifies the associated software design and implementation. Numerical experiments highlight the relationship of the new solver with classical multigrid. We also demonstrate the generality and potential performance gains associated with this type of semi-structured multigrid.

Turbulence Modulation by Slender Fibers

In this paper, we numerically investigate the turbulence modulation produced by long flexible fibres in channel flow. The simulations are based on an Euler–Lagrangian approach, where fibres are modelled as chains of constrained, sub-Kolmogorov rods. A novel algorithm is deployed to make the resolution of dispersed systems of constraint equations, which represent the fibres, compatible with a state-of-the-art, Graphics Processing Units-accelerated flow-solver for direct numerical simulations in the two-way coupling regime on High Performance Computing architectures. Two-way coupling is accounted for using the Exact Regularized Point Particle method, which allows to calculate the disturbance generated by the fibers on the flow considering progressively refined grids, down to a quasi-viscous length-scale. The bending stiffness of the fibers is also modelled, while collisions are neglected. Results of fluid velocity statistics for friction Reynolds number of the flow Reτ=150 and fibers with Stokes number St = 0.01 (nearly tracers) and 10 (inertial) are presented, with special regard to turbulence modulation and its dependence on fiber inertia and volume fraction (equal to ϕ=2.12·10−5 and 2.12·10−4). The non-Newtonian stresses determined by the carried phase are also displayed, determined by long and slender fibers with fixed aspect ratio λtot=200, which extend up to the inertial range of the turbulent flow.

Responsibly Reckless Matrix Algorithms for HPC Scientific Applications

High-performance computing (HPC) achieved an astonishing three orders of magnitude performance improvement per decade for three decades, thanks to hardware technology scaling resulting in exponential improvement in the rate of floating point executions, though slowing in the most recent. Captured in the Top500 list, this hardware evolution cascaded through the software stack, triggering changes at all levels, including the redesign of numerical linear algebra libraries. HPC simulations on massively parallel systems are often driven by matrix computations, whose rate of execution depends on their floating point precision. Referred to by Jack Dongarra, the 2021 ACM A.M. Turing Award Laureate, as “responsibly reckless” matrix algorithms, we highlight the implications of mixed-precision (MP) computations for HPC applications. Introduced 75 years ago, long before the advent of HPC architectures, MP numerical methods turn out to be paramount for increasing the throughput of traditional and artificial intelligence (AI) workloads beyond riding the wave of the hardware alone. Reducing precision comes at the price of trading away some accuracy for performance (reckless behavior) but in noncritical segments of the workflow (responsible behavior) so that accuracy requirements of the application can still be satisfied. They offer a valuable performance/accuracy knob and, just as they are in AI, they are now indispensable in the pursuit of knowledge and discovery in simulations. In particular, we illustrate the MP impact on three representative HPC applications related to seismic imaging, climate/environment geospatial predictions, and computational astronomy.

Performance Evaluation of Massively Parallel Systems Using SPEC OMP Suite

Performance analysis plays an essential role in achieving a scalable performance of applications on massively parallel supercomputers equipped with thousands of processors. This paper is an empirical investigation to study, in depth, the performance of two of the most common High-Performance Computing architectures in the world. IBM has developed three generations of Blue Gene supercomputers—Blue Gene/L, P, and Q—that use, at a large scale, low-power processors to achieve high performance. Better CPU core efficiency has been empowered by a higher level of integration to gain more parallelism per processing element. On the other hand, the Intel Xeon Phi coprocessor armed with 61 on-chip x86 cores, provides high theoretical peak performance, as well as software development flexibility with existing high-level programming tools. We present an extensive evaluation study of the performance peaks and scalability of these two modern architectures using SPEC OMP benchmarks.

Issue Information

Concurrency and Computation: Practice and ExperienceVolume 34, Issue 12 e6397 ISSUE INFORMATIONFree Access Issue Information First published: 22 April 2022 https://doi.org/10.1002/cpe.6397AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume34, Issue12Special Issue: High Performance Big Data and Intelligent Systems (HPBD&IS2019). networking, high-performance computer architecture, and parallel and distributed data storage technologies (NAS2019)30 May 2022e6397 RelatedInformation

