High Parallel Efficiency Research Articles

Gas-Kinetic Unified Algorithm for Aerodynamics Covering Various Flow Regimes by Computable Modeling of Boltzmann Equation

The gas-kinetic unified algorithm (GKUA), to solve the modeling of the Boltzmann equation, has been developed to study the aerothermodynamics problems with the effects of wall activation energy covering various flow regimes. The unified velocity distribution function equation could be accordingly presented on the basis of the Boltzmann-Shakhov model. To remove the dependence of the distribution function on velocity space, the conservational discrete velocity ordinate method has been developed for hypervelocity flows. The gas-kinetic finite difference scheme is constructed to directly solve the discrete velocity distribution functions by the operator splitting technique. The discrete velocity numerical integration method with the Gauss-type weight function has been developed to evaluate the macroscopic flow variables. Specially, to model the real physical process between gas molecules and the surface, the Maxwell-type gas-surface interaction model has been presented by the MD (molecular dynamic) simulation to obtain the energy adaptability coefficient. The multi-processing domain decomposition strategy and parallel implementation of high parallel efficiency and expansibility designed for the gas-kinetic numerical method is presented with good load balance and data communication efficiency. To validate the accuracy and feasibility of the present algorithm, the supersonic flows past two-dimensional circular cylinder are simulated covering various flow regimes. The results are in good agreement with the related theoretical, DSMC (Direct Simulation Monte Carlo), N-S (Navier-Stokes), and experimental data. The hypersonic reentry flows with the effects of wall activation energy around the Tianzhou-5 cargo spacecraft are simulated by the present GKUA and the massive parallel strategy. It has been confirmed that the present algorithm from the gas-kinetic point of view probably provides a promising approach to resolve the hypersonic aerothermodynamic problems with the complete spectrum of flow regimes during the re-entry and disintegration of the large-scale spacecraft.

High efficiency deep image compression via channel-wise scale adaptive latent representation learning

Recent learning based neural image compression methods have achieved impressive rate–distortion (RD) performance via the sophisticated context entropy model, which performs well in capturing the spatial correlations of latent features. However, due to the dependency on the adjacent or distant decoded features, existing methods require an inefficient serial processing structure, which significantly limits its practicability. Instead of pursuing computationally expensive entropy estimation, we propose to reduce the spatial redundancy via the channel-wise scale adaptive latent representation learning, whose entropy coding is spatially context-free and parallelizable. Specifically, the proposed encoder adaptively determines the scale of the latent features via a learnable binary mask, which is optimized with the RD cost. In this way, lower-scale latent representation will be allocated to the channels with higher spatial redundancy, which consumes fewer bits and vice versa. The downscaled latent features could be well recovered with a lightweight inter-channel upconversion module in the decoder. To compensate for the entropy estimation performance degradation, we further develop an inter-scale hyperprior entropy model, which supports the high efficiency parallel encoding/decoding within each scale of the latent features. Extensive experiments are conducted to illustrate the efficacy of the proposed method. Our method achieves bitrate savings of 18.23%, 19.36%, and 27.04% over HEVC Intra, along with decoding speeds that are 46 times, 48 times, and 51 times faster than the baseline method on the Kodak, Tecnick, and CLIC datasets, respectively.

Custom RISC-V architecture incorporating memristive in-memory computing

Due to the rise in data-intensive applications, the von Neumann bottleneck is increasingly restricting modern computer architectures, resulting to latency and energy consumption. Addressing this challenge necessitates a CMOS-compatible solution with high energy efficiency and significant parallelism. Utilizing resistive switching components within a 1T1R crossbar array and the application of Stanford RRAM model, this paper suggests an original method for in-memory computing. Moreover, this work shows a new way to advance the popular RISC-V architecture by including memristive crossbar array. It does this by adding a custom instruction set, special hardware blocks, and the Scouting Logic Scheme. These modifications serve both as a comprehensive testbed for the memory system and a proof of concept for the future integration of memristors in computing architectures. The proposed design undergoes extensive testing and power analysis to validate its functionality and performance under various conditions. The results demonstrate significant improvements in computational efficiency and energy savings, highlighting the potential of memristor-based in-memory computing systems to overcome current architectural limitations.

