The article presents the results of several studies to assess the impact of the configuration of NUMA (Non-Uniform Memory Access) nodes on the performance of GPU-accelerated applications in hybrid computing system with shared memory. Using Crossroads/N9 DGEMM (NVBLAS library) as a model application, the performance in various NUMA modes with one or more GPUs was analyzed, and the throughput of the memory subsystem and data transfer channels between the host memory and graphics processors was also measured. The impact of coprocessor distribution across NUMA nodes on the efficiency of the model application was also examined.Results showed that configuration of NUMA nodes can have a significant impact on the performance of applications that offload calculations to graphics coprocessors in a hybrid computing system with shared memory, and this impact could have an effect in different ways. For example, using one NUMA node for the entire computing system is the least optimal approach in terms of memory bandwidth, but it provided the highest bandwidth for communication between host memory and coprocessors during active data transfer to several accelerators. Thus, this mode achieves maximum performance when performing calculations on multiple GPUs that actively exchange data through host memory. Other modes showed advantages in different situations. Overall, to achieve maximum performance during active data transfer to coprocessors, they should be part of one NUMA node. These results will help to develop approaches to configuration of hybrid computing systems on processors with a chiplet layout, and help to improve the performance of software that offloads calculations to graphics accelerators with the Ampere architecture, such as NVIDIA A800 and NVIDIA A100, which are currently widely represented in the high-performance computing industry.

The paper presents an algorithm for precipitation estimation based on data from the Himawari-8/9 satellite. The algorithm is based on two neural networks of visual transformer and convolutional architectures for preliminary precipitation mask calculation and rain rate estimation. Data from the "Global Precipitation Measurements" (GPM) international project were used as a reference value of precipitation. These data are based on measurements from various active and passive microwave and infrared satellite instruments. The algorithm takes into account spectral, textural, and microphysical parameters of clouds. An accuracy assessment was carried out using GPM data and ground-based rain gauges. The results of a comparison between the algorithm and the ComsoRu-6 regional numerical weather prediction model are also given. It is shown that the presented algorithm most accurately estimates the amount of accumulated precipitation sums but it has a tendency to overestimate this value. On the other hand, GPM and CosmoRu-6 often underestimate precipitation. The comparison with the product of GPM showed a root-mean-squared error of about 2.19 mm/h.

4 Дальневосточный центр НИЦ «Планета»

The powerful explosive eruptions with large volumes of volcanic ash pose a great danger to the population and jet aircraft. Global experience in monitoring volcanoes and observing changes in the parameters of their thermal anomalies is successfully used to analyze the activity of volcanoes and predict their danger to the population. The Kamchatka Peninsula in Russia, with its 30 active volcanoes, is one of the most volcanically active regions in the world. The article considers the thermal activity in 2015–2022 of the Klyuchevskoy, Sheveluch, Bezymianny, and Karymsky volcanoes, whose rock composition varies from basaltic andesite to dacite. This study is based on the analysis of the Value of Temperature Difference between the thermal Anomaly and the Background (the VTDAB), obtained by manual processing of the AVHRR, MODIS, VIIRS, and MSU-MR satellite data in the VolSatView information system. Based on the VTDAB data, the following “background activity of the volcanoes” was determined: 20 °C for Sheveluch and Bezymianny, 12 °C for Klyuchevskoy, and 13–15 °C for Karymsky. This study showed that the highest temperature of the thermal anomaly corresponds to the juvenile magmatic material that arrived on the earth’s surface. The highest VTDAB is different for each volcano; it depends on the composition of the eruptive products produced by the volcano and on the character of an eruption. A joint analysis of the dynamics of the eruption of each volcano and changes in its thermal activity made it possible to determine the range of the VTDAB for different phases of a volcanic eruption.

One of the most important tasks when studying volcanic activity is to monitor their thermal radiation. To fix and assess the evolution of thermal anomalies in areas of volcanoes, specialized hardware-thermal imagers are usually used, as well as specialized instruments of modern satellite systems. The data obtained with their help contain information that makes it relatively easy to track changes in temperature and the size of a thermal anomaly. At the same time, due to the high cost of such complexes and other limitations, thermal imagers sometimes cannot be used to solve scientific problems related to the study of volcanoes. In the current paper, day/night video cameras with an infrared-cut filter are considered as an alternative to specialized tools for monitoring volcanoes’ thermal activity. In the daytime, a camera operated in the visible range, and at night the filter was removed, increasing the camera’s light sensitivity by allowing near-infrared light to hit the sensor. In that mode, a visible thermal anomaly could be registered on images, as well as other bright glows, flares, and other artifacts. The purpose of this study is to detect thermal anomalies on night images, separate them from other bright areas, and find their characteristics, which could be used for volcano activity monitoring. Using the image archive of the Sheveluch volcano as an example, this article presents the results of developing a computer algorithm that makes it possible to find and classify thermal anomalies on video frames with an accuracy of 98%. The test results are presented, along with their validation based on thermal activity data obtained from satellite systems.

