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Finding PageRank vector is of great scientific and practical interest due to its applicability to modern search engines. Despite the fact that this problem is reduced to finding the eigenvector of the stochastic matrix $P$, the need for new algorithms is justified by a large size of the input data. To achieve no more than linear execution time, various randomized methods have been proposed, returning the expected result only with some probability close enough to one. We will consider two of them by reducing the problem of calculating the PageRank vector to the problem of finding equilibrium in an antagonistic matrix game, which is then solved using the Grigoriadis – Khachiyan algorithm. This implementation works effectively under the assumption of sparsity of the input matrix. As far as we know, there are no successful implementations of neither the Grigoriadis – Khachiyan algorithm nor its application to the task of calculating the PageRank vector. The purpose of this paper is to fill this gap. The article describes an algorithm giving pseudocode and some details of the implementation. In addition, it discusses another randomized method of calculating the PageRank vector, namely, Markov chain Monte Carlo (MCMC), in order to compare the results of these algorithms on matrices with different values of the spectral gap. The latter is of particular interest, since the magnitude of the spectral gap strongly affects the convergence rate of MCMC and does not affect the other two approaches at all. The comparison was carried out on two types of generated graphs: chains and $d$-dimensional cubes. The experiments, as predicted by the theory, demonstrated the effectiveness of the Grigoriadis – Khachiyan algorithm in comparison with MCMC for sparse graphs with a small spectral gap value. The written code is publicly available, so everyone can reproduce the results themselves or use this implementation for their own needs. The work has a purely practical orientation, no theoretical results were obtained.

Although the era of exponential performance growth in computer chips has ended, processor core numbers have reached 16 or more even in general-purpose desktop CPUs. As DRAM throughput is unable to keep pace with this computing power growth, CPU designers need to find ways of lowering memory traffic per instruction. The straightforward way to do this is to reduce the miss rate of the last-level cache. Assuming “non-inclusive cache, inclusive directory” (NCID) scheme already implemented, three ways of reducing the cache miss rate further were studied.

The first is to achieve more uniform usage of cache banks and sets by employing hash-based interleaving and indexing. In the experiments in SPEC CPU2017 refrate tests, even the simplest XOR-based hash functions demonstrated a performance increase of 3.2%, 9.1%, and 8.2% for CPU configurations with 16, 32, and 64 cores and last-level cache banks, comparable to the results of more complex matrix-, division- and CRC-based functions.

The second optimisation is aimed at reducing replication at different cache levels by means of automatically switching to the exclusive scheme when it appears optimal. A known scheme of this type, FLEXclusion, was modified for use in NCID caches and showed an average performance gain of 3.8%, 5.4 %, and 7.9% for 16-, 32-, and 64-core configurations.

The third optimisation is to increase the effective cache capacity using compression. The compression rate of the inexpensive and fast BDI*-HL (Base-Delta-Immediate Modified, Half-Line) algorithm, designed for NCID, was measured, and the respective increase in cache capacity yielded roughly 1% of the average performance increase.

All three optimisations can be combined and demonstrated a performance gain of 7.7%, 16% and 19% for CPU configurations with 16, 32, and 64 cores and banks, respectively.

A promising method of increasing precipitation in arid climates is the method of creating a vertical high-temperature jet seeded by hygroscopic aerosol. Such an installation makes it possible to create artificial clouds with the possibility of precipitation formation in a cloudless atmosphere, unlike traditional methods of artificial precipitation enhancement, which provide for increasing the efficiency of precipitation formation only in natural clouds by seeding them with nuclei of crystallization and condensation. To increase the power of the jet, calcium chloride, carbamide, salt in the form of a coarse aerosol, as well as NaCl/TiO₂ core/shell novel nanopowder, which is capable of condensing much more water vapor than the listed types of aerosols, are added. Dispersed inclusions in the jet are also centers of crystallization and condensation in the created cloud to increase the possibility of precipitation. To simulate convective flows in the atmosphere, a mathematical model of FlowVision large-scale atmospheric flows is used, the solution of the equations of motion, energy and mass transfer is carried out in relative variables. The statement of the problem is divided into two parts: the initial jet model and the FlowVision large-scale atmospheric model. The lower region, where the initial high-speed jet flows, is calculated using a compressible formulation with the solution of the energy equation with respect to the total enthalpy. This division of the problem into two separate subdomains is necessary in order to correctly carry out the numerical calculation of the initial turbulent jet at high velocity (M > 0.3). The main mathematical dependencies of the model are given. Numerical experiments were carried out using the presented model, experimental data from field tests of the installation for creating artificial clouds were taken for the initial data. A good agreement with the experiment is obtained: in 55% of the calculations carried out, the value of the vertical velocity at a height of 400 m (more than 2 m/s) and the height of the jet rise (more than 600 m) is within an deviation of 30% of the experimental characteristics, and in 30% of the calculations it is completely consistent with the experiment. The results of numerical simulation allow evaluating the possibility of using the high-speed jet method to stimulate artificial updrafts and to create precipitation. The calculations were carried out using FlowVision CFD software on SUSU Tornado supercomputer.