Efficient Shift-and-Invert Preconditioning for Multi-GPU Accelerated Density Functional Calculations.

To accelerate the iterative diagonalization of electronic structure calculations, we propose a new inexact shift-and-invert (ISI) preconditioning method. The key idea is to improve shift values in the ISI preconditioning to be closer to the exact eigenvalues, leading to a significant boost in the convergence speed of the iterative diagonalization. Furthermore, we adopted a preconditioned conjugate gradient solver to rapidly evaluate an inversion process. Finally, we accelerated overall processes, including the proposed modification, with state-of-the-art graphical processing units (GPUs) and assessed its parallel efficiency with real-space density functional calculations of 1D, 2D, and 3D periodic systems. Our method attains both fast diagonalization convergence and high multi-GPU parallel efficiency. This is evident from the fact that single-point density functional calculations for hundreds of atom systems can be done in approximately 10 s using 8 GPUs. The proposed method can be generally applied to any electronic structure calculation methods involving large-scale diagonalizations.

A finite volume parallel adaptive mesh refinement method for solid-liquid phase change

We present a finite volume adaptive mesh refinement method for solid-liquid phase change problems with convection. The refinement criterion consisted of three different error estimators for the solid-liquid interface, the flow field, and the temperature field respectively. For the solid-liquid interface, the cells undergoing phase change were refined based on the maximum difference in the liquid fraction over the cell faces. For the flow field and the temperature field, an error indicator was used based on the cell residual method. To maintain a high parallelization efficiency, a dynamic load balancing procedure was used. The adaptive mesh refinement strategy was verified through three different test cases, these are the gallium melting in both 2D and 3D cavities, and the molten salt reactor freeze valve. For all three cases, very good agreement was obtained between the adaptive mesh results and the reference solutions. In addition, more accurate results were obtained with the adaptive meshes compared to static meshes with a similar amount of mesh cells. This illustrated the potential of the current approach for developing computationally efficient numerical methods for solid-liquid phase change problems.

A Novel Chaos-Based Encryption Technique with Parallel Processing Using CUDA for Mobile Powerful GPU Control Center

Chaotic systems possess unique properties that can be leveraged for cybersecurity due to their complex and unpredictable nature, making it challenging for systems to interpret them. When combined with CUDA, chaotic systems benefit from high-efficiency parallel processing capabilities, allowing for the rapid and secure handling of large data sets. In this study, a CUDA-supported chaos-based parallel processing encryption mechanism for mobile control centers is developed. The powerful GPU of the control center enables fast encryption and decryption of image data from multiple connected devices. The Logistic Map is used for random number generation, and XOR operations are applied to encrypt the R, G, B, and Gray Scale channels of images. Initially, an analysis of the numbers generated from the Logistic Map is conducted, followed by a detailed explanation of the encryption technique. The technique is then applied to image data, and image analyses are performed. Finally, the performance of the encryption technique is compared with other studies, and encryption speeds are examined. Results indicate that the new encryption technique provides significantly fast encryption and security levels comparable to other studies. The key discovery is that the mechanism is well-suited for parallel processing, allowing for rapid image encryption using the proposed method. For encrypting large IoT files, random number generation is performed first, followed by statistical tests, and then encryption using the developed algorithm. Security analyses are conducted, and the performance of the mechanism is compared with other studies. Results from image analysis and encryption performance demonstrate that the developed mechanism can be effectively used with high security for IoT applications.

Asymptotic preserving semi-Lagrangian discontinuous Galerkin methods for multiscale kinetic transport equations

We propose a new family of asymptotic preserving (AP) discontinuous Galerkin (DG) schemes for kinetic transport equations under a diffusive scaling. They are built upon a characteristics-based model reformulation proposed in Zhang et al. (2023) [27]. The reformulated model comprises a convection-diffusion-like equation for the macroscopic density and another evolution equation for the distribution function. We employ the equations in a weak form to enjoy the advantages of a DG method. We also leverage a semi-Lagrangian (SL) approach to achieve higher efficiency than a fully implicit scheme without loss of unconditional stability. A damping technique is introduced to control spurious numerical oscillations for high-order DG methods. We establish formal AP and Fourier stability analyses for fully discrete schemes in a linear scenario. Numerical experiments, covering one-dimensional to three-dimensional in space problems, confirm the accuracy, AP property, uniformly unconditional stability, and high parallel efficiency of the proposed methods.

TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows

Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.

High-efficiency computation for electromagnetic forming process: An explicit-implicit GPU approach

Low computational efficiency is one of the crucial challenges in electromagnetic forming process simulation. However, the huge gap in switching from the electromagnetic field to mechanics hinders the development of high-efficiency parallel coupling. A graphics processing unit (GPU)-based explicit-implicit coupling method is proposed to address the high-computational cost in simulations of the electromagnetic forming process, in which the large deformation (explicit) and electromagnetic field (implicit) are intimately coupled. The electromagnetic-mechanical coupling is achieved by constructing five parallel algorithms, which include the parallel operations of vector-vector, matrix-matrix, matrix-vector, vector assembly, and matrix-free assembly parallel preconditioned conjugate gradient algorithm. The high-efficiency GPU parallel computing is realized through the special data storage structure and parallel strategies. The explicit-implicit GPU method is validated against the experimental results, and several examples are presented for simulations of the electromagnetic forming process to illustrate both the accuracy and high-efficiency parallel acceleration performance of the present program.

Large‐scale homo‐ and heterogeneous parallel paradigm design based on CFD application PHengLEI

SummaryThe development of computational fluid dynamics (CFD) highly depends on high‐performance computers. Computer hardware has evolved rapidly, yet scalable CFD parallel software remains scarce. In this article, we design a highly scalable CFD parallel paradigm for both homogeneous and heterogeneous supercomputers. The paradigm achieves the separation of communication and computation and automatically adapts to various solvers and hardware environments, thus reducing programming difficulties and increasing automatic parallelization. Meanwhile, the number of communications is greatly reduced and the scalability of the program is improved through implementing centralized communication and two‐level partitioning techniques. Complex flow problems for real aircraft were then computed on different hardware platforms with a grid size of ten billion. The homogeneous computer hardware includes Intel Xeon Gold 6258R and Phytium 2000+ processors, and the heterogeneous computer platforms include NVIDIA Tesla V100 and SW26010 processors. High parallel efficiency was obtained on all computer platforms, verifying that the paradigm has good automatic parallelization, scalability, and stability. The paradigm in this article has an important reference value for CFD massively parallel computing and can promote the development and application of CFD technology.

Virtual lattice method for efficient Monte Carlo transport simulation of dispersion nuclear fuels

The randomly dispersed Tristructural-isotropic (TRISO) particle fuel is an attractive nuclear fuel type widely used in advanced reactor designs. However, the massive randomly-distributed fuel elements and the prohibitive computational costs pose challenges for high-fidelity modeling and simulation of dispersion fuels. To address this, we present a virtual lattice optimization method for accelerating the Monte Carlo particle transport simulation of nuclear systems with dispersion fuels. The new method involves using a regular overlaid mesh on the dispersed fuel region to improve geometry processing in Monte Carlo simulations. We describe the optimization methodology in our paper and construct a theoretical performance model to determine the optimal lattice pitch size that minimizes calculation cost. We then develop and implement the algorithm of the virtual method in the open-source Monte Carlo code OpenMC. To demonstrate the efficacy of the new algorithm, high-fidelity HTR-PM full core models based on the Shidao-Bay nuclear power plant are constructed, including an explicit representation of up to 420,000 fuel pebbles. Criticality and depletion simulations are performed on large-scale models. Results show that the k-effective derived by the optimized code agrees well with both the experiment and the original code. Further performance comparison shows that the virtual lattice method exhibits lower time consumption, higher parallel efficiency, and lower storage requirement in comparison to the conventional physical lattice method. The study establishes the effectiveness of the proposed methodology in facilitating efficient and accurate high-fidelity simulations of large-scale dispersion fuel models.

