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Wykorzystanie CPU i GPU do obliczeń w Matlabie

100%

Woźniak J.

tom Vol. 10

32--35

W artykule zostały przedstawione wybrane rozwiązania wykorzystujące procesory CPU oraz procesory graficzne GPU do obliczeń w środowisku Matlab. Porównywano różne metody wykonywania obliczeń na CPU, jak i na GPU. Zostały wskazane różnice, wady, zalety oraz skutki stosowania wybranych sposobów obliczeń.

The article presents selected solutions using CPU processors and GPUs for calculations in the Matlab environment. Various methods of performing calculations on the CPU as well as on the GPU were compared. Differences, disadvantages, advantages and effects of using selected calculation methods have been indicated.

Accelerating computation of a reduced order model of a structural system resulting from Craig–Bampton reduction using GPU programming

84%

Górecki P. , Kalinowski M. , Jeziorek Ł. , Broniszewski J. , Koziara T.

tom Vol. 31, no. 1

51--66

The Craig–Bampton (CB) method is a well-known substructuring technique that reduces the size of a finite element model (FEM) using a set of vibration modes. For large FEA models, the reduction process could be computationally expensive since it requires algebra operations on FEM mode shapes and FEM system sparse matrices. In this paper, we investigate the potential of usage of GPU parallel processing to speed up solving the system of linear equations that results from the CB reduction process made for a model of cyclic structures. A Python based high-level approach, employing the CuPy, GinkGo and STRUMPACK libraries on the GPU, is compared with an optimized Fortran code. In side-to-side comparisons, employing the same inputs, the Python-GPU code is run on a single GPU device and the Fortran code is run on a multi-core compute node. The CB reduction process was split into several parts, each dealing with different kind of algebraic formulation of the problem. Performance comparisons were focused on the sparse system linear solver, since it turned out to be the most time-consuming part. The results suggest that the current GPU-based linear sparse solvers do not surpass the state-of-the-art CPU-based MKL PARDISO solver (at least up to 1M DOFs).

Porting of finite element integration algorithm to Xeon Phi coprocessor-based HPC architectures

59%

Krużel F. , Banaś K. , Iacomo M.

tom Vol. 30, no. 4

427--459

In the present article, we describe the implementation of the finite element numerical integration algorithm for the Xeon Phi coprocessor. The coprocessor was an extension of the many-core specialized unit for calculations, and its performance was comparable with the corresponding GPUs. Its main advantages were the built-in 512-bit vector registers and the ease of transferring existing codes from traditional x86 architectures. In the article, we move the code developed for a standard CPU to the coprocessor. We compareits performance with our OpenCL implementation of the numerical integration algorithm, previously developed for GPUs. The GPU code is tuned to fit into a coprocessor by ourauto-tuning mechanism. Tests included two types of tasks to solve, using two types of approximation and two types of elements. The obtained timing results allow comparing the performance of highly optimized CPU and GPU codes with a Xeon Phi coprocessor performance. This article answers whether such massively parallel architectures perform better using the CPU or GPU programming method. Furthermore, we have compared the Xeon Phi architecture and the latest available Intel’s i9 13900K CPU when writing this article. This comparison determines if the old Xeon Phi architecture remains competitive in today’s computing landscape. Our findings provide valuable insights for selectingthe most suitable hardware for numerical computations and the appropriate algorithmic design.

Możliwości i perspektywy współczesnej grafiki komputerowej

59%

Szuba T.

tom T. 7, z. 6

97-103

The paper deals with the problem: what is modern computer graphics now and what is its potential. If we think in terms of .the centre of gravity., modern computer graphics is moving from the art towards capturing the essence of an object or a being to be modeled. In other words, key problems for the computer graphics are physical phenomena (e.g. liquids), mechanical properties (e.g. textile, hairs) or even mental properties of virtual beings. Therefore, modern computer graphics requires extremely high computational abilities. Advanced computer games demonstrate this very well. Having all this in mind, many researchers think that modern computer graphics is the main leading force in the development of modern computer science. On the basis of above remarks, the paper tries to resume the application areas of modern computer graphics now and in the near future.