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The paper presents the results of applying a scheme of very high accuracy and resolution to obtain numerical solutions of the Navier – Stokes equations of a compressible gas describing the occurrence and development of instability of a two-dimensional laminar boundary layer on a flat plate. The peculiarity of the conducted studies is the absence of commonly used artificial exciters of instability in the implementation of direct numerical modeling. The multioperator scheme used made it possible to observe the subtle effects of the birth of unstable modes and the complex nature of their development caused presumably by its small approximation errors. A brief description of the scheme design and its main properties is given. The formulation of the problem and the method of obtaining initial data are described, which makes it possible to observe the established non-stationary regime fairly quickly. A technique is given that allows detecting flow fluctuations with amplitudes many orders of magnitude smaller than its average values. A time-dependent picture of the appearance of packets of Tollmien – Schlichting waves with varying intensity in the vicinity of the leading edge of the plate and their downstream propagation is presented. The presented amplitude spectra with expanding peak values in the downstream regions indicate the excitation of new unstable modes other than those occurring in the vicinity of the leading edge. The analysis of the evolution of instability waves in time and space showed agreement with the main conclusions of the linear theory. The numerical solutions obtained seem to describe for the first time the complete scenario of the possible development of Tollmien – Schlichting instability, which often plays an essential role at the initial stage of the laminar-turbulent transition. They open up the possibilities of full-scale numerical modeling of this process, which is extremely important for practice, with a similar study of the spatial boundary layer.

Technological development determines the emergence of highly detailed data in time and space, which expands the possibilities of analysis, allowing us to consider consumer decisions and the competitive behavior of enterprises in all their diversity, taking into account the context of the territory and the characteristics of time periods. Despite the promise of such studies, they are currently limited in the scientific literature. This is due to the range of problems, the solution of which is considered in this paper. The article draws attention to the complexity of the analysis of depersonalized high-frequency data and the possibility of modeling consumption changes in time and space based on them. The features of the new type of data are considered on the example of real depersonalized data received from the fiscal data operator “First OFD” (JSC “Energy Systems and Communications”). It is shown that along with the spectrum of problems inherent in high-frequency data, there are disadvantages associated with the process of generating data on the side of the sellers, which requires a wider use of data mining tools. A series of statistical tests were carried out on the data under consideration, including a Unit-Root Test, test for unobserved individual effects, test for serial correlation and for cross-sectional dependence in panels, etc. The presence of spatial autocorrelation of the data was tested using modified tests of Lagrange multipliers. The tests carried out showed the presence of a consistent correlation and spatial dependence of the data, which determine the expediency of applying the methods of panel and spatial analysis in relation to high-frequency data accumulated by fiscal operators. The constructed models made it possible to substantiate the spatial relationship of sales growth and its dependence on the day of the week. The limitation for increasing the predictive ability of the constructed models and their subsequent complication, due to the inclusion of explanatory factors, was the lack of open access statistics grouped in the required detail in time and space, which determines the relevance of the formation of high-frequency geographically structured data bases.

High frequency algorithmic trading became is a subclass of trading which is focused on gaining basis-point like profitability on sub-second time frames. Such trading strategies do not depend on most of the factors eligible for the longer-term trading and require specific approach. There were many attempts to utilize machine learning techniques to both high and low frequency trading. However, it is still having limited application in the real world trading due to high exposure to overfitting, requirements for rapid adaptation to new market regimes and overall instability of the results. We conducted a comprehensive research on combination of known quantitative theory and reinforcement learning methods in order derive more effective and robust approach at construction of automated trading system in an attempt to create a support for a known algorithmic trading techniques. Using classical price behavior theories as well as modern application cases in sub-millisecond trading, we utilized the Reinforcement Learning models in order to improve quality of the algorithms. As a result, we derived a robust model which utilize Deep Reinforcement learning in order to optimise static market making trading algorithms’ parameters capable of online learning on live data. More specifically, we explored the system in the derivatives cryptocurrency market which mostly not dependent on external factors in short terms. Our research was implemented in high-frequency environment and the final models showed capability to operate within accepted high-frequency trading time-frames. We compared various combinations of Deep Reinforcement Learning approaches and the classic algorithms and evaluated robustness and effectiveness of improvements for each combination.

Variational inequalities constitute a broad class of problems with applications in a number of fields, including game theory, economics, and machine learning. Today’s practical applications of VIs are becoming increasingly computationally demanding. It is therefore necessary to employ distributed computations to solve such problems in a reasonable time. In this context, workers have to exchange data with each other, which creates a communication bottleneck. There are three main techniques to reduce the cost and the number of communications: the similarity of local operators, the compression of messages and the use of local steps on devices. There is an algorithm that uses all of these techniques to solve the VI problem and outperforms all previous methods in terms of communication complexity. However, this algorithm is limited to unbiased compression. Meanwhile, biased (contractive) compression leads to better results in practice, but it requires additional modifications within an algorithm and more effort to prove the convergence. In this work, we develop a new algorithm that solves distributed VI problems using data similarity, contractive compression and local steps on devices, derive the theoretical convergence of such an algorithm, and perform some experiments to show the applicability of the method.