An improved dynamical Poisson equation solver for self-gravity

ABSTRACT Since self-gravity is crucial in the structure formation of the Universe, many hydrodynamics simulations with the effect of self-gravity have been conducted. The multigrid method is widely used as a solver for the Poisson equation of the self-gravity; however, the parallelization efficiency of the multigrid method becomes worse when we use a massively parallel computer, and it becomes inefficient with more than 104 cores, even for highly tuned codes. To perform large-scale parallel simulations (>104 cores), developing a new gravity solver with good parallelization efficiency is beneficial. In this article, we develop a new self-gravity solver using the telegraph equation with a damping coefficient, κ. Parallelization is much easier than the case of the elliptic Poisson equation since the telegraph equation is a hyperbolic partial differential equation. We analyse convergence tests of our telegraph equations solver and determine that the best non-dimensional damping coefficient of the telegraph equations is $\tilde{\kappa } \simeq 2.5$. We also show that our method can maintain high parallelization efficiency even for massively parallel computations due to the hyperbolic nature of the telegraphic equation by weak-scaling tests. If the time-step of the calculation is determined by heating/cooling or chemical reactions, rather than the Courant–Friedrichs–Lewy (CFL) condition, our method may provide the method for calculating self-gravity faster than other previously known methods such as the fast Fourier transform and multigrid iteration solvers because gravitational phase velocity determined by the CFL condition using these time-scales is much larger than the fluid velocity plus sound speed.

GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)

Abstract. With semiconductor technology gradually approaching its physical and thermal limits, graphics processing units (GPUs) are becoming an attractive solution for many scientific applications due to their high performance. This paper presents an application of GPU accelerators in an air quality model. We demonstrate an approach that runs a piecewise parabolic method (PPM) solver of horizontal advection (HADVPPM) for the air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU–GPU communication frequency, increasing the data size computation on the GPU, optimizing the GPU memory access, and using thread and block indices to improve the overall computing performance of the CAMx model coupled with GPU-HADVPPM (named the CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with GPU-HADVPPM on the GPU clusters with a message passing interface (MPI) and CUDA. The offline experimental results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and an NVIDIA Tesla V100 GPU can achieve up to a 845.4× and 1113.6× acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model results in a 29.0× and 128.4× improvement in computational efficiency by using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3× and 18.8× speedup on an NVIDIA Tesla K40m GPU and NVIDIA Tesla V100 GPU, respectively. The multi-GPU acceleration algorithm enables a 4.5× speedup with eight CPU cores and eight GPU accelerators on a V100 cluster.

Modulus-based synchronous multisplitting iteration methods without auxiliary variable for solving vertical linear complementarity problems

In this work, by applying the synchronous multisplitting technique to the non-auxiliary variable modulus equation of the vertical linear complementarity problems, a new parallel method is constructed, which can generalize the existing modulus-based matrix splitting iteration method. The convergence conditions of the proposed method are discussed in the cases of more than two system matrices, and the existing results are improved. Numerical examples under OpenACC framework show that the proposed method can solve the large sparse vertical linear complementarity problems efficiently with high parallel efficiency.

High-efficiency photon-number-resolving detector for improving heralded single-photon sources

Heralded single-photon sources (HSPS) intrinsically suffer from multiphoton emission, leading to a trade-off between the source’s single-photon quality and the heralding rate. A solution to this problem is to use photon-number-resolving (PNR) detectors to filter out the heralding events where more than one photon pair is created. Here, we demonstrate an improvement of a HSPS by heralding photons using a high-efficiency parallel superconducting nanowire single-photon detector (P-SNSPD) with PNR power. Specifically, we show a reduction in the of the heralded single photon by for a fixed pump power, or alternatively, an increase in the heralding rate by a factor of for a fixed . We also demonstrate that such a PNR device can reveal thermal photon-number statistics of unheralded photons, which is enabled by our ability to construct its full input–output response function. These results are possible thanks to our P-SNSPD architecture that ensures non-latching operation with no electrical crosstalk, which are essential conditions necessary to obtain the correct photon-number statistics and also faster recovery times, therefore enabling fast heralding rates. These results show that our efficient PNR P-SNSPD architecture can significantly improve the performance of HSPSs and can precisely characterize them, making these detectors a useful tool for a wide range of optical quantum information protocols.