This paper reconstructs, for the first time, the motion dynamics of an eruptive cloud formed during the catastrophic eruption of the Sheveluch volcano in November 1964 (Volcanic Explosivity Index 4+). This became possible due to the public availability of atmospheric reanalysis data from the ERA-40 archive of the European Center for Medium-Range Weather Forecasts (ECMWF) and the development of numerical modeling of volcanic ash cloud propagation. The simulation of the eruptive cloud motion process, which was carried out using the FALL3D and PUFF models, made it possible to clarify the sequence of events of this eruption (destruction of extrusive domes in the crater and the formation of an eruptive column and pyroclastic flows), which lasted only 1 h 12 min. During the eruption, the ash cloud consisted of two parts: the main eruptive cloud that rose up to 15,000 m above sea level (a.s.l.), and the co-ignimbrite cloud that formed above the moving pyroclastic flows. The ashfall in Ust-Kamchatsk (Kamchatka) first occurred out of the eruptive cloud moving at a higher speed, then out of the co-ignimbrite cloud. In Nikolskoye (Bering Island, Commander Islands), ash fell only out of the co-ignimbrite cloud. Under the turbulent diffusion, the forefront of the main eruptive cloud rose slowly in the atmosphere and reached 16,500 m a.s.l. by 04:07 UTC on November 12. Three days after the eruption began, the eruptive cloud stretched for 3000 km over the territories of the countries of Russia, Canada, the USA, Mexico, and over both the Bering Sea and the Pacific Ocean. It is assumed that the well-known long-term decrease in the solar radiation intensity in the northern latitudes from 1963–1966, which was established according to the world remote sensing data, was associated with the spread of aerosol clouds formed not only by the Agung volcano, but those formed during the 1964 Sheveluch volcano catastrophic eruption.

О. А. Гирина , А. Г. Маневич , Д. В. Мельников , А. А. Нуждаев , А. В. Кашницкий , И. А. Уваров , И. М. Романова , А. А. Сорокин , С. И. Мальковский , С. П. Королев , Л. С. Крамарева 4 1 Институт вулканологии и сейсмологии ДВО РАН Петропавловск-Камчатский, 683006, Россия E-mail: girina@kscnet.ru 2 Институт космических исследований РАН, Москва, 117997, Россия 3 Вычислительный центр ДВО РАН, Хабаровск, 680000, Россия 4 Дальневосточный центр НИЦ «Планета», Хабаровск, 680000, Россия

The traditional labor contract has actually ceased to be the principal legal document, regulating labor relations of Russian university teachers. The individual labor contract was reduced to the status of a mere ‘rudimentary’ annex to the so-called effective contract. The latter is legally non-existent and is not even mentioned in the Labor Code of Russia of 2001. The Covid-19 pandemic with its isolationist features aggravated the absurd paradigm change within the Russian Labor law. As a result, the illegitimate effective contract has virtually supplanted the regular labor contract. There may be traced three dominantfeatures of the new labor regime, induced by the Covid-19 pandemic. Firstly, the said labor regime fosters social dissociation of former (ante-pandemic) colleagues with the inevitable harm to the social nature and human dignity of homo faber. Secondly, wecan witness the strengthening of the external -via internet -exploitation of university teachers by a corresponding managerial staff and the merging of this exploitation with the academic staff’s self-exploitation. Thirdly, the said regime is responsible for virtual disappearance of difference between working days of university teachers and leisure hours, previously reserved for reading and research.

Вулкан Карымский -один из наиболее активных вулканов Камчатки.В последние два года были отмечены единичные мощные эксплозии с выносом пепла до 8-10 км н. у. м.Эксплозивное событие 19 апреля с подъёмом пеплового облака до 10 км н. у. м. произошло на фоне непрерывной эмиссии пепла из вулкана.В связи с высокой циклонической активностью в районе Камчатки пепловое облако 19-21 апреля было растянуто в полосу длиной 1000 км с юго-востока на северо-восток.Северная часть облака была затянута другим циклоном в Арктику.Площадь пеплового облака составляла более 246 тыс.км 2 .Кроме эруптивного, в начале извержения хорошо проявилось крупное облако диоксида серы.Слабонасыщенное диоксидом серы облако было отмечено над Арктикой 21

The work is devoted to the activity analysis of Kamchatka and the Kuril Islands volcanoes in 2019-2020.The activity of the volcanoes was estimated based on the processing of data from daily satellite monitoring carried out using the information system “Remote monitoring of Kamchatkan and the Kuriles volcanoes activity (VolSatView)”.The activity of the Kamchatka and the Kuril Islands volcanoes considered based on the analysis of their thermal anomalies. Analysis of the characteristics of thermal anomalies over volcanoes was carried out in KVERT IS. Analysis of the temperature of thermal anomalies of volcanoes in the Kuril-Kamchatka region in 2019-2020 shows a significantly higher activity of the Kamchatka volcanoes in comparison with the Kuril volcanoes.