The influence of the process of initiating a rapid local heat release near surface streamlined by supersonic gas (air) flow on the separation region that occurs during a fast turn of the flow was investigated. This surface consists of two planes that form obtuse angle when crossing, so that when flowing around the formed surface, the supersonic gas flow turns by a positive angle, which forms an oblique shock wave that interacts with the boundary layer and causes flow separation. Rapid local heating of the gas above the streamlined surface simulates long spark discharge of submicrosecond duration that crosses the flow. The gas heated in the discharge zone interacts with the separation region. The flow can be considered two-dimensional, so the numerical simulation is carried out in a two-dimensional formulation. Numerical simulation was carried out for laminar regime of flow using the sonicFoam solver of the OpenFOAM software package.

The paper describes a method for constructing a two-dimensional computational grid using hexagonal cells. A study of grid convergence has been carried out. A technique is given for setting the initial profiles of the flow parameters at the entrance to the computational domain, which makes it possible to reduce the computation time by reducing the number of computational cells. A method for non-stationary simulation of the process of rapid local heating of a gas is described, which consists in superimposing additional fields of increased pressure and temperature values calculated from the amount of energy deposited in oncoming supersonic gas flow on the corresponding fields of values obtained in the stationary case. The parameters of the energy input into the flow corresponding to the parameters of the electric discharge process, as well as the parameters of the oncoming flow, are close to the experimental values.

During analyzing numerical simulation data it was found that the initiation of rapid local heating leads to the appearance of a gas-dynamic perturbation (a quasi-cylindrical shock wave and an unsteady swirling flow), which, when interacting with the separation region, leads to a displacement of the separation point downstream. The paper considers the question of the influence of the energy spent on local heating of the gas, and of the position on the streamlined surface of the place of heating relative to the separation point, on the value of its maximum displacement.

This paper demonstrates the advantages of using artificial intelligence algorithms for the design of experiment theory, which makes possible to improve the accuracy of parameter identification for an elastostatic robot model. Design of experiment for a robot consists of the optimal configuration-external force pairs for the identification algorithms and can be described by several main stages. At the first stage, an elastostatic model of the robot is created, taking into account all possible mechanical compliances. The second stage selects the objective function, which can be represented by both classical optimality criteria and criteria defined by the desired application of the robot. At the third stage the optimal measurement configurations are found using numerical optimization. The fourth stage measures the position of the robot body in the obtained configurations under the influence of an external force. At the last, fifth stage, the elastostatic parameters of the manipulator are identified based on the measured data.

The objective function required to finding the optimal configurations for industrial robot calibration is constrained by mechanical limits both on the part of the possible angles of rotation of the robot’s joints and on the part of the possible applied forces. The solution of this multidimensional and constrained problem is not simple, therefore it is proposed to use approaches based on artificial intelligence. To find the minimum of the objective function, the following methods, also sometimes called heuristics, were used: genetic algorithms, particle swarm optimization, simulated annealing algorithm, etc. The obtained results were analyzed in terms of the time required to obtain the configurations, the optimal value, as well as the final accuracy after applying the calibration. The comparison showed the advantages of the considered optimization techniques based on artificial intelligence over the classical methods of finding the optimal value. The results of this work allow us to reduce the time spent on calibration and increase the positioning accuracy of the robot’s end-effector after calibration for contact operations with high loads, such as machining and incremental forming.

A model, describing the influence of external factors on temporal evolution of phytoplankton distribution in a horizontally-homogenous water layer, is presented. This model is based upon the reactiondiffusion equation and takes into account the main factors of influence: mineral nutrients, insolation and temperature. The mineral nutrients and insolation act oppositely on spatial phytoplankton distribution. The results of numerical modeling are presented and the prospect of applying this model to reconstruction of phytoplankton distribution from sea-surface satellite data is discussed. The model was used to estimate the chlorophyll content of the Peter the Great Bay (Sea of Japan).

The article is devoted to description the author's method of multidimensional analysis for generate an predictive assessment of organizations’ credit risk, based on the credit history information, which taking into account value and period of credit. An example of credit risk assessment is given.

In the paper the model for a one-dimensional non-equilibrium riverbed process is proposed. The model takes into account the suspended and bed-load sediment transport. The bed-load transport is determined by using the original formula. This formula was derived from the thin bottom layer motion equation. The formula doesn’t contain new phenomenological parameters and takes into account the influence of bed slope, granulometric and physical mechanical parameters on the bed-load transport. A number of the model test problems are solved for the verification of the proposed mathematical model. The comparison of the calculation results with the established experimental data and the results of other authors is made. It was shown, that the obtained results have a good agreement with the experimental data in spite of the relative simplicity of the proposed mathematical model.

The BES-III experiment at the IHEP CAS, Beijing, is running at the high-luminosity e+e- collider BEPC-II to study physics of charm quarks and tau leptons. The world largest samples of J/psi and psi' events are already collected, a number of unique data samples in the energy range 2.5–4.6 GeV have been taken. The data volume is expected to increase by an order of magnitude in the coming years. This requires to move from a centralized computing system to a distributed computing environment, thus allowing the use of computing resources from remote sites — members of the BES-III Collaboration. In this report the general information, latest results and development plans of the BES-III distributed computing system are presented.

The article presents the results of computer simulations aimed to assess the influence of tree spatial locations (planting schemes) on the productivity and the dynamics of soil fertility in forest plantations. The growth of aspen (Populus tremula L.) in plantations with short rotation (30 years) was simulated in the EFIMOD system of models with the soil and climatic data matching forested lands in the Mari El Republic. The outcome reveals that higher biomass rates, increase in soil organic matter stocks, and the minimal loss of soil nitrogen can be obtained when the distance between trees in the row equals 1–4 m and 4–6 м in aisles.