A novel high efficiency Reactive Ion Figuring technique based on artificial microporous mask

Reactive Ion Figuring (RIF) is a promising technical solution for fabrication of meter-scale aperture optical substrates due to its high efficiency parallel processing and low surface damage feature. However, the existing RIF technique needs multiple cycles of photolithography and etching process which is time-consuming and expensive. An artificial microporous film was designed and fabricated as a mask to perform high efficient RIF process. The location of the artificial microporous is distributed gradually from high-density to the low-density state along the radius according to the law of power aberration, and then fluid simulation was carried out. A designed power aberration was superimposed on a 100 mm aperture quartz substrate in a single cycle of RIF process, proving that the artificial microporous mask is capable of tailoring etching rate distribution. The result shows that the power aberration difference between the designed figure and real fabricating one is around 6 nm, and the reasons behind this were analyzed and discussed. This work demonstrates feasibility of high efficiency RIF based on artificial microporous mask and laid the foundation for future arbitrary surface figuring processes.

Massively parallel numerical simulation of 3D shock wave propagation based on JASMIN framework

An efficient massively parallel computing program for shock wave propagation named Shock3d is developed for shock wave damage mechanism in complex scenes. With the finite volume method, the program is built upon JASMIN (J parallel Adaptive Structured Mesh applications INfrastructure) and employs a component-based hierarchical organization structure. Using the patch-based core algorithm, Shock3d enables high parallel efficiency and strong parallel scalability with a parallel domain decomposition method and an image domain-based communication mode. It not only considers gravity, real gas effects, and boundaries in complex scenes, but also supports dynamic load balancing (DLB) and three-dimensional structured meshes and allows the parallel solution of hundreds of millions of mesh problems. Typical examples were used to verify the validity, parallelism, and high-resolution simulation results of the program developed. In the scalability test, where the propagation of shock waves through hundreds of millions of mesh elements was simulated on 300,000 CPU cores, the program delivered parallel efficiency of 60.84%.

A novel parallel frequent itemset mining algorithm for automatic enterprise

ABSTRACT Heterogeneity, volume and real-time velocity of manufacturing data affect the business efficiency within the process for analyzing data in Robotic Process Automation (RPA). A novel parallel frequent itemset mining algorithm based on MapReduce (PMRARIM-IEG) is designed to improve the business efficiency. The algorithm is designed to address issues such as the CanTree's excessive space usage, the inability to dynamically set the support threshold, and the time-consuming data transmission during the Map and Reduce phases. Experiments show that the proposed algorithm has lower memory usage and higher parallel efficiency than the traditional parallel frequent itemset mining algorithm.

Multi-level parallelization of quantum-chemical calculations.

Strategies for multiple-level parallelizations of quantum-mechanical calculations are discussed, with an emphasis on using groups of workers for performing parallel tasks. These parallel programming models can be used for a variety abinitio quantum chemistry approaches, including the fragment molecular orbital method and replica-exchange molecular dynamics. Strategies for efficient load balancing on problems of increasing granularity are introduced and discussed. A four-level parallelization is developed based on a multi-level hierarchical grouping, and a high parallel efficiency is achieved on the Theta supercomputer using 131072 OpenMP threads.

Complex moment-based eigensolver coupled with two Krylov subspaces

Complex moment-based eigensolvers have been well studied for solving interior eigenvalue problems because of their high parallel efficiency. Recently, as a time-efficient complex moment-based eigensolver, the block SS–CAA method was proposed that is based on the communication-avoiding Arnoldi procedure regarding the high-order complex moments. These complex moment-based eigensolvers, including the block SS–CAA method, usually use with a subspace iteration technique for improving the accuracy. In this paper, we aim to improve the convergence behavior of the block SS–CAA method using a block Arnoldi iteration instead of the subspace iteration. The proposed method is based on a special subspace coupled with two Krylov subspaces: one is for the high-order complex moments, and the other is for the iteration technique. Numerical experiments indicate that the proposed method has a higher convergence rate than the block SS–CAA method and the FEAST eigensolver.