FgSpMSpV: A Fine-grained Parallel SpMSpV Framework on HPC Platforms

Sparse matrix-sparse vector (SpMSpV) multiplication is one of the fundamental and important operations in many high-performance scientific and engineering applications. The inherent irregularity and poor data locality lead to two main challenges to scaling SpMSpV over high-performance computing (HPC) systems: (i) a large amount of redundant data limits the utilization of bandwidth and parallel resources; (ii) the irregular access pattern limits the exploitation of computing resources. This paper proposes a fine-grained parallel SpMSpV ( fgSpMSpV ) framework on Sunway TaihuLight supercomputer to alleviate the challenges for large-scale real-world applications. First, fgSpMSpV adopts an MPI \( + \) OpenMP \( +X \) parallelization model to exploit the multi-stage and hybrid parallelism of heterogeneous HPC architectures and accelerate both pre-/post-processing and main SpMSpV computation. Second, fgSpMSpV utilizes an adaptive parallel execution to reduce the pre-processing, adapt to the parallelism and memory hierarchy of the Sunway system, while still tame redundant and random memory accesses in SpMSpV, including a set of techniques like the fine-grained partitioner, re-collection method, and Compressed Sparse Column Vector (CSCV) matrix format. Third, fgSpMSpV uses several optimization techniques to further utilize the computing resources. fgSpMSpV on the Sunway TaihuLight gains a noticeable performance improvement from the key optimization techniques with various sparsity of the input. Additionally, fgSpMSpV is implemented on an NVIDIA Tesal P100 GPU and applied to the breath-first-search (BFS) application. fgSpMSpV on a P100 GPU obtains the speedup of up to \( 134.38\times \) over the state-of-the-art SpMSpV algorithms, and the BFS application using fgSpMSpV achieves the speedup of up to \( 21.68\times \) over the state-of-the-arts.

Full Core Pin-Level VVER-440 Simulation of a Rod Drop Experiment with the GPU-Based Monte Carlo Code GUARDYAN

Targeting ultimate fidelity reactor physics calculations the Dynamic Monte Carlo (DMC) method simulates reactor transients without resorting to static or quasistatic approximations. Due to the capability to harness the computing power of Graphics Processing Units, the GUARDYAN (GpU Assisted Reactor DYnamic ANalysis) code has been recently upscaled to perform pin-by-pin simulations of power plant scale systems as demonstrated in this paper. A recent rod drop experiment at a VVER-440/213 (vodo-vodyanoi enyergeticheskiy reaktor) type power plant at Paks NPP, Hungary, was considered and signals of ex-core detectors placed at three different positions were simulated successfully by GUARDYAN taking realistic fuel loading, including burn-up data into account. Results were also compared to the time-dependent Paks NPP in-house nodal diffusion code VERETINA (VERONA: VVER Online Analysis and RETINA: Reactor Thermo-hydraulics Interactive). Analysis is given of the temporal and spatial variance distribution of GUARDYAN fuel pin node-wise power estimates. We can conclude that full core, pin-wise DMC power plant simulations using realistic isotope concentrations are feasible in reasonable computing times down to 1–2% error of ex-core detector signals using current GPU (Graphics Processing Unit) High Performance Computing architectures, thereby demonstrating a technological breakthrough.

A new adaptive sampling algorithm for big data classification

The exponential growth of the quantity of data that circulates on the web led to the emergence of the big data phenomenon. This fact is a natural consequence of the proliferation of social media, mobile devices, the abundance of free online storage, and new technologies like the internet of things. Subsequently, big data has created several challenges to the computer science community, among which the large size of data is the most challenging. Traditional machine learning algorithms used mostly for insight extraction find themselves inadequate, even on high-performance computer architectures. For instance, big data analytics algorithms can overcome the size issue by either: (1) adapting the existing machine learning techniques to the scale of the big data; or, (2) by sampling big datasets, choosing randomly much smaller subsets of the data population, to meet what current algorithms can handle. In the present work, we aim to proceed through the second alternative to address the size challenge in the big data context. We propose intelligent sampling techniques based on Scalable Simple Random Sampling (ScaSRS) and Subsampled Double Bootstrap (SDB). Test results carried out on public generic datasets show that our proposal is able to address the size dimension efficiently. The proposed algorithms were evaluative on a classification task where the obtained results provided significant improvement compared to the state-of-the-art.

Grand-potential-based phase-field model of dissolution/precipitation: Lattice Boltzmann simulations of counter term effect on porous medium

Most of the lattice Boltzmann methods simulate an approximation of the sharp interface problem of dissolution and precipitation. In such studies the curvature-driven motion of interface is neglected in the Gibbs–Thomson condition. In order to simulate those phenomena with or without curvature-driven motion, we propose a phase-field model which is derived from a thermodynamic functional of grand-potential. Compared to the free energy, the main advantage of the grand-potential is to provide a theoretical framework which is consistent with the equilibrium properties such as the equality of chemical potentials. The model is composed of one equation for the phase-field ϕ coupled with one equation for the chemical potential μ. In the phase-field method, the curvature-driven motion is always contained in the phase-field equation. For canceling it, a counter term must be added in the ϕ-equation. For reason of mass conservation, the μ-equation is written with a mixed formulation which involves the composition c and the chemical potential. The closure relationship between c and μ is derived by assuming quadratic free energies for the bulk phases. The anti-trapping current is also considered in the composition equation for simulations with null solid diffusion. The lattice Boltzmann schemes are implemented in LBM_saclay, a numerical code running on various High Performance Computing architectures. Validations are carried out with analytical solutions representative of dissolution and precipitation. Simulations with or without counter term are compared on the shape of porous medium characterized by microtomography. The computations have run on a single GPU-V100.