Specialized Web Portal for Solving Problems on Multiprocessor Computing Systems

A system for remote calculations called “Specialized web portal for solving problems on multiprocessor computing systems” has been developed and installed at the Department of Ill-Posed Problems of Analysis and Applications of the Institute of Mathematics and Mechanics UrB RAS. The parallel algorithms have been incorporated into this system to solve the inverse gravity problem of lateral density reconstruction, the structural inverse gravity and magnetic problem of the contact surfaces reconstruction, and solving SLAEs with block-tridiagonal matrices in geoelectrics problems

Algorithms for solving inverse geophysical problems on parallel computing systems

For solving inverse gravimetry problems, efficient stable parallel algorithms based on iterative gradient methods are proposed. For solving systems of linear algebraic equations with block-tridiagonal matrices arising in geoelectrics problems, a parallel matrix sweep algorithm, a square root method, and a conjugate gradient method with preconditioner are proposed. The algorithms are implemented numerically on a parallel computing system of the Institute of Mathematics and Mechanics (PCS-IMM), NVIDIA graphics processors, and an Intel multi-core CPU with some new computing technologies. The parallel algorithms are incorporated into a system of remote computations entitled "Specialized Web-Portal for Solving Geophysical Problems on Multiprocessor Computers." Some problems with "quasi-model" and real data are solved. © 2013 Pleiades Publishing, Ltd

Memory efficient algorithm for solving the inverse gravimetry problem of finding several boundary surfaces in multilayered medium

For solving the inverse gravimetry problem of finding several boundary surfaces in a multilayered medium, the parallel algorithm was constructed and implemented for multicore CPU using OpenMP technology. The algorithm is based on the modified nonlinear conjugate gradient method with weighting factors previously proposed by authors. To reduce the memory requirements and computation time, the modification was constructed on the basis of utilizing the Toeplitz-block-Toeplitz structure of the Jacobian matrix of the integral operator. The model problem of reconstructing three surfaces using the quasi-real gravitational data was solved on a large grid. It was shown that the proposed implementation reduces the computation time by 80% in comparison with the earlier algorithm based on calculating the entire matrix. The parallel algorithm shows good scaling of 94% on 8-core processor. © 2019 Author(s).Ministry of Education and Science of the Republic of Kazakhstan: AP 05133873This work was financially supported by the Ministry of Education and Science of the Republic of Kazakhstan (project AP 05133873)

Hydrodynamical Simulation of Astrophysical Flows: High-Performance GPU Implementation

We present a new hydrodynamical code GPUPEGAS 2.0 for 3D simulation of astrophysical flows using the GPUs. This code is an extension of GPUPEGAS code developed in 2014 for simulation of interacting galaxies. GPUPEGAS 2.0 is based on the Authors' numerical method of high order of accuracy for smooth solutions with small dissipation of the solution in discontinuities. The high order of accuracy and small dissipation are achieved by using the piecewise-linear representation of the physical variables in each dimension. The Rusanov flux allows one to simply vectorize the solution of the Riemann problem. The code was implemented for the cluster supercomputers NKS-30T (Siberian Supercomputer Center, SB RAS) and Uran (Institute of Mathematics and Mechanics, UrB RAS) using the hybrid MPI+CUDA technology. To avoid the compute capability-specific implementations of reduction routines, the Thrust library was used. The optimal parameters for kernel function were found for the three-dimensional computation grid. The Sedov point blast problem was used as a main test one. The numerical experiment was performed to simulate the hydrodynamics of the type II supernova explosion for the grid size of 2563. A set of experiments was performed to study performance and scalability of the developed code. The performance of 25 GFLOPS was achieved using a single Tesla M2090 GPU. The speedup of 3 times was achieved using a node with 4 GPUs. By using 16 GPUs, 70% scalability was achieved. © 2019 IOP Publishing Ltd. All rights reserved.Russian Science Foundation, RSF: 18-11-00044The work of Igor Kulikov and Igor Chernykh was supported by Russian Science Foundation (project no. 18-11-00044)

