Результаты поиска по 'CUDA':
Найдено статей: 7
  1. Editor's note
    Computer Research and Modeling, 2021, v. 13, no. 1, pp. 5-8
  2. Kazennov A.M.
    Basic concepts of CUDA technology
    Computer Research and Modeling, 2010, v. 2, no. 3, pp. 295-308

    The history of the development of CUDA technology and its fundamental limitations are discribed. The article is intended for those readers who are not familiar with graphics adapter programming features but want to evaluate the possibilities for GPU computing applications.

    Views (last year): 5. Citations: 4 (RSCI).
  3. Geller O.V., Vasilev M.O., Kholodov Y.A.
    Building a high-performance computing system for simulation of gas dynamics
    Computer Research and Modeling, 2010, v. 2, no. 3, pp. 309-317

    The aim of research is to develop software system for solving gas dynamic problem in multiply connected integration domains of regular shape by high-performance computing system. Comparison of the various technologies of parallel computing has been done. The program complex is implemented using multithreaded parallel systems to organize both multi-core and massively parallel calculation. The comparison of numerical results with known model problems solutions has been done. Research of performance of different computing platforms has been done.

    Views (last year): 5. Citations: 6 (RSCI).
  4. Kopysov S.P., Kuzmin I.M., Nedozhogin N.S., Novikov A.K., Rychkov V.N., Sagdeeva Y.A., Tonkov L.E.
    Parallel implementation of a finite-element algorithms on a graphics accelerator in the software package FEStudio
    Computer Research and Modeling, 2014, v. 6, no. 1, pp. 79-97

    In this paper, we present new parallel algorithms for finite element analysis implemented in the FEStudio software framework. We describe the programming model of finite element method, which supports parallelism on different stages of numerical simulations. Using this model, we develop parallel algorithms of numerical integration for dynamic problems and local stiffness matrices. For constructing and solving the systems of equations, we use the CUDA programming platform.

    Views (last year): 4. Citations: 24 (RSCI).
  5. Kiryushkin A.E., Minkov L.L.
    Parallel implementation of numerical algorithm of solving coupled internal ballistics modelling problem for solid rocket motors
    Computer Research and Modeling, 2021, v. 13, no. 1, pp. 47-65

    We present a physico-mathematical statement of coupled geometrical and gas dynamics problem of intrachamber processes simulation and calculation of main internal ballistics characteristics of solid rocket motors in axisymmetric approximation. Method and numerical algorithm of solving the problem are described in this paper. We track the propellant burning surface using the level set method. This method allows us to implicitly represent the surface on a fixed Cartesian grid as zero-level of some function. Two-dimensional gas-dynamics equations describe a flow of combustion products in a solid rocket motor. Due to inconsistency of domain boundaries and nodes of computational grid, presence of ghost points lying outside the computational domain is taken into account. For setting the values of flow parameters in ghost points, we use the inverse Lax – Wendroff procedure. We discretize spatial derivatives of level set and gas-dynamics equations with standard WENO schemes of fifth and third-order respectively and time derivatives using total variation diminishing Runge –Kutta methods. We parallelize the presented numerical algorithm using CUDA technology and further optimize it with regard to peculiarities of graphics processors architecture.

    Created software package is used for calculating internal ballistics characteristics of nozzleless solid rocket motor during main firing phase. On the base of obtained numerical results, we discuss efficiency of parallelization using CUDA technology and applying considered optimizations. It has been shown that implemented parallelization technique leads to a significant acceleration in comparison with central processes. Distributions of key parameters of combustion products flow in different periods of time have been presented in this paper. We make a comparison of obtained results between quasione-dimensional approach and developed numerical technique.

  6. Alekseenko A.E., Kazennov A.M.
    CUDA and OpenCL implementations of Conway’s Game of Life cellular automata
    Computer Research and Modeling, 2010, v. 2, no. 3, pp. 323-326

    In this article the experience of reading “CUDA and OpenCL programming” course during high perfomance computing summer school MIPT-2010 is analyzed. Content of lectures and practical tasks, as well as manner of presenting of the material are regarded. Performance issues of different algorithms implemented by students at practical training session are dicussed.

    Views (last year): 9. Citations: 3 (RSCI).
  7. Bogdanov A.V., Gankevich I.G., Gayduchok V.Yu., Yuzhanin N.V.
    Running applications on a hybrid cluster
    Computer Research and Modeling, 2015, v. 7, no. 3, pp. 475-483

    A hybrid cluster implies the use of computational devices with radically different architectures. Usually, these are conventional CPU architecture (e.g. x86_64) and GPU architecture (e. g. NVIDIA CUDA). Creating and exploiting such a cluster requires some experience: in order to harness all computational power of the described system and get substantial speedup for computational tasks many factors should be taken into account. These factors consist of hardware characteristics (e.g. network infrastructure, a type of data storage, GPU architecture) as well as software stack (e.g. MPI implementation, GPGPU libraries). So, in order to run scientific applications GPU capabilities, software features, task size and other factors should be considered.

    This report discusses opportunities and problems of hybrid computations. Some statistics from tests programs and applications runs will be demonstrated. The main focus of interest is open source applications (e. g. OpenFOAM) that support GPGPU (with some parts rewritten to use GPGPU directly or by replacing libraries).

    There are several approaches to organize heterogeneous computations for different GPU architectures out of which CUDA library and OpenCL framework are compared. CUDA library is becoming quite typical for hybrid systems with NVIDIA cards, but OpenCL offers portability opportunities which can be a determinant factor when choosing framework for development. We also put emphasis on multi-GPU systems that are often used to build hybrid clusters. Calculations were performed on a hybrid cluster of SPbU computing center.

    Views (last year): 4.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"