The advent of interconnected machines laid the foundation for utilizing distributed computing resources. Coinciding with rapid advancements in computing technologies and significant hardware innovations at the turn of the century, the field of science also experienced exponential growth. As supercomputers remained limited in availability and usage, distributed computing leveraged the potential of idle and dedicated resources, bridging the gap between scientists and computationally intensive projects. This paper provides a review of the evolutionary journey of grid computing in scientific applications, starting from the advancements in network connection technologies and concept of metacomputing and progressing to the current developments integrating cloud technologies with large-scale grids. The paper aims to outline the key milestones, advancements, and challenges encountered throughout this evolution, highlighting the potential of grid computing in enabling scientific breakthroughs and addressing future research directions. The most popular middleware systems are considered, as well as a description of scientific grid systems that existed in the past and are still in operation today is given. At the end of the article, we examined two of the most significant scientific discoveries that became possible largely thanks to grid technologies.
Full-atomistic modeling of the deposition of TiO2, SiO2 and TiO2–SiO2 films is performed using parallel calculations. The dependence of film density on the deposition angle and deposition energy is studied. Simulation of post-deposition annealing of film structures is also carried out. Mechanical stresses in TiO2–SiO2 films, arising due to differences in the properties of silicon dioxide and titanium dioxide, are calculated. It is found that the film density decreases with decreasing deposition energy and increasing deposition angle. The use of surfacing annealing leads to an increase in film thickness. In two-layer TiO2–SiO2 films, the stresses are compressive. Particular attention is paid to reducing computational costs when simulating large atomistic clusters, consisting of hundreds of thousands of atoms. Reducing the parameter that determines the calculation of the electrostatic part of interatomic energy significantly reduces the simulation time. At the same time, in this case, the accuracy of determining the electrostatic energy in the reciprocal space decreases, which should be taken into account during modeling.
The article examines potential applications of WAVEWATCH III (WW3), the thirdgeneration wind-wave model. This study delves into the implementation of hybrid parallelization (MPI-OpenMP) and the development of multiple-cell grids tailored for the Azov Sea region. It elucidates fundamental equations of the model, their discretization, and software execution. The multiple-cell grid strategy employs high-resolution cells within the region of interest, gradually increasing cell density in other areas to optimize memory consumption. A 6-level multiple-cell grid was specifically crafted for the Azov Sea, with an algorithm outlined for its generation incorporating two refinement methods. This algorithm enables the creation of refined multiple-cell grids near shorelines at varying levels, along with the capability to refine grid structures in arbitrary zones. Additionally, the article presents hybrid parallelization techniques for the wave spectral component (MPI-OpenMP), assessing scalability in both MPI and hybrid deployments. The WW3 model offers a multigrid option facilitating parallel operation of subdomains akin to domain decomposition, while ensuring parallelization of each subnet via the component decomposition method.
Global models of the Earth's gravitational field, built from data collected by space geodetic missions, play a very important role in studying global processes across Earth's various geospheres. The paper is devoted to the development of a program for the recovery of the Earth's gravity field parameters. This program will enable in the future to process the results of measurements from the Russian satellite constellation and to build gravity field models of different spatial and temporal resolution. The recovery of the gravity field from satellite measurements is a rather resourceconsuming computational process, and parallel computations are crucial for its optimization. This paper describes the mathematical model, the algorithm and the results of parallelization, as well as presents the results of the gravity field recovery using parallel computations working with real measurement data. The static model of the Earth's gravitational field MSU2024-1 was built using the GRACE-FO mission data for the whole year 2021. The model is decomposed to degrees and orders of n = m = 120 and presented in terms of geoid heights. We also compared the EGF recovery on a monthly interval using the GRACE-FO data obtained in this work with the CSR, GFZ, and JPL temporal models built at other world centers.
Computer aided structural based approach was used to find inhibitors of SARS-CoV-2 nsp16 (2'-O-methyltransferase). Docking based virtual screening of three libraries, Enamine Coronavirus Library, Enamine Nucleoside Mimetics Library, and Chemdiv Nucleoside Analogue Library, was performed. In total, 39350 3D-structures of low molecular weight ligands were docked into a model of nsp16 prepared using the structure of 6WKQ complex from the Protein Data Bank. Docking was performed by the SOL docking program. For the best SOL scored ligands, the protein-ligand binding enthalpy was calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. The most promising eleven compounds were purchased and their inhibitory activity against the recombinant viral nsp16 protein was measured using MST assay with Monolith NT.115. As a result, two compounds, Z195979162 and Z1333277068, from Enamine Coronavirus Library demonstrated dissociation constants Kd for nsp16/nsp10 complex equal to 2.0 and 5.0 μM. The relative stability of these ligands in their docked positions in the nsp16 S-adenosylmethionine (SAM) binding site was confirmed in the molecular dynamics simulations along 70 ns trajectories. Z195979162 and Z1333277068 compounds belong to two chemical classes: 1,4-disubstituted tetrahydropyridines and derivatives of pyrazole-5-carboxamide, respectively, and can be good starting points for further hit optimization in the field of nsp16 inhibitors design.