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame.

The article discusses the analysis of images obtained from video cameras shooting in a wide range, including visible and near-infrared wavelengths. The problem of identification of thermal anomalies in the images of volcanoes obtained with such cameras is solved. The introduction contains a brief review of methods and approaches to the solution of the problem. Section 1 is devoted to the description of the developed algorithm of thermal anomalies detection. At the beginning of the section, the procedure of data sampling preparation is described and the details of thermal anomalies display on the images are shown. Then a description of all the stages of the algorithm is given, including finding the centers of potential anomalies, calculating the area and determining the features of the anomalies with further classification of the obtained data into classes of "thermal" anomaly and "non-thermal" anomaly. In Section 2, the description of the computer system for automated image analysis is given. At the beginning, the implementation of the developed algorithm in the form of a console application is shown. Then its integration into the computer system for the automated analysis of images of the system of continuous video observation of volcanoes of Kamchatka is considered. Also is given a description of all capabilities of the obtained system, a scheme of its work and examples of developed interfaces. In the conclusion of the article summarized the results of the work and the prospects for further research.

In this work, using the classical technique for determining of the integral water vapor content in the Earth's troposphere (Integrated Water Vapor -IWV) we studied the IWV variations in the continentocean transition zone from GNSS observations at two points located in the continental and coastal parts of Primorsky Krai (Far East of Russia).Using the measurements at the nearest stations of the global GNSS-network IGS and radiosonde data the high accuracy and reliability of the estimates of atmospheric moisture content have been confirmed.At the measurement points, IWV variations for the period from 2015 to 2019 were studied, empirical approximation models of annual variations in IWV were constructed, the obtained estimates were compared with the data of the global model GFS and Reanalysis ERA5.The diurnal changes in the concentration of water vapor in the atmosphere, as well as its change during the passage of typhoons, accompanied by massive precipitation, were studied.It was found that more than 60 % of massive precipitation (>20 mm) falls within 3-9 hours at the IWV decline after a sharp increase in the integral moisture content recorded by GNSS methods.The high accuracy and frequency of IVW determination (up to 1 Hz), together with the high efficiency of obtaining information about the IWV change from GNSS observations, open up broad prospects for the application of GNSS meteorology in the forecasting practice of hydrometeorological services in the Russian Federation.

Fast Fourier transform is widely used to solve numerous scientific and engineering problems. In particular, this transform is behind the software dealing with speech and image recognition, signal analysis, modeling of properties of new materials and substances, etc. Newly emerging high-performance hybrid computing systems, as well as systems with alternative architectures, require research on discrete Fourier transform computation efficiency on these new platforms. The results of such research allow assessing the feasibility of certain solutions for building modern computing and data processing centers. This paper presents the results of such research covering modern hybrid computing systems based on the IBM POWER and Intel Xeon processors, as well as on NVIDIA Tesla co-processors. The analysis is carried out, and conclusions are presented on their performance when executing fast Fourier transforms. The impact of the existing architectural aspects of the hardware (CPU simultaneous multithreading mode, GPU data transfer bus, etc.) on the transform performance efficiency is assessed. The obtained results are used to provide recommendations on the optimal operation modes and settings of the considered mathematical libraries.

Остров-вулкан Чиринкотан находится в тыловой зоне Северных Курильских островов

4 Дальневосточный центр «НИЦ «Планета»

A key problem of any video volcano surveillance network is an inconsistent quality and information value of the images obtained. To timely analyze the incoming data, they should be pre-filtered. Additionally, due to the continuous network operation and low shooting intervals, an operative visual analysis of the shots stream is quite difficult and requires the application of various computer algorithms. The article considers the parametric algorithms of image analysis developed by the authors for processing the shots of the volcanoes of Kamchatka. They allow automatically filtering the image flow generated by the surveillance network, highlighting those significant shots that will be further analyzed by volcanologists. A retrospective processing of the full image archive with the methods suggested helps to get a data set, labeled with different classes, for future neural network training.

The problem of revealing the appearance and development of thermal anomalies in the images of volcanoes taken at night in the visible and near-infrared ranges is discussed. An algorithm for detecting and classifying such anomalies is proposed and is tested on the data from the archive of the video monitoring of volcanoes on Kamchatka. The results obtained suggest the possible use of the developed solution in the tasks of real-time monitoring of volcanic activity in the Russian Far East.