Review of deep learning approaches in solving rock fragmentation problems

One of the most significant challenges of the mining industry is resource yield estimation from visual data. An example would be identification of the rock chunk distribution parameters in an open pit. Solution of this task allows one to estimate blasting quality and other parameters of open-pit mining. This task is of the utmost importance, as it is critical to achieving optimal operational efficiency, reducing costs and maximizing profits in the mining industry. The mentioned task is known as rock fragmentation estimation and is typically tackled using computer vision techniques like instance segmentation or semantic segmentation. These problems are often solved using deep learning convolutional neural networks. One of the key requirements for an industrial application is often the need for real-time operation. Fast computation and accurate results are required for practical tasks. Thus, the efficient utilization of computing power to process high-resolution images and large datasets is essential. Our survey is focused on the recent advancements in rock fragmentation, blast quality estimation, particle size distribution estimation and other related tasks. We consider most of the recent results in this field applied to open-pit, conveyor belts and other types of work conditions. Most of the reviewed papers cover the period of 2018-2023. However, the most significant of the older publications are also considered. A review of publications reveals their specificity, promising trends and best practices in this field. To place the rock fragmentation problems in a broader context and propose future research topics, we also discuss state-of-the-art achievements in real-time computer vision and parallel implementations of neural networks

Parallel Direct and Iterative Methods for Solving the Time-Fractional Diffusion Equation on Multicore Processors

The work is devoted to developing the parallel algorithms for solving the initial boundary problem for the time-fractional diffusion equation. After applying the finite-difference scheme to approximate the basis equation, the problem is reduced to solving a system of linear algebraic equations for each subsequent time level. The developed parallel algorithms are based on the Thomas algorithm, parallel sweep algorithm, and accelerated over-relaxation method for solving this system. Stability of the approximation scheme is established. The parallel implementations are developed for the multicore CPU using the OpenMP technology. The numerical experiments are performed to compare these methods and to study the performance of parallel implementations. The parallel sweep method shows the lowest computing time. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: The first author (M.A.S.) and fourth author (E.N.) were financially supported by the Ministry of Education and Science of the Republic of Kazakhstan (project AP09258836). The second author (E.N.A.) and third author (V.E.M.) received no external funding

OMPEGAS: Optimized Relativistic Code for Multicore Architecture

The paper presents a new hydrodynamical code, OMPEGAS, for the 3D simulation of astrophysical flows on shared memory architectures. It provides a numerical method for solving the three-dimensional equations of the gravitational hydrodynamics based on Godunov’s method for solving the Riemann problem and the piecewise parabolic approximation with a local stencil. It obtains a high order of accuracy and low dissipation of the solution. The code is implemented for multicore processors with vector instructions using the OpenMP technology, Intel SDLT library, and compiler auto-vectorization tools. The model problem of simulating a star explosion was used to study the developed code. The experiments show that the presented code reproduces the behavior of the explosion correctly. Experiments for the model problem with a grid size of (Formula presented.) were performed on an 16-core Intel Core i9-12900K CPU to study the efficiency and performance of the developed code. By using the autovectorization, we achieved a 3.3-fold increase in speed in comparison with the non-vectorized program on the processor with AVX2 support. By using multithreading with OpenMP, we achieved an increase in speed of 2.6 times on a 16-core processor in comparison with the vectorized single-threaded program. The total increase in speed was up to ninefold. © 2022 by the authors.Russian Science Foundation, RSF: 18-11-00044The work of the third author (I.M.K.) and fourth author (I.G.C.) was supported by the Russian Science Foundation (project no. 18-11-00044). The first author (E.N.A.) and second author (V.E.M.) received no external funding

A survey on software defect prediction using deep learning

Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field. © 2021 by the authors. Licensee MDPI, Basel, Switzerland