The problem of centralized planning for data transmission routes in delay tolerant networks is considered. The original problem is extended with additional requirements to nodes storage and communication process. First, it is assumed that the connection between the nodes of the graph is established using antennas. Second, it is assumed that each node has a storage of finite capacity. The existing works do not consider these requirements. It is assumed that we have in advance information about messages to be processed, information about the network configuration at specified time points taken with a certain time periods, information on time delays for the orientation of the antennas for data transmission and restrictions on the amount of data storage on each satellite of the grouping. Two wellknown algorithms — CGR and Earliest Delivery with All Queues are improved to satisfy the extended requirements. The obtained algorithms solve the optimal message routing problem separately for each message. The problem of validation of the algorithms under conditions of lack of test data is considered as well. Possible approaches to the validation based on qualitative conjectures are proposed and tested, and experiment results are described. A performance comparison of the two implementations of the problem solving algorithms is made. Two algorithms named RDTNAS-CG and RDTNAS-AQ have been developed based on the CGR and Earliest Delivery with All Queues algorithms, respectively. The original algorithms have been significantly expanded and an augmented implementation has been developed. Validation experiments were carried to check the minimum «quality» requirements for the correctness of the algorithms. Comparative analysis of the performance of the two algorithms showed that the RDTNAS-AQ algorithm is several orders of magnitude faster than RDTNAS-CG.

We'd like to present a specific grid infrastructure and web application development and deployment. The purpose of infrastructure and web application is to solve particular geophysical problems that require heavy computational resources. Here we cover technology overview and connector framework internals. The connector framework links problem-specific routines with middleware in a manner that developer of application doesn't have to be aware of any particular grid software. That is, the web application built with this framework acts as an interface between the user 's web browser and Grid's (often very) own middleware.

Our distributed computing system is built around Gridway metascheduler. The metascheduler is connected to TORQUE resource managers of virtual compute nodes that are being run atop of compute cluster utilizing the virtualization technology. Such approach offers several notable features that are unavailable to bare-metal compute clusters.

The first application we've integrated with our framework is seismic anisotropic parameters determination by inversion of SKS and converted phases. We've used probabilistic approach to inverse problem solution based on a posteriory probability distribution function (APDF) formalism. To get the exact solution of the problem we have to compute the values of multidimensional function. Within our implementation we used brute-force APDF calculation on rectangular grid across parameter space.

The result of computation is stored in relational DBMS and then represented in familiar human-readable form. Application provides several instruments to allow analysis of function's shape by computational results: maximum value distribution, 2D cross-sections of APDF, 2D marginals and a few other tools. During the tests we've run the application against both synthetic and observed data.

One of the most widely used storage solutions in WLCG is a Disk Pool Manager (DPM) developed and supported by SDC/ID group at CERN. Recently DPM went through a massive overhaul to address scalability and extensibility issues of the old code.

New system was called DMLite. Unlike the old DPM that was based on daemons, DMLite is arranged as a library that can be loaded directly by an application. This approach greatly improves performance and transaction rate by avoiding unnecessary inter-process communication via network as well as threading bottlenecks.

DMLite has a modular architecture with its core library providing only the very basic functionality. Backends (storage engines) and frontends (data access protocols) are implemented as plug-in modules. Doubtlessly DMLite wouldn't be able to completely replace DPM without GridFTP as it is used for most of the data transfers in WLCG.

In DPM GridFTP support was implemented in a Data Storage Interface (DSI) module for Globus’ GridFTP server. In DMLite an effort was made to rewrite a GridFTP module from scratch in order to take advantage of new DMLite features and also implement new functionality. The most important improvement over the old version is a redirection capability.

With old GridFTP frontend a client needed to contact SRM on the head node in order to obtain a transfer URL (TURL) before reading or writing a file. With new GridFTP frontend this is no longer necessary: a client may connect directly to the GridFTP server on the head node and perform file I/O using only logical file names (LFNs). Data channel is then automatically redirected to a proper disk node.

This renders the most often used part of SRM unnecessary, simplifies file access and improves performance. It also makes DMLite a more appealing choice for non-LHC VOs that were never much interested in SRM.

With new GridFTP frontend it's also possible to access data on various DMLite-supported backends like HDFS, S3 and legacy DPM.

The approach to build territorial distributed storage system for high performance computing environment of UB RAS is presented. The storage system is based on the dCache middleware from the European Middleware Initiative project. The first milestone of distributed storage system implementation includes the data centers at the two UB RAS Regions: Yekaterinburg and Perm.

This work is devoted to molecular dynamic modeling of the thermal impact processes on the metal sample consisting of nickel atoms. For the solution of this problem, a continuous mathematical model on the basis of the classical Newton mechanics equations has been used; a numerical method based on the Verlet scheme has been chosen; a parallel algorithm has been offered, and its realization within the MPI and OpenMP technologies has been executed. By means of the developed parallel program, the investigation of thermodynamic equilibrium of nickel atoms’ system under the conditions of heating a sample to desired temperature has been executed. In numerical experiments both optimum parameters of calculation procedure and physical parameters of analyzed process have been defined. The obtained numerical results are well corresponding to known theoretical and experimental data.

Big science projects are producing data at ever increases rates. Typical techniques involve storing the data to disk, after minor filtering, and then processing it in large computer farms. Data production has reached a point where on-line processing is required in order to filter the data down to manageable sizes. A potential solution involves using low-cost, low-power ARM processors in large arrays to provide massive parallelisation for data stream computing (DSC). The main advantage in using System on Chips (SoCs) is inherent in its design philosophy. SoCs are primarily used in mobile devices and hence consume less power while maintaining relatively good performance. A benchmarking characterisation of three different models of ARM processors will be presented.